Java tutorial
/* * * JAQPOT Quattro * * JAQPOT Quattro and the components shipped with it, in particular: * (i) JaqpotCoreServices * (ii) JaqpotAlgorithmServices * (iii) JaqpotDB * (iv) JaqpotDomain * (v) JaqpotEAR * are licensed by GPL v3 as specified hereafter. Additional components may ship * with some other licence as will be specified therein. * * Copyright (C) 2014-2015 KinkyDesign (Charalampos Chomenidis, Pantelis Sopasakis) * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see <http://www.gnu.org/licenses/>. * * Source code: * The source code of JAQPOT Quattro is available on github at: * https://github.com/KinkyDesign/JaqpotQuattro * All source files of JAQPOT Quattro that are stored on github are licensed * with the aforementioned licence. */ package org.jaqpot.algorithm.resource; import java.io.ByteArrayInputStream; import java.io.ByteArrayOutputStream; import java.io.ObjectInput; import java.io.ObjectInputStream; import java.io.ObjectOutput; import java.io.ObjectOutputStream; import java.util.ArrayList; import java.util.Base64; import java.util.HashMap; import java.util.LinkedHashMap; import java.util.List; import java.util.Map; import java.util.logging.Level; import java.util.logging.Logger; import java.util.stream.Collectors; import javax.ws.rs.Consumes; import javax.ws.rs.POST; import javax.ws.rs.Path; import javax.ws.rs.Produces; import javax.ws.rs.core.MediaType; import javax.ws.rs.core.Response; import org.apache.commons.math3.stat.StatUtils; import org.jaqpot.algorithm.model.ScalingModel; import org.jaqpot.core.model.dto.dataset.DataEntry; import org.jaqpot.core.model.dto.jpdi.PredictionRequest; import org.jaqpot.core.model.dto.jpdi.PredictionResponse; import org.jaqpot.core.model.dto.jpdi.TrainingRequest; import org.jaqpot.core.model.dto.jpdi.TrainingResponse; import org.jaqpot.core.model.factory.ErrorReportFactory; /** * * @author Charalampos Chomenidis * @author Pantelis Sopasakis */ @Path("std") @Consumes(MediaType.APPLICATION_JSON) @Produces(MediaType.APPLICATION_JSON) public class Standarization { private static final Logger LOG = Logger.getLogger(Standarization.class.getName()); @POST @Path("training") public Response training(TrainingRequest request) { try { if (request.getDataset().getDataEntry().isEmpty() || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) { return Response.status(Response.Status.BAD_REQUEST).entity( ErrorReportFactory.badRequest("Dataset is empty", "Cannot train model on empty dataset")) .build(); } List<String> features = request.getDataset().getDataEntry().stream().findFirst().get().getValues() .keySet().stream().filter(feature -> !feature.equals(request.getPredictionFeature())) .collect(Collectors.toList()); LinkedHashMap<String, Double> maxValues = new LinkedHashMap<>(); LinkedHashMap<String, Double> minValues = new LinkedHashMap<>(); features.stream().forEach(feature -> { List<Double> values = request.getDataset().getDataEntry().stream().map(dataEntry -> { return Double.parseDouble(dataEntry.getValues().get(feature).toString()); }).collect(Collectors.toList()); double[] doubleValues = values.stream().mapToDouble(Double::doubleValue).toArray(); Double mean = StatUtils.mean(doubleValues); Double stddev = Math.sqrt(StatUtils.variance(doubleValues)); maxValues.put(feature, stddev); minValues.put(feature, mean); }); ScalingModel model = new ScalingModel(); model.setMaxValues(maxValues); model.setMinValues(minValues); TrainingResponse response = new TrainingResponse(); ByteArrayOutputStream baos = new ByteArrayOutputStream(); ObjectOutput out = new ObjectOutputStream(baos); out.writeObject(model); String base64Model = Base64.getEncoder().encodeToString(baos.toByteArray()); response.setRawModel(base64Model); response.setIndependentFeatures(features); response.setPredictedFeatures(features.stream().map(feature -> { return "Standarized " + feature; }).collect(Collectors.toList())); return Response.ok(response).build(); } catch (Exception ex) { LOG.log(Level.SEVERE, null, ex); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build(); } } @POST @Path("prediction") public Response prediction(PredictionRequest request) { try { if (request.getDataset().getDataEntry().isEmpty() || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) { return Response .status(Response.Status.BAD_REQUEST).entity(ErrorReportFactory .badRequest("Dataset is empty", "Cannot make predictions on empty dataset")) .build(); } List<String> features = request.getDataset().getDataEntry().stream().findFirst().get().getValues() .keySet().stream().collect(Collectors.toList()); String base64Model = (String) request.getRawModel(); byte[] modelBytes = Base64.getDecoder().decode(base64Model); ByteArrayInputStream bais = new ByteArrayInputStream(modelBytes); ObjectInput in = new ObjectInputStream(bais); ScalingModel model = (ScalingModel) in.readObject(); in.close(); bais.close(); List<LinkedHashMap<String, Object>> predictions = new ArrayList<>(); for (DataEntry dataEntry : request.getDataset().getDataEntry()) { LinkedHashMap<String, Object> data = new LinkedHashMap<>(); for (String feature : features) { Double stdev = model.getMaxValues().get(feature); Double mean = model.getMinValues().get(feature); Double value = Double.parseDouble(dataEntry.getValues().get(feature).toString()); if (stdev != null && stdev != 0.0 && mean != null) { value = (value - mean) / stdev; } else { value = 1.0; } data.put("Standarized " + feature, value); } predictions.add(data); } PredictionResponse response = new PredictionResponse(); response.setPredictions(predictions); return Response.ok(response).build(); } catch (Exception ex) { LOG.log(Level.SEVERE, null, ex); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build(); } } }