Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

MnNaiveBayesDenseBatch.java

/* file: MnNaiveBayesDenseBatch.java */
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/*
// Content:
// Java example of Naive Bayes classification in the batch processing mode.
//
// The program trains the Naive Bayes model on a supplied training data set
// in dense format and then performs classification of previously unseen
// data.
*/
package com.intel.daal.examples.naive_bayes;
import com.intel.daal.algorithms.classifier.prediction.ModelInputId;
import com.intel.daal.algorithms.classifier.prediction.NumericTableInputId;
import com.intel.daal.algorithms.classifier.prediction.PredictionResult;
import com.intel.daal.algorithms.classifier.prediction.PredictionResultId;
import com.intel.daal.algorithms.classifier.training.InputId;
import com.intel.daal.algorithms.classifier.training.TrainingResultId;
import com.intel.daal.algorithms.multinomial_naive_bayes.Model;
import com.intel.daal.algorithms.multinomial_naive_bayes.prediction.*;
import com.intel.daal.algorithms.multinomial_naive_bayes.training.*;
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;
class MnNaiveBayesDenseBatch {
/* Input data set parameters */
private static final String trainDatasetFileName = "../data/batch/naivebayes_train_dense.csv";
private static final String testDatasetFileName = "../data/batch/naivebayes_test_dense.csv";
private static final int nFeatures = 20;
private static final long nClasses = 20;
/* Parameters for the Naive Bayes algorithm */
private static TrainingResult trainingResult;
private static PredictionResult predictionResult;
private static NumericTable testGroundTruth;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
trainModel();
testModel();
printResults();
context.dispose();
}
private static void trainModel() throws java.io.FileNotFoundException, java.io.IOException {
/* Initialize FileDataSource to retrieve the input data from a .csv file */
FileDataSource trainDataSource = new FileDataSource(context, trainDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for training data and labels */
NumericTable trainData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
NumericTable trainGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(trainData);
mergedData.addNumericTable(trainGroundTruth);
/* Retrieve the data from an input file */
trainDataSource.loadDataBlock(mergedData);
/* Create algorithm objects to train the Naive Bayes model */
TrainingBatch algorithm = new TrainingBatch(context, Float.class, TrainingMethod.defaultDense, nClasses);
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(InputId.data, trainData);
algorithm.input.set(InputId.labels, trainGroundTruth);
/* Train the Naive Bayes model */
trainingResult = algorithm.compute();
}
private static void testModel() throws java.io.FileNotFoundException, java.io.IOException {
FileDataSource testDataSource = new FileDataSource(context, testDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for testing data and labels */
NumericTable testData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
testGroundTruth = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(testData);
mergedData.addNumericTable(testGroundTruth);
/* Retrieve the data from an input file */
testDataSource.loadDataBlock(mergedData);
/* Create algorithm objects to predict Naive Bayes values with the defaultDense method */
PredictionBatch algorithm = new PredictionBatch(context, Float.class, PredictionMethod.defaultDense, nClasses);
/* Pass a testing data set and the trained model to the algorithm */
algorithm.input.set(NumericTableInputId.data, testData);
Model model = trainingResult.get(TrainingResultId.model);
algorithm.input.set(ModelInputId.model, model);
/* Compute the prediction results */
predictionResult = algorithm.compute();
}
private static void printResults() throws java.io.FileNotFoundException, java.io.IOException {
NumericTable expected = testGroundTruth;
NumericTable prediction = predictionResult.get(PredictionResultId.prediction);
Service.printClassificationResult(expected, prediction, "Ground truth", "Classification results",
"NaiveBayes classification results (first 20 observations):", 20);
System.out.println("");
}
}

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