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 {
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;
private static final int nClasses = 2;
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() {
FileDataSource trainDataSource = new FileDataSource(context, trainDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
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);
trainDataSource.loadDataBlock(mergedData);
TrainingBatch logisticRegressionTrain = new TrainingBatch(context, Float.class, TrainingMethod.defaultDense, nClasses);
logisticRegressionTrain.input.set(InputId.data, trainData);
logisticRegressionTrain.input.set(InputId.labels, trainDependentVariables);
TrainingResult trainingResult = logisticRegressionTrain.compute();
model = trainingResult.get(TrainingResultId.model);
}
private static void testModel() {
FileDataSource testDataSource = new FileDataSource(context, testDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);
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);
testDataSource.loadDataBlock(mergedData);
PredictionBatch logisticRegressionPredict = new PredictionBatch(context, Float.class,
PredictionMethod.defaultDense, nClasses);
logisticRegressionPredict.input.set(NumericTableInputId.data, testData);
logisticRegressionPredict.input.set(ModelInputId.model, model);
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);
}
}