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

LogRegModelBuilder.java

/* file: LogRegModelBuilder.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 logistic regression model builder.
//
// The program trains the logistic regression model on a training data set with
// the normal equations method and computes regression for the test data.
*/
package com.intel.daal.examples.logistic_regression;
import java.nio.FloatBuffer;
import com.intel.daal.algorithms.logistic_regression.Model;
import com.intel.daal.algorithms.logistic_regression.ModelBuilder;
import com.intel.daal.algorithms.logistic_regression.prediction.*;
import com.intel.daal.algorithms.classifier.prediction.ModelInputId;
import com.intel.daal.algorithms.classifier.prediction.NumericTableInputId;
import com.intel.daal.algorithms.classifier.prediction.PredictionResultId;
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 LogRegModelBuilder {
/* Input data set parameters */
private static final String trainDatasetFileName = "../data/batch/logreg_trained_model.csv";
private static final String testDatasetFileName = "../data/batch/logreg_test.csv";
private static final int nFeatures = 6; /* Number of features in training and testing data sets */
private static final int nClasses = 5; /* Number of classes */
static Model model;
static NumericTable results;
static NumericTable testGroundTruth;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
buildModel();
testModel();
printResults();
context.dispose();
}
public static void buildModel() {
/* Initialize FileDataSource to retrieve the beta data from a .csv file */
FileDataSource trainDataSource = new FileDataSource(context, trainDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
/* Create Numeric Table for beta coefficients */
NumericTable betaData = new HomogenNumericTable(context, Float.class, nFeatures + 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
trainDataSource.loadDataBlock(betaData);
/* Define the size of beta */
long nBeta = betaData.getNumberOfRows()*betaData.getNumberOfColumns();
/* Initialize beta array */
FloatBuffer bufferBeta = FloatBuffer.allocate(0);
bufferBeta = betaData.getBlockOfRows(0, nClasses, bufferBeta);
/* Create model builder */
ModelBuilder modelBuilder = new ModelBuilder(context, Float.class, nFeatures, nClasses);
/* Set beta */
modelBuilder.setBeta(bufferBeta, nBeta);
model = modelBuilder.getModel();
}
private static void testModel() {
/* Initialize FileDataSource to retrieve the test data from a .csv file */
FileDataSource testDataSource = new FileDataSource(context, testDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for testing data and ground truth 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);
/* Load the data from the input file */
testDataSource.loadDataBlock(mergedData);
/* Create algorithm objects to predict values of multiple logistic regression with the default method */
PredictionBatch logisticRegressionPredict = new PredictionBatch(context, Float.class,
PredictionMethod.defaultDense, nClasses);
logisticRegressionPredict.input.set(NumericTableInputId.data, testData);
logisticRegressionPredict.input.set(ModelInputId.model, model);
logisticRegressionPredict.parameter.setResultsToCompute(PredictionResultsToComputeId.computeClassesLabels);
/* Compute prediction results */
PredictionResult predictionResult = logisticRegressionPredict.compute();
results = predictionResult.get(PredictionResultId.prediction);
}
private static void printResults() {
Service.printNumericTable("Logistic Regression coefficients of built model:", model.getBeta());
Service.printNumericTable("Logistic Regression prediction results: (first 10 rows):", results, 10);
Service.printNumericTable("Ground truth (first 10 rows):", testGroundTruth, 10);
}
}

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