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

linear_regression_norm_eq_distributed_mpi.cpp

/* file: linear_regression_norm_eq_distributed_mpi.cpp */
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/*
! Content:
! C++ sample of multiple linear regression in the distributed processing
! mode.
!
! The program trains the multiple linear regression model on a training
! data set with the normal equations method and computes regression for the
! test data.
!******************************************************************************/
#include <mpi.h>
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms::linear_regression;
const string trainDatasetFileNames[] =
{
"./data/distributed/linear_regression_train_1.csv",
"./data/distributed/linear_regression_train_2.csv",
"./data/distributed/linear_regression_train_3.csv",
"./data/distributed/linear_regression_train_4.csv"
};
string testDatasetFileName = "./data/distributed/linear_regression_test.csv";
const size_t nBlocks = 4;
const size_t nFeatures = 10; /* Number of features in training and testing data sets */
const size_t nDependentVariables = 2; /* Number of dependent variables that correspond to each observation */
int rankId, comm_size;
#define mpi_root 0
void trainModel();
void testModel();
training::ResultPtr trainingResult;
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();
}
MPI_Finalize();
return 0;
}
void trainModel()
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> trainDataSource(trainDatasetFileNames[rankId],
DataSource::notAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for training data and labels */
NumericTablePtr trainData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
NumericTablePtr trainDependentVariables(new HomogenNumericTable<>(nDependentVariables, 0, NumericTable::doNotAllocate));
NumericTablePtr mergedData(new MergedNumericTable(trainData, trainDependentVariables));
/* Retrieve the data from the input file */
trainDataSource.loadDataBlock(mergedData.get());
/* Create an algorithm object to train the multiple linear regression model based on the local-node data */
training::Distributed<step1Local> localAlgorithm;
/* Pass a training data set and dependent values to the algorithm */
localAlgorithm.input.set(training::data, trainData);
localAlgorithm.input.set(training::dependentVariables, trainDependentVariables);
/* Train the multiple linear regression 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 = services::SharedPtr<byte>(new byte[perNodeArchLength * nBlocks]);
}
byte *nodeResults = new byte[perNodeArchLength];
dataArch.copyArchiveToArray( nodeResults, perNodeArchLength );
/* Transfer partial results to step 2 on the root node */
MPI_Gather( nodeResults, perNodeArchLength, MPI_CHAR, serializedData.get(), perNodeArchLength, MPI_CHAR, mpi_root,
MPI_COMM_WORLD);
delete[] nodeResults;
if(rankId == mpi_root)
{
/* Create an algorithm object to build the final multiple linear regression model on the master node */
training::Distributed<step2Master> masterAlgorithm;
for(size_t i = 0; i < nBlocks ; i++)
{
/* Deserialize partial results from step 1 */
OutputDataArchive dataArch(serializedData.get() + perNodeArchLength * i, perNodeArchLength);
training::PartialResultPtr dataForStep2FromStep1 = training::PartialResultPtr
(new training::PartialResult());
dataForStep2FromStep1->deserialize(dataArch);
/* Set the local multiple linear regression model as input for the master-node algorithm */
masterAlgorithm.input.add(training::partialModels, dataForStep2FromStep1);
}
/* Merge and finalizeCompute the multiple linear regression model on the master node */
masterAlgorithm.compute();
masterAlgorithm.finalizeCompute();
/* Retrieve the algorithm results */
trainingResult = masterAlgorithm.getResult();
printNumericTable(trainingResult->get(training::model)->getBeta(), "Linear Regression coefficients:");
}
}
void testModel()
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> testDataSource(testDatasetFileName, DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for testing data and ground truth values */
NumericTablePtr testData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
NumericTablePtr testGroundTruth(new HomogenNumericTable<>(nDependentVariables, 0, NumericTable::doNotAllocate));
NumericTablePtr mergedData(new MergedNumericTable(testData, testGroundTruth));
/* Retrieve the data from an input file */
testDataSource.loadDataBlock(mergedData.get());
/* Create an algorithm object to predict values of multiple linear regression */
prediction::Batch<> algorithm;
/* Pass a testing data set and the trained model to the algorithm */
algorithm.input.set(prediction::data, testData);
algorithm.input.set(prediction::model, trainingResult->get(training::model));
/* Predict values of multiple linear regression */
algorithm.compute();
/* Retrieve the algorithm results */
predictionResult = algorithm.getResult();
printNumericTable(predictionResult->get(prediction::prediction),
"Linear Regression prediction results: (first 10 rows):", 10);
printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10);
}

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