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

LogRegBinaryDenseBatch.java

/* file: LogRegBinaryDenseBatch.java */
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/*
// Content:
// Java example of logistic regression in the batch processing mode.
//
// 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 com.intel.daal.algorithms.logistic_regression.Model;
import com.intel.daal.algorithms.logistic_regression.prediction.*;
import com.intel.daal.algorithms.logistic_regression.training.*;
import com.intel.daal.algorithms.classifier.training.InputId;
import com.intel.daal.algorithms.classifier.training.TrainingResultId;
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 LogRegBinaryDenseBatch {
/* Input data set parameters */
private static final String trainDatasetFileName = "../data/batch/binary_cls_train.csv";
private static final String testDatasetFileName = "../data/batch/binary_cls_test.csv";
private static final int nFeatures = 20; /* Number of features in training and testing data sets */
private static final int nClasses = 2; /* Number of classes */
static Model model;
static NumericTable results;
static NumericTable testDependentVariables;
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() {
/* 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 trainDependentVariables = new HomogenNumericTable(context, Float.class, 1, 0,
NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(trainData);
mergedData.addNumericTable(trainDependentVariables);
/* Retrieve the data from an input file */
trainDataSource.loadDataBlock(mergedData);
/* Create an algorithm object to train the multiple logistic regression model with the normal equations method */
TrainingBatch logisticRegressionTrain = new TrainingBatch(context, Float.class, TrainingMethod.defaultDense, nClasses);
logisticRegressionTrain.input.set(InputId.data, trainData);
logisticRegressionTrain.input.set(InputId.labels, trainDependentVariables);
/* Build the multiple logistic regression model */
TrainingResult trainingResult = logisticRegressionTrain.compute();
model = trainingResult.get(TrainingResultId.model);
}
private static void testModel() {
/* Initialize FileDataSource to retrieve the input data from a .csv file */
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);
testDependentVariables = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(testData);
mergedData.addNumericTable(testDependentVariables);
/* Retrieve the data from an 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);
/* Compute prediction results */
PredictionResult predictionResult = logisticRegressionPredict.compute();
results = predictionResult.get(PredictionResultId.prediction);
}
private static void printResults() {
NumericTable beta = model.getBeta();
NumericTable expected = testDependentVariables;
Service.printNumericTable("Logistic Regression coefficients: ", beta);
Service.printNumericTable("Logistic Regression prediction results: (first 10 rows):", results, 10);
Service.printNumericTable("Ground truth (first 10 rows):", expected, 10);
}
}

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