lin_reg_model_builder.cpp

/* file: lin_reg_model_builder.cpp */
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/*
!  Content:
!    C++ example of multiple linear regression in the batch processing mode.
!
!    The program trains the multiple linear regression model on a training data
!    set with a QR decomposition-based method and computes regression for the
!    test data.
!******************************************************************************/

#include "daal.h"
#include "service.h"

using namespace std;
using namespace daal;
using namespace daal::algorithms::linear_regression;

/* Input data set parameters */
string trainedModelFileName            = "../data/batch/linear_regression_trained_model.csv";
string testDatasetFileName             = "../data/batch/linear_regression_test.csv";

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 */

ModelPtr buildModel();
void testModel(ModelPtr&);

int main(int argc, char *argv[])
{
    checkArguments(argc, argv, 2, &trainedModelFileName, &testDatasetFileName);

    ModelPtr builtModel = buildModel();
    testModel(builtModel);

    return 0;
}

ModelPtr buildModel()
{
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file */
    FileDataSource<CSVFeatureManager> modelSource(trainedModelFileName,
                                                     DataSource::doAllocateNumericTable,
                                                     DataSource::doDictionaryFromContext);

    /* Create Numeric Table for beta coefficients */
    NumericTablePtr beta(new HomogenNumericTable<>(nFeatures+1, 0, NumericTable::doNotAllocate));
    /* Get beta from trained model */
    modelSource.loadDataBlock(beta.get());

    /* Retrive pointer to the begining of beta */
    BlockDescriptor<> blockResult;
    beta->getBlockOfRows(0, beta->getNumberOfRows(), readOnly, blockResult);
    /* Define the size of beta */
    size_t numberOfBetas = (beta->getNumberOfRows())*(beta->getNumberOfColumns());

    /* Initialize iterators for beta array with itrecepts */
    float* first = blockResult.getBlockPtr();
    float* last = first + numberOfBetas;

    /* Create model builder with true intercept flag */
    ModelBuilder<> modelBuilder(nFeatures, nDependentVariables);

    /* Set beta */
    modelBuilder.setBeta(first, last);
    beta->releaseBlockOfRows(blockResult);

    printNumericTable(modelBuilder.getModel()->getBeta(), "Linear Regression coefficients of built model:");

    return modelBuilder.getModel();
}

void testModel(ModelPtr& inputModel)
{
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the test 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));

    /* Load the data from the data 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, inputModel);

    /* Predict values of multiple linear regression */
    algorithm.compute();

    /* Retrieve the algorithm results */
    NumericTablePtr prediction = algorithm.getResult()->get(prediction::prediction);
    printNumericTable(prediction, "Linear Regression prediction results: (first 10 rows):", 10);
    printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10);
}
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