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

SAGALogisticLossDenseBatch.java

/* file: SAGALogisticLossDenseBatch.java */
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/*
// Content:
// Java example of dense SAGA in the batch processing mode
// processing mode
*/
package com.intel.daal.examples.optimization_solvers;
import com.intel.daal.algorithms.optimization_solver.saga.*;
import com.intel.daal.algorithms.optimization_solver.iterative_solver.InputId;
import com.intel.daal.algorithms.optimization_solver.iterative_solver.Result;
import com.intel.daal.algorithms.optimization_solver.iterative_solver.ResultId;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.data_management.data.NumericTable;
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 SAGALogisticLossDenseBatch {
private static final int nFeatures = 100;
private static final double accuracyThreshold = 0.00000001;
private static final long nIterations = 100000;
private static double[] initialPoint = new double[nFeatures + 1];
/* Input data set parameters */
private static final String dataFileName = "../data/batch/XM_100.csv";
private static final String groundTruth = "../data/batch/saga_solution_100_features.csv";
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
float l1 = 0.06f;
float l2 = 0.0f;
boolean intercept = false;
/* Retrieve the data from input data sets */
FileDataSource dataSource = new FileDataSource(context, dataFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
/* Create Numeric Tables for data and values for dependent variable */
NumericTable data = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
NumericTable dataDependents = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(data);
mergedData.addNumericTable(dataDependents);
/* Retrieve the data from an input file */
dataSource.loadDataBlock(mergedData);
/* Create an Logistic Loss objective function to compute a SAGA */
com.intel.daal.algorithms.optimization_solver.logistic_loss.Batch logLossFunction =
new com.intel.daal.algorithms.optimization_solver.logistic_loss.Batch(context, Float.class,
com.intel.daal.algorithms.optimization_solver.logistic_loss.Method.defaultDense, data.getNumberOfRows());
logLossFunction.getInput().set(com.intel.daal.algorithms.optimization_solver.logistic_loss.InputId.data, data);
logLossFunction.getInput().set(com.intel.daal.algorithms.optimization_solver.logistic_loss.InputId.dependentVariables, dataDependents);
logLossFunction.parameter.setPenaltyL1(l1);
logLossFunction.parameter.setPenaltyL2(l2);
logLossFunction.parameter.setInterceptFlag(intercept);
/* Create algorithm objects to compute SAGA results */
Batch sagaAlgorithm = new Batch(context, Float.class, Method.defaultDense);
sagaAlgorithm.parameter.setFunction(logLossFunction);
sagaAlgorithm.parameter.setNIterations(nIterations);
sagaAlgorithm.parameter.setAccuracyThreshold(accuracyThreshold);
for(int i = 0; i < (nFeatures+1); i++)
initialPoint[i] = 0;
sagaAlgorithm.input.set(InputId.inputArgument, new HomogenNumericTable(context, initialPoint, 1, nFeatures + 1));
/* Compute the SAGA result for Logistic Loss objective function matrix */
Result result = sagaAlgorithm.compute();
Service.printNumericTable("Minimum:", result.get(ResultId.minimum));
Service.printNumericTable("nIterations:", result.get(ResultId.nIterations));
/* Create an Logistic Loss objective function to check SAGA result */
com.intel.daal.algorithms.optimization_solver.logistic_loss.Batch logLossFunction_check =
new com.intel.daal.algorithms.optimization_solver.logistic_loss.Batch(context, Float.class,
com.intel.daal.algorithms.optimization_solver.logistic_loss.Method.defaultDense, data.getNumberOfRows());
logLossFunction_check.getInput().set(com.intel.daal.algorithms.optimization_solver.logistic_loss.InputId.data, data);
logLossFunction_check.getInput().set(com.intel.daal.algorithms.optimization_solver.logistic_loss.InputId.dependentVariables, dataDependents);
logLossFunction_check.parameter.setPenaltyL1(l1);
logLossFunction_check.parameter.setPenaltyL2(l2);
logLossFunction_check.parameter.setInterceptFlag(intercept);
logLossFunction_check.parameter.setResultsToCompute(com.intel.daal.algorithms.optimization_solver.objective_function.ResultsToComputeId.value);
FileDataSource groundTruthDS = new FileDataSource(context, groundTruth,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
NumericTable groundTruthNT = new HomogenNumericTable(context, Float.class, 1, 0, NumericTable.AllocationFlag.DoNotAllocate);
groundTruthDS.loadDataBlock(groundTruthNT);
logLossFunction_check.getInput().set(com.intel.daal.algorithms.optimization_solver.logistic_loss.InputId.argument, groundTruthNT);
logLossFunction_check.compute();
Service.printNumericTable("groundTruth:", logLossFunction_check.getResult().get(com.intel.daal.algorithms.optimization_solver.objective_function.ResultId.valueIdx));
logLossFunction_check.getInput().set(com.intel.daal.algorithms.optimization_solver.logistic_loss.InputId.argument, result.get(ResultId.minimum));
logLossFunction_check.compute();
Service.printNumericTable("value DAAL:", logLossFunction_check.getResult().get(com.intel.daal.algorithms.optimization_solver.objective_function.ResultId.valueIdx));
context.dispose();
}
}

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