Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

DtRegDenseBatch.java

/* file: DtRegDenseBatch.java */
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/*
// Content:
// Java example of decision tree regression
//
*/
package com.intel.daal.examples.decision_tree;
import com.intel.daal.algorithms.decision_tree.regression.Model;
import com.intel.daal.algorithms.decision_tree.regression.prediction.*;
import com.intel.daal.algorithms.decision_tree.regression.training.*;
import com.intel.daal.algorithms.decision_tree.*;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.data_management.data.MergedNumericTable;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.data_management.data_source.FileDataSource;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
import com.intel.daal.data_management.data.*;
class DtRegDenseBatch {
/* Input data set parameters */
private static final String trainDataset ="../data/batch/decision_tree_train.csv";
private static final String pruneDataset ="../data/batch/decision_tree_prune.csv";
private static final String testDataset = "../data/batch/decision_tree_test.csv";
private static final int nFeatures = 5; /* Number of features in training and testing data sets */
private static NumericTable testGroundTruth;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
TrainingResult trainingResult = trainModel();
PredictionResult predictionResult = testModel(trainingResult);
printResults(predictionResult);
context.dispose();
}
private static TrainingResult trainModel() {
/* Retrieve the data from the input data sets */
FileDataSource trainDataSource = new FileDataSource(context, trainDataset,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for training data and dependent variables */
NumericTable trainData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.NotAllocate);
NumericTable trainGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.NotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(trainData);
mergedData.addNumericTable(trainGroundTruth);
/* Retrieve the data from an input file */
trainDataSource.loadDataBlock(mergedData);
/* Retrieve the pruning data from the input data sets */
FileDataSource pruneDataSource = new FileDataSource(context, pruneDataset,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for pruning data and dependent variables */
NumericTable pruneData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.NotAllocate);
NumericTable pruneGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.NotAllocate);
MergedNumericTable pruneMergedData = new MergedNumericTable(context);
pruneMergedData.addNumericTable(pruneData);
pruneMergedData.addNumericTable(pruneGroundTruth);
/* Retrieve the pruning data from an input file */
pruneDataSource.loadDataBlock(pruneMergedData);
/* Create algorithm objects to train the decision tree regression model */
TrainingBatch algorithm = new TrainingBatch(context, Float.class, TrainingMethod.defaultDense);
/* Pass the training data set with labels, and pruning dataset with labels to the algorithm */
algorithm.input.set(TrainingInputId.data, trainData);
algorithm.input.set(TrainingInputId.dependentVariables, trainGroundTruth);
algorithm.input.set(TrainingInputId.dataForPruning, pruneData);
algorithm.input.set(TrainingInputId.dependentVariablesForPruning, pruneGroundTruth);
/* Train the decision tree regression model */
TrainingResult trainingResult = algorithm.compute();
return trainingResult;
}
private static PredictionResult testModel(TrainingResult trainingResult) {
FileDataSource testDataSource = new FileDataSource(context, testDataset,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for testing data and dependent variables */
NumericTable testData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.NotAllocate);
testGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.NotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(testData);
mergedData.addNumericTable(testGroundTruth);
/* Retrieve the data from an input file */
testDataSource.loadDataBlock(mergedData);
/* Create algorithm objects for decision tree regression prediction */
PredictionBatch algorithm = new PredictionBatch(context, Float.class, PredictionMethod.defaultDense);
/* Pass a testing data set and the trained model to the algorithm */
Model model = trainingResult.get(TrainingResultId.model);
algorithm.input.set(NumericTableInputId.data, testData);
algorithm.input.set(ModelInputId.model, model);
/* Compute prediction results */
return algorithm.compute();
}
private static void printResults(PredictionResult predictionResult) {
NumericTable predictionResults = predictionResult.get(PredictionResultId.prediction);
Service.printClassificationResult(testGroundTruth, predictionResults, "Ground truth", "Regression results",
"Decision tree regression results (first 20 observations):", 20);
System.out.println("");
}
}

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