Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

AdaBoostDenseBatch.java

/* file: AdaBoostDenseBatch.java */
/*******************************************************************************
* Copyright 2014-2019 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:
// Java example of AdaBoost classification
*/
package com.intel.daal.examples.boosting.adaboost;
import com.intel.daal.algorithms.adaboost.Model;
import com.intel.daal.algorithms.adaboost.prediction.*;
import com.intel.daal.algorithms.adaboost.training.*;
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.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 AdaBoostDenseBatch {
/* Input data set parameters */
private static final String trainDataset = "../data/batch/adaboost_train.csv";
private static final String testDataset = "../data/batch/adaboost_test.csv";
private static final int nFeatures = 20;
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() {
/* Retrieve 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 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 AdaBoost model */
TrainingBatch algorithm = new TrainingBatch(context, Float.class, TrainingMethod.defaultDense);
/* 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 AdaBoost model */
trainingResult = algorithm.compute();
}
private static void testModel() {
FileDataSource testDataSource = new FileDataSource(context, testDataset,
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 for AdaBoost prediction with the fast method */
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 */
predictionResult = algorithm.compute();
}
private static void printResults() {
NumericTable predictionResults = predictionResult.get(PredictionResultId.prediction);
Service.printClassificationResult(testGroundTruth, predictionResults, "Ground truth", "Classification results",
"AdaBoost classification results (first 20 observations):", 20);
System.out.println("");
}
}

For more complete information about compiler optimizations, see our Optimization Notice.