Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

MnNaiveBayesCSROnline.java

/* file: MnNaiveBayesCSROnline.java */
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/*
// Content:
// Java example of Naive Bayes classification in the online processing mode.
//
// The program trains the Naive Bayes model on a supplied training data set
// in compressed sparse rows (CSR) 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.CSRNumericTable;
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 MnNaiveBayesCSROnline {
/* Input data set parameters */
private static final String[] trainGroundTruthFileNames = { "../data/online/naivebayes_train_labels_1.csv",
"../data/online/naivebayes_train_labels_2.csv", "../data/online/naivebayes_train_labels_3.csv",
"../data/online/naivebayes_train_labels_4.csv" };
private static final String[] trainDatasetFileNames = { "../data/online/naivebayes_train_csr_1.csv",
"../data/online/naivebayes_train_csr_2.csv", "../data/online/naivebayes_train_csr_3.csv",
"../data/online/naivebayes_train_csr_4.csv" };
private static final String testDatasetFileName = "../data/online/naivebayes_test_csr.csv";
private static final String testGroundTruthFileName = "../data/online/naivebayes_test_labels.csv";
private static final int nTrainObservations = 8000;
private static final int nTestObservations = 2000;
private static final long nClasses = 20;
private static final int nBlocks = 4;
/* Parameters for the Naive Bayes algorithm */
private static TrainingResult trainingResult;
private static PredictionResult predictionResult;
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 {
/* Create algorithm objects to train the Naive Bayes model */
TrainingOnline algorithm = new TrainingOnline(context, Float.class, TrainingMethod.fastCSR, nClasses);
for (int node = 0; node < nBlocks; node++) {
/* Initialize FileDataSource to retrieve the input data from a .csv file */
FileDataSource trainGroundTruthSource = new FileDataSource(context, trainGroundTruthFileNames[node],
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
/* Create Numeric Tables for training data and labels */
CSRNumericTable trainData = Service.createSparseTable(context, trainDatasetFileNames[node]);
NumericTable labels = trainGroundTruthSource.getNumericTable();
/* Retrieve the data from input file */
trainGroundTruthSource.loadDataBlock(nTrainObservations);
/* Pass a training data set and dependent values to the algorithm */
algorithm.input.set(InputId.data, trainData);
algorithm.input.set(InputId.labels, labels);
/* Train the Naive Bayes model */
algorithm.compute();
}
/* Retrieve the algorithm results */
trainingResult = algorithm.finalizeCompute();
}
private static void testModel() throws java.io.FileNotFoundException, java.io.IOException {
/* Create algorithm objects to predict Naive Bayes values with the fastCSR method */
PredictionBatch algorithm = new PredictionBatch(context, Float.class, PredictionMethod.fastCSR, nClasses);
/* Create Numeric Table for test data */
CSRNumericTable testData = Service.createSparseTable(context, testDatasetFileName);
/* 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 {
FileDataSource testGroundTruth = new FileDataSource(context, testGroundTruthFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
testGroundTruth.loadDataBlock(nTestObservations);
NumericTable expected = testGroundTruth.getNumericTable();
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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