C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

svm_two_class_metrics_dense_batch.cpp

/* file: svm_two_class_metrics_dense_batch.cpp */
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/*
! Content:
! C++ example of two-class support vector machine (SVM) quality metrics
!
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::data_management;
using namespace daal::algorithms;
using namespace daal::algorithms::classifier::quality_metric;
/* Input data set parameters */
string trainDatasetFileName = "../data/batch/svm_two_class_train_dense.csv";
string testDatasetFileName = "../data/batch/svm_two_class_test_dense.csv";
const size_t nFeatures = 20;
/* Parameters for the SVM kernel function */
kernel_function::KernelIfacePtr kernel(new kernel_function::linear::Batch<>());
/* Model object for the SVM algorithm */
svm::training::ResultPtr trainingResult;
classifier::prediction::ResultPtr predictionResult;
svm::quality_metric_set::ResultCollectionPtr qualityMetricSetResult;
NumericTablePtr predictedLabels;
NumericTablePtr groundTruthLabels;
void trainModel();
void testModel();
void testModelQuality();
void printResults();
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 2, &trainDatasetFileName, &testDatasetFileName);
trainModel();
testModel();
testModelQuality();
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 SVM model */
svm::training::Batch<> algorithm;
algorithm.parameter.kernel = kernel;
algorithm.parameter.cacheSize = 40000000;
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(classifier::training::data, trainData);
algorithm.input.set(classifier::training::labels, trainGroundTruth);
/* Build the SVM model */
algorithm.compute();
/* Retrieve the algorithm results */
trainingResult = algorithm.getResult();
}
void testModel()
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> testDataSource(testDatasetFileName,
DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create Numeric Tables for testing data and labels */
NumericTablePtr testData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
groundTruthLabels = NumericTablePtr(new HomogenNumericTable<>(1, 0, NumericTable::doNotAllocate));
NumericTablePtr mergedData(new MergedNumericTable(testData, groundTruthLabels));
/* Retrieve the data from input file */
testDataSource.loadDataBlock(mergedData.get());
/* Create an algorithm object to predict SVM values */
svm::prediction::Batch<> algorithm;
algorithm.parameter.kernel = kernel;
/* 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 SVM values */
algorithm.compute();
/* Retrieve the algorithm results */
predictionResult = algorithm.getResult();
}
void testModelQuality()
{
/* Retrieve predicted labels */
predictedLabels = predictionResult->get(classifier::prediction::prediction);
/* Create a quality metric set object to compute quality metrics of the SVM algorithm */
svm::quality_metric_set::Batch qualityMetricSet;
binary_confusion_matrix::InputPtr input =
qualityMetricSet.getInputDataCollection()->getInput(svm::quality_metric_set::confusionMatrix);
input->set(binary_confusion_matrix::predictedLabels, predictedLabels);
input->set(binary_confusion_matrix::groundTruthLabels, groundTruthLabels);
/* Compute quality metrics */
qualityMetricSet.compute();
/* Retrieve the quality metrics */
qualityMetricSetResult = qualityMetricSet.getResultCollection();
}
void printResults()
{
/* Print the classification results */
printNumericTables<int, float>(groundTruthLabels.get(), predictedLabels.get(),
"Ground truth", "Classification results",
"SVM classification results (first 20 observations):", 20);
/* Print the quality metrics */
binary_confusion_matrix::ResultPtr qualityMetricResult =
qualityMetricSetResult->getResult(svm::quality_metric_set::confusionMatrix);
printNumericTable(qualityMetricResult->get(binary_confusion_matrix::confusionMatrix), "Confusion matrix:");
BlockDescriptor<> block;
NumericTablePtr qualityMetricsTable = qualityMetricResult->get(binary_confusion_matrix::binaryMetrics);
qualityMetricsTable->getBlockOfRows(0, 1, readOnly, block);
float *qualityMetricsData = block.getBlockPtr();
std::cout << "Accuracy: " << qualityMetricsData[binary_confusion_matrix::accuracy ] << std::endl;
std::cout << "Precision: " << qualityMetricsData[binary_confusion_matrix::precision ] << std::endl;
std::cout << "Recall: " << qualityMetricsData[binary_confusion_matrix::recall ] << std::endl;
std::cout << "F-score: " << qualityMetricsData[binary_confusion_matrix::fscore ] << std::endl;
std::cout << "Specificity: " << qualityMetricsData[binary_confusion_matrix::specificity] << std::endl;
std::cout << "AUC: " << qualityMetricsData[binary_confusion_matrix::AUC ] << std::endl;
qualityMetricsTable->releaseBlockOfRows(block);
}

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