SVMMultiClassDenseBatch.java

/* file: SVMMultiClassDenseBatch.java */
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/*
 //  Content:
 //     Java example of multi-class support vector machine (SVM) classification
 //
 //     The program trains multi-class SVM model on a supplied training data set
 //     in dense format and then performs classification of previously unseen
 //     data.
 */

package com.intel.daal.examples.svm;

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.multi_class_classifier.Model;
import com.intel.daal.algorithms.multi_class_classifier.prediction.*;
import com.intel.daal.algorithms.multi_class_classifier.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 SVMMultiClassDenseBatch {

    /* Input data set parameters */
    private static final String trainDatasetFileName = "../data/batch/svm_multi_class_train_dense.csv";

    private static final String testDatasetFileName  = "../data/batch/svm_multi_class_test_dense.csv";

    private static final int nFeatures     = 20;
    private static final int nClasses      = 5;

    private static TrainingResult   trainingResult;
    private static PredictionResult predictionResult;
    private static NumericTable     testGroundTruth;

    private static com.intel.daal.algorithms.svm.training.TrainingBatch twoClassTraining;
    private static com.intel.daal.algorithms.svm.prediction.PredictionBatch twoClassPrediction;

    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() {

        twoClassTraining = new com.intel.daal.algorithms.svm.training.TrainingBatch(
                context, Float.class, com.intel.daal.algorithms.svm.training.TrainingMethod.boser);

        twoClassPrediction = new com.intel.daal.algorithms.svm.prediction.PredictionBatch(
                context, Float.class, com.intel.daal.algorithms.svm.prediction.PredictionMethod.defaultDense);

        /* Retrieve the data from input data sets */
        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 an algorithm to train the multi-class SVM model */
        TrainingBatch algorithm = new TrainingBatch(context, Float.class, TrainingMethod.oneAgainstOne, nClasses);

        /* Set parameters for the multi-class SVM algorithm */
        algorithm.parameter.setTraining(twoClassTraining);
        algorithm.parameter.setPrediction(twoClassPrediction);

        /* 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 multi-class SVM model */
        trainingResult = algorithm.compute();
    }

    private static void testModel() {

        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 a numeric table to store the prediction results */
        PredictionBatch algorithm = new PredictionBatch(context, Float.class, PredictionMethod.multiClassClassifierWu, nClasses);

        algorithm.parameter.setTraining(twoClassTraining);
        algorithm.parameter.setPrediction(twoClassPrediction);

        Model model = trainingResult.get(TrainingResultId.model);

        /* Pass a testing data set and the trained model to the algorithm */
        algorithm.input.set(NumericTableInputId.data, testData);
        algorithm.input.set(ModelInputId.model, model);

        /* Compute the prediction results */
        predictionResult = algorithm.compute();
    }

    private static void printResults() {
        NumericTable predictionResults = predictionResult.get(PredictionResultId.prediction);
        Service.printClassificationResult(testGroundTruth, predictionResults, "Ground truth", "Classification results",
                "Multi-class SVM classification sample program results (first 20 observations):", 20);
        System.out.println("");
    }
}
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