| package com.yahoo.labs.samoa.learners.classifiers.ensemble; |
| |
| /* |
| * #%L |
| * SAMOA |
| * %% |
| * Copyright (C) 2013 Yahoo! Inc. |
| * %% |
| * 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 com.yahoo.labs.samoa.core.ContentEvent; |
| import com.yahoo.labs.samoa.learners.InstanceContentEvent; |
| import com.yahoo.labs.samoa.core.Processor; |
| import com.yahoo.labs.samoa.instances.Instance; |
| import com.yahoo.labs.samoa.moa.core.MiscUtils; |
| import com.yahoo.labs.samoa.topology.Stream; |
| import java.util.Random; |
| |
| /** |
| * The Class BaggingDistributorPE. |
| */ |
| public class BaggingDistributorProcessor implements Processor{ |
| |
| /** |
| * |
| */ |
| private static final long serialVersionUID = -1550901409625192730L; |
| |
| /** The size ensemble. */ |
| private int sizeEnsemble; |
| |
| /** The training stream. */ |
| private Stream trainingStream; |
| |
| /** The prediction stream. */ |
| private Stream predictionStream; |
| |
| /** |
| * On event. |
| * |
| * @param event the event |
| * @return true, if successful |
| */ |
| public boolean process(ContentEvent event) { |
| InstanceContentEvent inEvent = (InstanceContentEvent) event; //((s4Event) event).getContentEvent(); |
| //InstanceEvent inEvent = (InstanceEvent) event; |
| |
| if (inEvent.getInstanceIndex() < 0) { |
| // End learning |
| predictionStream.put(event); |
| return false; |
| } |
| |
| |
| if (inEvent.isTesting()){ |
| Instance trainInst = inEvent.getInstance(); |
| for (int i = 0; i < sizeEnsemble; i++) { |
| Instance weightedInst = trainInst.copy(); |
| //weightedInst.setWeight(trainInst.weight() * k); |
| InstanceContentEvent instanceContentEvent = new InstanceContentEvent( |
| inEvent.getInstanceIndex(), weightedInst, false, true); |
| instanceContentEvent.setClassifierIndex(i); |
| instanceContentEvent.setEvaluationIndex(inEvent.getEvaluationIndex()); |
| predictionStream.put(instanceContentEvent); |
| } |
| } |
| |
| /* Estimate model parameters using the training data. */ |
| if (inEvent.isTraining()) { |
| train(inEvent); |
| } |
| return false; |
| } |
| |
| /** The random. */ |
| protected Random random = new Random(); |
| |
| /** |
| * Train. |
| * |
| * @param inEvent the in event |
| */ |
| protected void train(InstanceContentEvent inEvent) { |
| Instance trainInst = inEvent.getInstance(); |
| for (int i = 0; i < sizeEnsemble; i++) { |
| int k = MiscUtils.poisson(1.0, this.random); |
| if (k > 0) { |
| Instance weightedInst = trainInst.copy(); |
| weightedInst.setWeight(trainInst.weight() * k); |
| InstanceContentEvent instanceContentEvent = new InstanceContentEvent( |
| inEvent.getInstanceIndex(), weightedInst, true, false); |
| instanceContentEvent.setClassifierIndex(i); |
| instanceContentEvent.setEvaluationIndex(inEvent.getEvaluationIndex()); |
| trainingStream.put(instanceContentEvent); |
| } |
| } |
| } |
| |
| /* |
| * (non-Javadoc) |
| * |
| * @see org.apache.s4.core.ProcessingElement#onCreate() |
| */ |
| @Override |
| public void onCreate(int id) { |
| //do nothing |
| } |
| |
| |
| /** |
| * Gets the training stream. |
| * |
| * @return the training stream |
| */ |
| public Stream getTrainingStream() { |
| return trainingStream; |
| } |
| |
| /** |
| * Sets the training stream. |
| * |
| * @param trainingStream the new training stream |
| */ |
| public void setOutputStream(Stream trainingStream) { |
| this.trainingStream = trainingStream; |
| } |
| |
| /** |
| * Gets the prediction stream. |
| * |
| * @return the prediction stream |
| */ |
| public Stream getPredictionStream() { |
| return predictionStream; |
| } |
| |
| /** |
| * Sets the prediction stream. |
| * |
| * @param predictionStream the new prediction stream |
| */ |
| public void setPredictionStream(Stream predictionStream) { |
| this.predictionStream = predictionStream; |
| } |
| |
| /** |
| * Gets the size ensemble. |
| * |
| * @return the size ensemble |
| */ |
| public int getSizeEnsemble() { |
| return sizeEnsemble; |
| } |
| |
| /** |
| * Sets the size ensemble. |
| * |
| * @param sizeEnsemble the new size ensemble |
| */ |
| public void setSizeEnsemble(int sizeEnsemble) { |
| this.sizeEnsemble = sizeEnsemble; |
| } |
| |
| |
| /* (non-Javadoc) |
| * @see samoa.core.Processor#newProcessor(samoa.core.Processor) |
| */ |
| @Override |
| public Processor newProcessor(Processor sourceProcessor) { |
| BaggingDistributorProcessor newProcessor = new BaggingDistributorProcessor(); |
| BaggingDistributorProcessor originProcessor = (BaggingDistributorProcessor) sourceProcessor; |
| if (originProcessor.getPredictionStream() != null){ |
| newProcessor.setPredictionStream(originProcessor.getPredictionStream()); |
| } |
| if (originProcessor.getTrainingStream() != null){ |
| newProcessor.setOutputStream(originProcessor.getTrainingStream()); |
| } |
| newProcessor.setSizeEnsemble(originProcessor.getSizeEnsemble()); |
| /*if (originProcessor.getLearningCurve() != null){ |
| newProcessor.setLearningCurve((LearningCurve) originProcessor.getLearningCurve().copy()); |
| }*/ |
| return newProcessor; |
| } |
| |
| } |