| package org.apache.samoa.learners.classifiers.ensemble; |
| |
| /* |
| * #%L |
| * SAMOA |
| * %% |
| * Copyright (C) 2014 - 2015 Apache Software Foundation |
| * %% |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| * #L% |
| */ |
| |
| /** |
| * License |
| */ |
| |
| import java.util.Arrays; |
| import java.util.Random; |
| |
| import org.apache.samoa.core.ContentEvent; |
| import org.apache.samoa.core.Processor; |
| import org.apache.samoa.instances.Instance; |
| import org.apache.samoa.learners.InstanceContentEvent; |
| import org.apache.samoa.moa.core.MiscUtils; |
| import org.apache.samoa.topology.Stream; |
| |
| import com.google.common.base.Preconditions; |
| |
| /** |
| * The Class BaggingDistributorPE. |
| */ |
| public class BaggingDistributorProcessor implements Processor { |
| |
| private static final long serialVersionUID = -1550901409625192730L; |
| |
| /** The ensemble size. */ |
| private int ensembleSize; |
| |
| /** The stream ensemble. */ |
| private Stream[] ensembleStreams; |
| |
| /** Ramdom number generator. */ |
| protected Random random = new Random(); //TODO make random seed configurable |
| |
| /** |
| * On event. |
| * |
| * @param event |
| * the event |
| * @return true, if successful |
| */ |
| public boolean process(ContentEvent event) { |
| Preconditions.checkState(ensembleSize == ensembleStreams.length, String.format( |
| "Ensemble size ({}) and number of enseble streams ({}) do not match.", ensembleSize, ensembleStreams.length)); |
| InstanceContentEvent inEvent = (InstanceContentEvent) event; |
| |
| if (inEvent.getInstanceIndex() < 0) { |
| // end learning |
| for (Stream stream : ensembleStreams) |
| stream.put(event); |
| return false; |
| } |
| |
| if (inEvent.isTesting()) { |
| Instance testInstance = inEvent.getInstance(); |
| for (int i = 0; i < ensembleSize; i++) { |
| Instance instanceCopy = testInstance.copy(); |
| InstanceContentEvent instanceContentEvent = new InstanceContentEvent(inEvent.getInstanceIndex(), instanceCopy, |
| false, true); |
| instanceContentEvent.setClassifierIndex(i); //TODO probably not needed anymore |
| instanceContentEvent.setEvaluationIndex(inEvent.getEvaluationIndex()); //TODO probably not needed anymore |
| ensembleStreams[i].put(instanceContentEvent); |
| } |
| } |
| |
| // estimate model parameters using the training data |
| if (inEvent.isTraining()) { |
| train(inEvent); |
| } |
| return true; |
| } |
| |
| /** |
| * Train. |
| * |
| * @param inEvent |
| * the in event |
| */ |
| protected void train(InstanceContentEvent inEvent) { |
| Instance trainInstance = inEvent.getInstance(); |
| for (int i = 0; i < ensembleSize; i++) { |
| int k = MiscUtils.poisson(1.0, this.random); |
| if (k > 0) { |
| Instance weightedInstance = trainInstance.copy(); |
| weightedInstance.setWeight(trainInstance.weight() * k); |
| InstanceContentEvent instanceContentEvent = new InstanceContentEvent(inEvent.getInstanceIndex(), |
| weightedInstance, true, false); |
| instanceContentEvent.setClassifierIndex(i); |
| instanceContentEvent.setEvaluationIndex(inEvent.getEvaluationIndex()); |
| ensembleStreams[i].put(instanceContentEvent); |
| } |
| } |
| } |
| |
| @Override |
| public void onCreate(int id) { |
| // do nothing |
| } |
| |
| public Stream[] getOutputStreams() { |
| return ensembleStreams; |
| } |
| |
| public void setOutputStreams(Stream[] ensembleStreams) { |
| this.ensembleStreams = ensembleStreams; |
| } |
| |
| public int getEnsembleSize() { |
| return ensembleSize; |
| } |
| |
| public void setEnsembleSize(int ensembleSize) { |
| this.ensembleSize = ensembleSize; |
| } |
| |
| @Override |
| public Processor newProcessor(Processor sourceProcessor) { |
| BaggingDistributorProcessor newProcessor = new BaggingDistributorProcessor(); |
| BaggingDistributorProcessor originProcessor = (BaggingDistributorProcessor) sourceProcessor; |
| if (originProcessor.getOutputStreams() != null) { |
| newProcessor.setOutputStreams(Arrays.copyOf(originProcessor.getOutputStreams(), |
| originProcessor.getOutputStreams().length)); |
| } |
| newProcessor.setEnsembleSize(originProcessor.getEnsembleSize()); |
| /* |
| * if (originProcessor.getLearningCurve() != null){ |
| * newProcessor.setLearningCurve((LearningCurve) |
| * originProcessor.getLearningCurve().copy()); } |
| */ |
| return newProcessor; |
| } |
| } |