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

ImplAlsDenseBatch.java

/* file: ImplAlsDenseBatch.java */
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/*
// Content:
// Java example of the implicit alternating least squares (ALS) algorithm in
// the batch processing mode.
//
// The program trains the implicit ALS model on a dense training data set.
*/
package com.intel.daal.examples.implicit_als;
import com.intel.daal.algorithms.implicit_als.Model;
import com.intel.daal.algorithms.implicit_als.prediction.ratings.*;
import com.intel.daal.algorithms.implicit_als.training.*;
import com.intel.daal.algorithms.implicit_als.training.init.*;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.data_management.data.NumericTable;
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 ImplAlsDenseBatch {
private static long nFactors = 2;
private static long nObservations = 46;
private static Model initialModel;
private static Model trainedModel;
private static NumericTable data;
/* Input data set parameters */
private static final String trainDatasetFileName = "../data/batch/implicit_als_dense.csv";
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
initializeModel();
trainModel();
testModel();
context.dispose();
}
private static void initializeModel() {
/* Read trainDatasetFileName from a file and create a numeric table for storing the input data */
FileDataSource dataSource = new FileDataSource(context, trainDatasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
/* Retrieve the input data */
dataSource.loadDataBlock(nObservations);
data = dataSource.getNumericTable();
/* Create an algorithm object to initialize the implicit ALS model with the default method */
InitBatch initAlgorithm = new InitBatch(context, Float.class, InitMethod.defaultDense);
initAlgorithm.parameter.setNFactors(nFactors);
/* Pass a training data set and dependent values to the algorithm */
initAlgorithm.input.set(InitInputId.data, data);
/* Initialize the implicit ALS model */
InitResult initResult = initAlgorithm.compute();
initialModel = initResult.get(InitResultId.model);
}
private static void trainModel() {
/* Create an algorithm object to train the implicit ALS model with the default method */
TrainingBatch alsTrain = new TrainingBatch(context, Float.class, TrainingMethod.defaultDense);
alsTrain.input.set(NumericTableInputId.data, data);
alsTrain.input.set(ModelInputId.inputModel, initialModel);
alsTrain.parameter.setNFactors(nFactors);
/* Build the implicit ALS model */
TrainingResult trainingResult = alsTrain.compute();
trainedModel = trainingResult.get(TrainingResultId.model);
}
private static void testModel() {
/* Create an algorithm object to predict recommendations of the implicit ALS model */
RatingsBatch algorithm = new RatingsBatch(context, Float.class, RatingsMethod.defaultDense);
algorithm.parameter.setNFactors(nFactors);
algorithm.input.set(RatingsModelInputId.model, trainedModel);
RatingsResult result = algorithm.compute();
NumericTable predictedRatings = result.get(RatingsResultId.prediction);
Service.printNumericTable("Predicted ratings:", predictedRatings);
}
}

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