samoa-api/src/main/java/com/yahoo/labs/samoa/learners/classifiers/rules/common/TargetMean.java

/*
 *    TargetMean.java
 *    Copyright (C) 2014 University of Porto, Portugal
 *    @author  J. Duarte, A. Bifet, J. Gama
 *
 *    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.
 *    
 *    
 */
package com.yahoo.labs.samoa.learners.classifiers.rules.common;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2013 - 2014 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%
 */
/**
 * Prediction scheme using TargetMean:
 * TargetMean - Returns the mean of the target variable of the training instances
 * 
 * @author Joao Duarte 
 * 
 *  */

import com.esotericsoftware.kryo.Kryo;
import com.esotericsoftware.kryo.Serializer;
import com.esotericsoftware.kryo.io.Input;
import com.esotericsoftware.kryo.io.Output;
import com.github.javacliparser.FloatOption;
import com.yahoo.labs.samoa.instances.Instance;
import com.yahoo.labs.samoa.moa.classifiers.AbstractClassifier;
import com.yahoo.labs.samoa.moa.classifiers.Regressor;
import com.yahoo.labs.samoa.moa.core.Measurement;
import com.yahoo.labs.samoa.moa.core.StringUtils;

public class TargetMean extends AbstractClassifier implements Regressor {

	/**
	 * 
	 */
	protected long n;
	protected double sum;
	protected double errorSum;
	protected double nError;
	private double fadingErrorFactor;

	private static final long serialVersionUID = 7152547322803559115L;

	public FloatOption fadingErrorFactorOption = new FloatOption(
			"fadingErrorFactor", 'e', 
			"Fading error factor for the TargetMean accumulated error", 0.99, 0, 1);

	@Override
	public boolean isRandomizable() {
		return false;
	}

	@Override
	public double[] getVotesForInstance(Instance inst) {
		return getVotesForInstance();
	}
	
	public double[] getVotesForInstance() {
		double[] currentMean=new double[1];
		if (n>0)
			currentMean[0]=sum/n;
		else
			currentMean[0]=0;
		return currentMean;
	}

	@Override
	public void resetLearningImpl() {
		sum=0;
		n=0;
		errorSum=Double.MAX_VALUE;
		nError=0;
	}

	@Override
	public void trainOnInstanceImpl(Instance inst) {
		updateAccumulatedError(inst);
		++this.n;
		this.sum+=inst.classValue();
	}
	protected void updateAccumulatedError(Instance inst){
		double mean=0;
		nError=1+fadingErrorFactor*nError;
		if(n>0)
			mean=sum/n;
		errorSum=Math.abs(inst.classValue()-mean)+fadingErrorFactor*errorSum;
	}

	@Override
	protected Measurement[] getModelMeasurementsImpl() {
		return null;
	}

	@Override
	public void getModelDescription(StringBuilder out, int indent) {
		StringUtils.appendIndented(out, indent, "Current Mean: " + this.sum/this.n);
		StringUtils.appendNewline(out); 

	}
	/* JD
	 * Resets the learner but initializes with a starting point 
	 * */
	 public void reset(double currentMean, long numberOfInstances) {
		this.sum=currentMean*numberOfInstances;
		this.n=numberOfInstances;
		this.resetError();
	}

	/* JD
	 * Resets the learner but initializes with a starting point 
	 * */
	 public double getCurrentError(){
		if(this.nError>0)
			return this.errorSum/this.nError;
		else
			return Double.MAX_VALUE;
	 }

	 public TargetMean(TargetMean t) {
		 super();
		 this.n = t.n;
		 this.sum = t.sum;
		 this.errorSum = t.errorSum;
		 this.nError = t.nError;
		 this.fadingErrorFactor = t.fadingErrorFactor;
		 this.fadingErrorFactorOption = t.fadingErrorFactorOption;
	 }
	 
	 public TargetMean(TargetMeanData td) {
		 this();
		 this.n = td.n;
		 this.sum = td.sum;
		 this.errorSum = td.errorSum;
		 this.nError = td.nError;
		 this.fadingErrorFactor = td.fadingErrorFactor;
		 this.fadingErrorFactorOption.setValue(td.fadingErrorFactorOptionValue);
	 }

	 public TargetMean() {
		 super();
		 fadingErrorFactor=fadingErrorFactorOption.getValue();
	 }

	 public void resetError() {
		 this.errorSum=0;
		 this.nError=0;
	 }
	 
	 public static class TargetMeanData {
		 private long n;
		 private double sum;
		 private double errorSum;
		 private double nError;
		 private double fadingErrorFactor;
		 private double fadingErrorFactorOptionValue;
		 
		 public TargetMeanData() {
			 
		 }
		 
		 public TargetMeanData(TargetMean tm) {
			 this.n = tm.n;
			 this.sum = tm.sum;
			 this.errorSum = tm.errorSum;
			 this.nError = tm.nError;
			 this.fadingErrorFactor = tm.fadingErrorFactor;
			 if (tm.fadingErrorFactorOption != null)
				 this.fadingErrorFactorOptionValue = tm.fadingErrorFactorOption.getValue();
			 else
				 this.fadingErrorFactorOptionValue = 0.99;
		 }
		 
		 public TargetMean build() {
			 return new TargetMean(this);
		 }
	 }
	 
	 public static final class TargetMeanSerializer extends Serializer<TargetMean>{

			@Override
			public void write(Kryo kryo, Output output, TargetMean t) {
				kryo.writeObjectOrNull(output, new TargetMeanData(t), TargetMeanData.class);
			}

			@Override
			public TargetMean read(Kryo kryo, Input input, Class<TargetMean> type) {
				TargetMeanData data = kryo.readObjectOrNull(input, TargetMeanData.class);
				return data.build();
			}
		}
}