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

df_cls_dense_batch_model_builder.cpp

/* file: df_cls_dense_batch_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 decision forest classification model building.
!
! The program builds the decision forest classification model
! via Model Builder 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::decision_forest::classification;
/* Input data set parameters */
const string testDatasetFileName = "../data/batch/df_classification_model_builder_test.csv";
const size_t categoricalFeaturesIndices[] = { 2 };
const size_t nFeatures = 3; /* Number of features in training and testing data sets */
/* Decision forest parameters */
const size_t nTrees = 3;
const size_t nClasses = 5; /* Number of classes */
void testModel(decision_forest::classification::ModelPtr& model);
decision_forest::classification::ModelPtr buildModel();
void loadData(const std::string& fileName, NumericTablePtr& pData, NumericTablePtr& pDependentVar);
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &testDatasetFileName);
decision_forest::classification::ModelPtr model = buildModel();
testModel(model);
return 0;
}
decision_forest::classification::ModelPtr buildModel()
{
const size_t nNodes = 3;
ModelBuilder modelBuilder(nClasses, nTrees);
ModelBuilder::TreeId tree1 = modelBuilder.createTree(nNodes);
ModelBuilder::NodeId root1 = modelBuilder.addSplitNode(tree1, ModelBuilder::noParent, 0, 0, 0.174108);
ModelBuilder::NodeId child12 = modelBuilder.addLeafNode(tree1, root1, 1, 4);
ModelBuilder::NodeId child11 = modelBuilder.addLeafNode(tree1, root1, 0, 0);
ModelBuilder::TreeId tree2 = modelBuilder.createTree(nNodes);
ModelBuilder::NodeId root2 = modelBuilder.addSplitNode(tree2, ModelBuilder::noParent, 0, 1, 0.571184);
ModelBuilder::NodeId child22 = modelBuilder.addLeafNode(tree2, root2, 1, 4);
ModelBuilder::NodeId child21 = modelBuilder.addLeafNode(tree2, root2, 0, 2);
ModelBuilder::TreeId tree3 = modelBuilder.createTree(nNodes);
ModelBuilder::NodeId root3 = modelBuilder.addSplitNode(tree3, ModelBuilder::noParent, 0, 0, 0.303995);
ModelBuilder::NodeId child32 = modelBuilder.addLeafNode(tree3, root3, 1, 4);
ModelBuilder::NodeId child31 = modelBuilder.addLeafNode(tree3, root3, 0, 2);
return modelBuilder.getModel();
}
void testModel(decision_forest::classification::ModelPtr& model)
{
/* 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 decision forest classification */
prediction::Batch<> algorithm(nClasses);
/* Pass a testing data set and the trained model to the algorithm */
algorithm.input.set(classifier::prediction::data, testData);
/* set model obtained by builder */
algorithm.input.set(classifier::prediction::model, model);
/* Predict values of decision forest classification */
algorithm.compute();
/* Retrieve the algorithm results */
classifier::prediction::ResultPtr predictionResult = algorithm.getResult();
printNumericTable(predictionResult->get(classifier::prediction::prediction),
"Decision forest prediction results (first 10 rows):", 10);
printNumericTable(testGroundTruth, "Ground truth (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());
NumericTableDictionaryPtr pDictionary = pData->getDictionarySharedPtr();
for(size_t i = 0, n = sizeof(categoricalFeaturesIndices) / sizeof(categoricalFeaturesIndices[0]); i < n; ++i)
(*pDictionary)[categoricalFeaturesIndices[i]].featureType = data_feature_utils::DAAL_CATEGORICAL;
}

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