samoa-api/src/main/java/org/apache/samoa/learners/classifiers/ensemble/BaggingDistributorProcessor.java

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.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;

/**
 * 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;
  }

}