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

df_cls_dense_batch.cpp

/* file: df_cls_dense_batch.cpp */
/*******************************************************************************
* Copyright 2014-2018 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 in the batch processing mode.
!
! The program trains the decision forest classification model on a training
! datasetFileName 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 trainDatasetFileName = "../data/batch/df_classification_train.csv";
const string testDatasetFileName = "../data/batch/df_classification_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 = 10;
const size_t minObservationsInLeafNode = 8;
const size_t nClasses = 5; /* Number of classes */
training::ResultPtr trainModel();
void testModel(const training::ResultPtr& res);
void loadData(const std::string& fileName, NumericTablePtr& pData, NumericTablePtr& pDependentVar);
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 2, &trainDatasetFileName, &testDatasetFileName);
training::ResultPtr trainingResult = trainModel();
testModel(trainingResult);
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.varImportance = algorithms::decision_forest::training::MDI;
algorithm.parameter.resultsToCompute = algorithms::decision_forest::training::computeOutOfBagError;
/* Build the decision forest classification model */
algorithm.compute();
/* Retrieve the algorithm results */
training::ResultPtr trainingResult = algorithm.getResult();
printNumericTable(trainingResult->get(training::variableImportance), "Variable importance results: ");
printNumericTable(trainingResult->get(training::outOfBagError), "OOB error: ");
return trainingResult;
}
void testModel(const training::ResultPtr& trainingResult)
{
/* 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);
algorithm.input.set(classifier::prediction::model, trainingResult->get(classifier::training::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.