C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

multinomial_naive_bayes_csr_distributed_mpi.cpp

/* file: multinomial_naive_bayes_csr_distributed_mpi.cpp */
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/*
! Content:
! C++ sample of Naive Bayes classification in the distributed processing
! mode.
!
! The program trains the Naive Bayes model on a supplied training data set
! and then performs classification of previously unseen data.
!******************************************************************************/
#include <mpi.h>
#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/distributed/naivebayes_train_csr.csv", "./data/distributed/naivebayes_train_csr.csv",
"./data/distributed/naivebayes_train_csr.csv", "./data/distributed/naivebayes_train_csr.csv"
};
const string trainGroundTruthFileNames[4] =
{
"./data/distributed/naivebayes_train_labels.csv", "./data/distributed/naivebayes_train_labels.csv",
"./data/distributed/naivebayes_train_labels.csv", "./data/distributed/naivebayes_train_labels.csv"
};
string testDatasetFileName = "./data/distributed/naivebayes_test_csr.csv";
string testGroundTruthFileName = "./data/distributed/naivebayes_test_labels.csv";
const size_t nClasses = 20;
const size_t nBlocks = 4;
int rankId, comm_size;
#define mpi_root 0
void trainModel();
void testModel();
void printResults();
training::ResultPtr trainingResult;
classifier::prediction::ResultPtr predictionResult;
int main(int argc, char *argv[])
{
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &comm_size);
MPI_Comm_rank(MPI_COMM_WORLD, &rankId);
trainModel();
if(rankId == mpi_root)
{
testModel();
printResults();
}
MPI_Finalize();
return 0;
}
void trainModel()
{
/* Retrieve the input data from a .csv file */
CSRNumericTable *trainDataTable = createSparseTable<float>(trainDatasetFileNames[rankId]);
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> trainLabelsSource(trainGroundTruthFileNames[rankId], DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Retrieve the data from input files */
trainLabelsSource.loadDataBlock();
/* Create an algorithm object to train the Naive Bayes model based on the local-node data */
training::Distributed<step1Local, algorithmFPType, training::fastCSR> localAlgorithm(nClasses);
/* Pass a training data set and dependent values to the algorithm */
localAlgorithm.input.set(classifier::training::data, CSRNumericTablePtr(trainDataTable));
localAlgorithm.input.set(classifier::training::labels, trainLabelsSource.getNumericTable());
/* Train the Naive Bayes model on local nodes */
localAlgorithm.compute();
/* Serialize partial results required by step 2 */
services::SharedPtr<byte> serializedData;
InputDataArchive dataArch;
localAlgorithm.getPartialResult()->serialize(dataArch);
size_t perNodeArchLength = dataArch.getSizeOfArchive();
/* Serialized data is of equal size on each node if each node called compute() equal number of times */
if (rankId == mpi_root)
{
serializedData.reset(new byte[perNodeArchLength * nBlocks]);
}
{
services::SharedPtr<byte> nodeResults(new byte[perNodeArchLength]);
dataArch.copyArchiveToArray(nodeResults.get(), perNodeArchLength );
/* Transfer partial results to step 2 on the root node */
MPI_Gather(nodeResults.get(), perNodeArchLength, MPI_CHAR, serializedData.get(), perNodeArchLength, MPI_CHAR, mpi_root,
MPI_COMM_WORLD);
}
if(rankId == mpi_root)
{
/* Create an algorithm object to build the final Naive Bayes model on the master node */
training::Distributed<step2Master, algorithmFPType, training::fastCSR> masterAlgorithm(nClasses);
for(size_t i = 0; i < nBlocks ; i++)
{
/* Deserialize partial results from step 1 */
OutputDataArchive dataArch(serializedData.get() + perNodeArchLength * i, perNodeArchLength);
training::PartialResultPtr dataForStep2FromStep1(new training::PartialResult());
dataForStep2FromStep1->deserialize(dataArch);
/* Set the local Naive Bayes model as input for the master-node algorithm */
masterAlgorithm.input.add(training::partialModels, dataForStep2FromStep1);
}
/* Merge and finalizeCompute the Naive Bayes model on the master node */
masterAlgorithm.compute();
masterAlgorithm.finalizeCompute();
/* Retrieve the algorithm results */
trainingResult = masterAlgorithm.getResult();
}
}
void testModel()
{
/* Retrieve the input data from a .csv file */
CSRNumericTable *testDataTable = createSparseTable<float>(testDatasetFileName);
/* Create an algorithm object to predict values of the Naive Bayes model */
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, CSRNumericTablePtr(testDataTable));
algorithm.input.set(classifier::prediction::model, trainingResult->get(classifier::training::model));
/* Predict values of the Naive Bayes model */
algorithm.compute();
/* Retrieve the algorithm results */
predictionResult = algorithm.getResult();
}
void printResults()
{
FileDataSource<CSVFeatureManager> testGroundTruth(testGroundTruthFileName, DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
testGroundTruth.loadDataBlock();
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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