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

LinRegQRDenseDistr.java

/* file: LinRegQRDenseDistr.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 multiple linear regression in the distributed processing
// mode.
//
// The program trains the multiple linear regression model on a training
// data set with a QR decomposition-based method and computes regression
// for the test data.
*/
package com.intel.daal.examples.linear_regression;
import com.intel.daal.algorithms.linear_regression.Model;
import com.intel.daal.algorithms.linear_regression.prediction.*;
import com.intel.daal.algorithms.linear_regression.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 LinRegQRDenseDistr {
/* Input data set parameters */
private static final String[] trainDatasetFileNames = {
"../data/distributed/linear_regression_train_1.csv", "../data/distributed/linear_regression_train_2.csv",
"../data/distributed/linear_regression_train_3.csv", "../data/distributed/linear_regression_train_4.csv" };
private static final String testDatasetFileName = "../data/distributed/linear_regression_test.csv";
private static final int nFeatures = 10; /* Number of features in training and testing data sets */
private static final int nDependentVariables = 2; /* Number of dependent variables that correspond to each observation */
private static final int nNodes = 4;
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() {
/* Build partial multiple linear regression models on local nodes */
PartialResult[] pres = new PartialResult[nNodes];
for (int node = 0; node < nNodes; node++) {
/* Initialize FileDataSource to retrieve the input data from a .csv file */
FileDataSource trainDataSource = new FileDataSource(context, trainDatasetFileNames[node],
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, nDependentVariables, 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 linear regression model with a QR decomposition-based method */
TrainingDistributedStep1Local linearRegressionTraining = new TrainingDistributedStep1Local(context,
Float.class, TrainingMethod.qrDense);
/* Set the input data */
linearRegressionTraining.input.set(TrainingInputId.data, trainData);
linearRegressionTraining.input.set(TrainingInputId.dependentVariable, trainDependentVariables);
/* Build a partial multiple linear regression model */
pres[node] = linearRegressionTraining.compute();
}
/* Build the final multiple linear regression model on the master node*/
/* Create an algorithm object to train the multiple linear regression model with a QR decomposition-based method */
TrainingDistributedStep2Master linearRegressionTraining = new TrainingDistributedStep2Master(context,
Float.class, TrainingMethod.qrDense);
/* Set partial multiple linear regression models built on local nodes */
for (int node = 0; node < nNodes; node++) {
linearRegressionTraining.input.add(MasterInputId.partialModels, pres[node]);
}
/* Build and retrieve the final multiple linear regression model */
linearRegressionTraining.compute();
TrainingResult trainingResult = linearRegressionTraining.finalizeCompute();
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, nDependentVariables, 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 linear regression with the default method */
PredictionBatch linearRegressionPredict = new PredictionBatch(context, Float.class,
PredictionMethod.defaultDense);
/* Provide the input data */
linearRegressionPredict.input.set(PredictionInputId.data, testData);
linearRegressionPredict.input.set(PredictionInputId.model, model);
/* Compute and retrieve the prediction results */
PredictionResult predictionResult = linearRegressionPredict.compute();
results = predictionResult.get(PredictionResultId.prediction);
}
private static void printResults() {
NumericTable beta = model.getBeta();
NumericTable expected = testDependentVariables;
Service.printNumericTable("Linear Regression coefficients:", beta);
Service.printNumericTable("Linear Regression prediction results: (first 10 rows):", results, 10);
Service.printNumericTable("Ground truth (first 10 rows):", expected, 10);
}
}

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