C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

lin_reg_model_builder.cpp

/* file: lin_reg_model_builder.cpp */
/*******************************************************************************
* Copyright 2014-2019 Intel Corporation.
*
* This software and the related documents are Intel copyrighted materials, and
* your use of them is governed by the express license under which they were
* provided to you (License). Unless the License provides otherwise, you may not
* use, modify, copy, publish, distribute, disclose or transmit this software or
* the related documents without Intel's prior written permission.
*
* This software and the related documents are provided as is, with no express
* or implied warranties, other than those that are expressly stated in the
* License.
*******************************************************************************/
/*
! 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);
}

For more complete information about compiler optimizations, see our Optimization Notice.