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

logitboost_dense_batch.cpp

/* file: logitboost_dense_batch.cpp */
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* Copyright 2014-2018 Intel Corporation.
*
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/*
! Content:
! C++ example of LogitBoost classification.
!
! The program trains the LogitBoost model on a supplied training datasetFileName
! and then performs classification of previously unseen data.
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
/* Input data set parameters */
string trainDatasetFileName = "../data/batch/logitboost_train.csv";
string testDatasetFileName = "../data/batch/logitboost_test.csv";
const size_t nFeatures = 20;
const size_t nClasses = 5;
/* LogitBoost algorithm parameters */
const size_t maxIterations = 100; /* Maximum number of terms in additive regression */
const double accuracyThreshold = 0.01; /* Training accuracy */
/* Model object for the LogitBoost algorithm */
logitboost::ModelPtr model;
classifier::prediction::ResultPtr predictionResult;
NumericTablePtr testGroundTruth;
void trainModel();
void testModel();
void printResults();
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 2, &trainDatasetFileName, &testDatasetFileName);
trainModel();
testModel();
printResults();
return 0;
}
void trainModel()
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> trainDataSource(trainDatasetFileName,
DataSource::notAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for training data and labels */
NumericTablePtr trainData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
NumericTablePtr trainGroundTruth(new HomogenNumericTable<>(1, 0, NumericTable::doNotAllocate));
NumericTablePtr mergedData(new MergedNumericTable(trainData, trainGroundTruth));
/* Retrieve the data from the input file */
trainDataSource.loadDataBlock(mergedData.get());
/* Create an algorithm object to train the LogitBoost model */
logitboost::training::Batch<> algorithm(nClasses);
algorithm.parameter.maxIterations = maxIterations;
algorithm.parameter.accuracyThreshold = accuracyThreshold;
/* Pass the training data set and dependent values to the algorithm */
algorithm.input.set(classifier::training::data, trainData);
algorithm.input.set(classifier::training::labels, trainGroundTruth);
/* Train the LogitBoost model */
algorithm.compute();
/* Retrieve the results of the training algorithm */
logitboost::training::ResultPtr trainingResult = algorithm.getResult();
model = trainingResult->get(classifier::training::model);
}
void testModel()
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file */
FileDataSource<CSVFeatureManager> testDataSource(testDatasetFileName,
DataSource::notAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for testing data and labels */
NumericTablePtr testData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
testGroundTruth = NumericTablePtr(new HomogenNumericTable<>(1, 0, NumericTable::doNotAllocate));
NumericTablePtr mergedData(new MergedNumericTable(testData, testGroundTruth));
/* Retrieve the data from input file */
testDataSource.loadDataBlock(mergedData.get());
/* Create algorithm objects for LogitBoost prediction with the default method */
logitboost::prediction::Batch<> algorithm(nClasses);
/* Pass the testing data set and trained model to the algorithm */
algorithm.input.set(classifier::prediction::data, testData);
algorithm.input.set(classifier::prediction::model, model);
/* Compute prediction results */
algorithm.compute();
/* Retrieve algorithm results */
predictionResult = algorithm.getResult();
}
void printResults()
{
printNumericTables<int, int>(testGroundTruth,
predictionResult->get(classifier::prediction::prediction),
"Ground truth", "Classification results",
"LogitBoost classification results (first 20 observations):", 20);
}

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