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

df_cls_traverse_model.cpp

/* file: df_cls_traverse_model.cpp */
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* Copyright 2014-2019 Intel Corporation.
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/*
! Content:
! C++ example of decision forest classification model traversal.
!
! The program trains the decision forest classification model on a training
! datasetFileName and prints the trained model by its depth-first traversing.
!******************************************************************************/
#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 trainDatasetFileName = "../data/batch/df_classification_train.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 = 2;
const size_t minObservationsInLeafNode = 8;
const size_t maxTreeDepth = 15;
const size_t nClasses = 5; /* Number of classes */
training::ResultPtr trainModel();
void loadData(const std::string& fileName, NumericTablePtr& pData, NumericTablePtr& pDependentVar);
void printModel(const daal::algorithms::decision_forest::classification::Model& m);
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &trainDatasetFileName);
training::ResultPtr trainingResult = trainModel();
printModel(*trainingResult->get(classifier::training::model));
return 0;
}
training::ResultPtr trainModel()
{
/* Create Numeric Tables for training data and dependent variables */
NumericTablePtr trainData;
NumericTablePtr trainDependentVariable;
loadData(trainDatasetFileName, trainData, trainDependentVariable);
/* Create an algorithm object to train the decision forest classification model */
training::Batch<> algorithm(nClasses);
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(classifier::training::data, trainData);
algorithm.input.set(classifier::training::labels, trainDependentVariable);
algorithm.parameter.nTrees = nTrees;
algorithm.parameter.featuresPerNode = nFeatures;
algorithm.parameter.minObservationsInLeafNode = minObservationsInLeafNode;
algorithm.parameter.maxTreeDepth = maxTreeDepth;
/* Build the decision forest classification model */
algorithm.compute();
/* Retrieve the algorithm results */
return algorithm.getResult();
}
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<double>(nFeatures, 0, NumericTable::notAllocate));
pDependentVar.reset(new HomogenNumericTable<double>(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;
}
class PrintNodeVisitor : public daal::algorithms::tree_utils::classification::TreeNodeVisitor
{
public:
virtual bool onLeafNode(const tree_utils::classification::LeafNodeDescriptor &desc)
{
for(size_t i = 0; i < desc.level; ++i)
std::cout << " ";
std::cout << "Level " << desc.level << ", leaf node. Response value = " << desc.label << ", Impurity = " << desc.impurity <<
", Number of samples = " << desc.nNodeSampleCount << std::endl;
return true;
}
virtual bool onSplitNode(const tree_utils::classification::SplitNodeDescriptor &desc)
{
for(size_t i = 0; i < desc.level; ++i)
std::cout << " ";
std::cout << "Level " << desc.level << ", split node. Feature index = " << desc.featureIndex <<
", feature value = " << desc.featureValue << ", Impurity = " << desc.impurity <<
", Number of samples = " << desc.nNodeSampleCount << std::endl;
return true;
}
};
void printModel(const daal::algorithms::decision_forest::classification::Model& m)
{
PrintNodeVisitor visitor;
std::cout << "Number of trees: " << m.getNumberOfTrees() << std::endl;
for(size_t i = 0, n = m.getNumberOfTrees(); i < n; ++i)
{
std::cout << "Tree #" << i << std::endl;
m.traverseDFS(i, visitor);
}
}

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