svm_multi_class_csr_batch.cpp

/* file: svm_multi_class_csr_batch.cpp */
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* Copyright 2014-2019 Intel Corporation.
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/*
!  Content:
!    C++ example of multi-class support vector machine (SVM) classification
!
!******************************************************************************/

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

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

/* Input data set parameters */
string trainDatasetFileName     = "../data/batch/svm_multi_class_train_csr.csv";
string trainLabelsFileName      = "../data/batch/svm_multi_class_train_labels.csv";

string testDatasetFileName      = "../data/batch/svm_multi_class_test_csr.csv";
string testLabelsFileName       = "../data/batch/svm_multi_class_test_labels.csv";

const size_t nClasses           = 5;

services::SharedPtr<svm::training::Batch<> > training(new svm::training::Batch<>());
services::SharedPtr<svm::prediction::Batch<> > prediction(new svm::prediction::Batch<>());

multi_class_classifier::training::ResultPtr trainingResult;
classifier::prediction::ResultPtr predictionResult;
kernel_function::KernelIfacePtr kernel(
    new kernel_function::linear::Batch<float, kernel_function::linear::fastCSR>());
NumericTablePtr testGroundTruth;

void trainModel();
void testModel();
void printResults();

int main(int argc, char *argv[])
{
    checkArguments(argc, argv, 4, &trainDatasetFileName, &trainLabelsFileName, &testDatasetFileName, &testLabelsFileName);

    training->parameter.cacheSize = 100000000;
    training->parameter.kernel = kernel;
    prediction->parameter.kernel = kernel;

    trainModel();

    testModel();

    printResults();

    return 0;
}

void trainModel()
{
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
    FileDataSource<CSVFeatureManager> trainLabelsDataSource(trainLabelsFileName,
                                                            DataSource::doAllocateNumericTable,
                                                            DataSource::doDictionaryFromContext);

    /* Create numeric table for training data */
    CSRNumericTablePtr trainData(createSparseTable<float>(trainDatasetFileName));

    /* Retrieve the data from the input file */
    trainLabelsDataSource.loadDataBlock();

    /* Create an algorithm object to train the multi-class SVM model */
    multi_class_classifier::training::Batch<> algorithm(nClasses);

    algorithm.parameter.training = training;
    algorithm.parameter.prediction = prediction;

    /* Pass a training data set and dependent values to the algorithm */
    algorithm.input.set(classifier::training::data, trainData);
    algorithm.input.set(classifier::training::labels, trainLabelsDataSource.getNumericTable());

    /* Build the multi-class SVM model */
    algorithm.compute();

    /* Retrieve the algorithm results */
    trainingResult = algorithm.getResult();
}

void testModel()
{

    /* Create Numeric Tables for testing data */
    NumericTablePtr testData(createSparseTable<float>(testDatasetFileName));

    /* Create an algorithm object to predict multi-class SVM values */
    multi_class_classifier::prediction::Batch<> algorithm(nClasses);

    algorithm.parameter.training = training;
    algorithm.parameter.prediction = prediction;

    /* 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 multi-class SVM values */
    algorithm.compute();

    /* Retrieve the algorithm results */
    predictionResult = algorithm.getResult();
}

void printResults()
{
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file */
    FileDataSource<CSVFeatureManager> testLabelsDataSource(testLabelsFileName,
                                                           DataSource::doAllocateNumericTable,
                                                           DataSource::doDictionaryFromContext);
    /* Retrieve the data from input file */
    testLabelsDataSource.loadDataBlock();
    testGroundTruth = testLabelsDataSource.getNumericTable();

    printNumericTables<int, int>(testGroundTruth,
                                 predictionResult->get(classifier::prediction::prediction),
                                 "Ground truth", "Classification results",
                                 "Multi-class SVM classification sample program results (first 20 observations):", 20);
}
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