Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

LinRegMetricsDenseBatch.java

/* file: LinRegMetricsDenseBatch.java */
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/*
// Content:
// Java example of multiple linear regression in the batch processing mode.
//
// The program trains the multiple linear regression model on a training
// data set with the normal equations method and computes regression for
// the test data.
*/
package com.intel.daal.examples.quality_metrics;
import java.nio.DoubleBuffer;
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;
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.algorithms.linear_regression.quality_metric.*;
import com.intel.daal.algorithms.linear_regression.quality_metric_set.*;
import com.intel.daal.data_management.data.DataCollection;
class LinRegMetricsDenseBatch {
/* Input data set parameters */
private static final String trainDatasetFileName = "../data/batch/linear_regression_train.csv";
private static final int nFeatures = 10;
private static final int nDependentVariables = 2;
private static final int iBeta1 = 2;
private static final int iBeta2 = 10;
static Model model;
static NumericTable trainData;
static NumericTable expectedResponses;
static NumericTable predictedResponses;
static NumericTable predictedReducedModelResponses;
static ResultCollection qualityMetricSetResult;
static double[] savedBetas;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* 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 */
trainData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
expectedResponses = new HomogenNumericTable(context, Float.class, nDependentVariables, 0,
NumericTable.AllocationFlag.DoNotAllocate);
MergedNumericTable mergedData = new MergedNumericTable(context);
mergedData.addNumericTable(trainData);
mergedData.addNumericTable(expectedResponses);
/* Retrieve the data from an input file */
trainDataSource.loadDataBlock(mergedData);
for(int i = 0; i < 2; ++i)
{
if(i == 0)
{
/* Create an algorithm object to train the multiple linear regression model with normal equation method */
System.out.println("Train model with normal equation algorithm.");
/* Create an algorithm object to train the multiple linear regression model with the normal equations method */
TrainingBatch linearRegressionTrain = new TrainingBatch(context, Float.class, TrainingMethod.normEqDense);
linearRegressionTrain.input.set(TrainingInputId.data, trainData);
linearRegressionTrain.input.set(TrainingInputId.dependentVariable, expectedResponses);
/* Build the multiple linear regression model */
TrainingResult trainingResult = linearRegressionTrain.compute();
model = trainingResult.get(TrainingResultId.model);
}
else
{
/* Create an algorithm object to train the multiple linear regression model with QR method */
System.out.println("Train model with QR algorithm.");
/* Create an algorithm object to train the multiple linear regression model with the normal equations method */
TrainingBatch linearRegressionTrain = new TrainingBatch(context, Float.class, TrainingMethod.qrDense);
linearRegressionTrain.input.set(TrainingInputId.data, trainData);
linearRegressionTrain.input.set(TrainingInputId.dependentVariable, expectedResponses);
/* Build the multiple linear regression model */
TrainingResult trainingResult = linearRegressionTrain.compute();
model = trainingResult.get(TrainingResultId.model);
}
testModelQuality();
printResults();
}
context.dispose();
}
private static NumericTable predictResults() {
/* Create algorithm objects to predict values of multiple linear regression with the default method */
PredictionBatch linearRegressionPredict = new PredictionBatch(context, Float.class, PredictionMethod.defaultDense);
linearRegressionPredict.input.set(PredictionInputId.data, trainData);
linearRegressionPredict.input.set(PredictionInputId.model, model);
/* Compute prediction results */
PredictionResult predictionResult = linearRegressionPredict.compute();
return predictionResult.get(PredictionResultId.prediction);
}
private static void reduceModel() {
final int nBeta = (int)model.getNumberOfBetas();
savedBetas = new double[nBeta * nDependentVariables];
/* Read a block of rows */
DoubleBuffer betas = DoubleBuffer.allocate(nBeta * nDependentVariables);
betas = model.getBeta().getBlockOfRows(0, nDependentVariables, betas);
savedBetas[iBeta1] = betas.get(iBeta1);
savedBetas[iBeta2] = betas.get(iBeta2);
savedBetas[iBeta1 + nBeta] = betas.get(iBeta1 + nBeta);
savedBetas[iBeta2 + nBeta] = betas.get(iBeta2 + nBeta);
betas.put(iBeta1, 0);
betas.put(iBeta2, 0);
betas.put(iBeta1 + nBeta, 0);
betas.put(iBeta2 + nBeta, 0);
model.getBeta().releaseBlockOfRows(0, nDependentVariables, betas);
}
private static void restoreModel() {
