C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

log_reg_dense_batch.cpp

/* file: log_reg_dense_batch.cpp */
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* Copyright 2014-2019 Intel Corporation.
*
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/*
! Content:
! C++ example of logistic regression in the batch processing mode.
!
! The program trains the logistic regression model on a training
! datasetFileName and computes classification for the test data.
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::algorithms::logistic_regression;
/* Input data set parameters */
const string trainDatasetFileName = "../data/batch/logreg_train.csv";
const string testDatasetFileName = "../data/batch/logreg_test.csv";
const size_t nFeatures = 6; /* Number of features in training and testing data sets */
const size_t nClasses = 5; /* Number of classes */
training::ResultPtr trainModel();
void testModel(const training::ResultPtr& res);
void loadData(const std::string& fileName, NumericTablePtr& pData, NumericTablePtr& pDependentVar);
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 2, &trainDatasetFileName, &testDatasetFileName);
training::ResultPtr trainingResult = trainModel();
testModel(trainingResult);
return 0;
}
training::ResultPtr trainModel()
{
/* Create Numeric Tables for training data and dependent variables */
NumericTablePtr trainData;
NumericTablePtr trainDependentVariable;
loadData(trainDatasetFileName, trainData, trainDependentVariable);
/* Create an algorithm object to train the logistic regression model */
training::Batch<> algorithm(nClasses);
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(classifier::training::data, trainData);
algorithm.input.set(classifier::training::labels, trainDependentVariable);
algorithm.parameter().penaltyL1 = 0.1f;
algorithm.parameter().penaltyL2 = 0.1f;
/* Build the logistic regression model */
algorithm.compute();
/* Retrieve the algorithm results */
training::ResultPtr trainingResult = algorithm.getResult();
logistic_regression::interface1::ModelPtr modelptr = trainingResult->get(classifier::training::model);
if(modelptr.get())
{
printNumericTable(modelptr->getBeta(), "Logistic Regression coefficients:");
}
else
{
std::cout << "Null model pointer" << std::endl;
}
return trainingResult;
}
void testModel(const training::ResultPtr& trainingResult)
{
/* Create Numeric Tables for testing data and ground truth values */
NumericTablePtr testData;
NumericTablePtr testGroundTruth;
loadData(testDatasetFileName, testData, testGroundTruth);
/* Create an algorithm object to predict values of logistic regression */
prediction::Batch<> algorithm(nClasses);
algorithm.parameter().resultsToCompute |= prediction::computeClassesProbabilities | prediction::computeClassesLogProbabilities;
/* 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 values of logistic regression */
algorithm.compute();
/* Retrieve the algorithm results */
logistic_regression::prediction::ResultPtr predictionResult = logistic_regression::prediction::Result::cast(algorithm.getResult());
printNumericTable(predictionResult->get(classifier::prediction::prediction),
"Logistic regression prediction results (first 10 rows):", 10);
printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10);
printNumericTable(predictionResult->get(logistic_regression::prediction::probabilities),
"Logistic regression prediction probabilities (first 10 rows):", 10);
printNumericTable(predictionResult->get(logistic_regression::prediction::logProbabilities),
"Logistic regression prediction log probabilities (first 10 rows):", 10);
}
void loadData(const std::string& fileName, NumericTablePtr& pData, NumericTablePtr& pDependentVar)
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> trainDataSource(fileName,
DataSource::notAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for training data and dependent variables */
pData.reset(new HomogenNumericTable<>(nFeatures, 0, NumericTable::notAllocate));
pDependentVar.reset(new HomogenNumericTable<>(1, 0, NumericTable::notAllocate));
NumericTablePtr mergedData(new MergedNumericTable(pData, pDependentVar));
/* Retrieve the data from input file */
trainDataSource.loadDataBlock(mergedData.get());
}

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