final int nBeta = (int)model.getNumberOfBetas();
/* Read a block of rows */
DoubleBuffer betas = DoubleBuffer.allocate(nBeta * nDependentVariables);
betas = model.getBeta().getBlockOfRows(0, nDependentVariables, betas);
betas.put(iBeta1, savedBetas[iBeta1]);
betas.put(iBeta2, savedBetas[iBeta2]);
betas.put(iBeta1 + nBeta, savedBetas[iBeta1 + nBeta]);
betas.put(iBeta2 + nBeta, savedBetas[iBeta2 + nBeta]);
model.getBeta().releaseBlockOfRows(0, nDependentVariables, betas);
}
private static void testModelQuality() {
/* Compute prediction results */
predictedResponses = predictResults();
/* Predict results with the reduced model */
reduceModel();
predictedReducedModelResponses = predictResults();
restoreModel();
/* Create a quality metric set object to compute quality metrics of the linear regression algorithm */
final long nBeta = model.getNumberOfBetas();
final long nBetaReducedModel = nBeta - 2;
QualityMetricSetBatch qms = new QualityMetricSetBatch(context, nBeta, nBetaReducedModel);
SingleBetaInput singleBetaInput = (SingleBetaInput)qms.getInputDataCollection().getInput(QualityMetricId.singleBeta);
singleBetaInput.set(SingleBetaModelInputId.model, model);
singleBetaInput.set(SingleBetaDataInputId.expectedResponses, expectedResponses);
singleBetaInput.set(SingleBetaDataInputId.predictedResponses, predictedResponses);
GroupOfBetasInput groupOfBetasInput = (GroupOfBetasInput)qms.getInputDataCollection().getInput(QualityMetricId.groupOfBetas);
groupOfBetasInput.set(GroupOfBetasInputId.expectedResponses, expectedResponses);
groupOfBetasInput.set(GroupOfBetasInputId.predictedResponses, predictedResponses);
groupOfBetasInput.set(GroupOfBetasInputId.predictedReducedModelResponses, predictedReducedModelResponses);
/* Compute quality metrics */
qualityMetricSetResult = qms.compute();
}
private static void printResults() {
NumericTable beta = model.getBeta();
Service.printNumericTable("Linear Regression coefficients:", beta);
Service.printNumericTable("Expected responses (first 20 rows):", expectedResponses, 20);
Service.printNumericTable("Predicted responses (first 20 rows):", predictedResponses, 20);
Service.printNumericTable("Responses predicted with reduced model (first 20 rows):", predictedReducedModelResponses, 20);
/* Print the quality metrics for a single beta */
System.out.println("Quality metrics for a single beta");
SingleBetaResult singleBetaResult = (SingleBetaResult)qualityMetricSetResult.getResult(QualityMetricId.singleBeta);
Service.printNumericTable("Root means square errors for each response (dependent variable):", singleBetaResult.get(SingleBetaResultId.rms), 20);
Service.printNumericTable("Variance for each response (dependent variable):", singleBetaResult.get(SingleBetaResultId.variance), 20);
Service.printNumericTable("Z-score statistics:", singleBetaResult.get(SingleBetaResultId.zScore), 20);
Service.printNumericTable("Confidence intervals for each beta coefficient:", singleBetaResult.get(SingleBetaResultId.confidenceIntervals), 20);
Service.printNumericTable("Inverse(Xt * X) matrix:", singleBetaResult.get(SingleBetaResultId.inverseOfXtX), 20);
DataCollection coll = singleBetaResult.get(SingleBetaResultDataCollectionId.betaCovariances);
for (int i = 0; i < coll.size(); i++) {
NumericTable tbl = (NumericTable)coll.get(i);
Service.printNumericTable("Variance-covariance matrix for betas of " + i + "-th response\n", tbl, 20);
}
/* Print quality metrics for a group of betas */
System.out.println("Quality metrics for a group of betas");
GroupOfBetasResult groupOfBetasResult = (GroupOfBetasResult)qualityMetricSetResult.getResult(QualityMetricId.groupOfBetas);
Service.printNumericTable("Means of expected responses for each dependent variable:", groupOfBetasResult.get(GroupOfBetasResultId.expectedMeans), 20);
Service.printNumericTable("Variance of expected responses for each dependent variable:", groupOfBetasResult.get(GroupOfBetasResultId.expectedVariance), 20);
Service.printNumericTable("Regression sum of squares of expected responses:", groupOfBetasResult.get(GroupOfBetasResultId.regSS), 20);
Service.printNumericTable("Sum of squares of residuals for each dependent variable:", groupOfBetasResult.get(GroupOfBetasResultId.resSS), 20);
Service.printNumericTable("Total sum of squares for each dependent variable:", groupOfBetasResult.get(GroupOfBetasResultId.tSS), 20);
Service.printNumericTable("Determination coefficient for each dependent variable:", groupOfBetasResult.get(GroupOfBetasResultId.determinationCoeff), 20);
Service.printNumericTable("F-statistics for each dependent variable:", groupOfBetasResult.get(GroupOfBetasResultId.fStatistics), 20);
}
}

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