/*
* Copyright (c) 2010 Pentaho Corporation. All rights reserved.
* This software was developed by Pentaho Corporation and is provided under the terms
* of the GNU Lesser General Public License, Version 2.1. You may not use
* this file except in compliance with the license. If you need a copy of the license,
* please go to http://www.gnu.org/licenses/lgpl-2.1.txt. The Original Code is Time Series
* Forecasting. The Initial Developer is Pentaho Corporation.
*
* Software distributed under the GNU Lesser Public License is distributed on an "AS IS"
* basis, WITHOUT WARRANTY OF ANY KIND, either express or implied. Please refer to
* the license for the specific language governing your rights and limitations.
*/
/*
* MAEModule.java
* Copyright (C) 2010 Pentaho Corporation
*/
package weka.classifiers.timeseries.eval;
import java.util.List;
import weka.classifiers.evaluation.NumericPrediction;
import weka.core.Utils;
/**
* An evaluation module that computes the mean absolute error
* of forecasted values.
*
* @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
* @version $Revision: 49983 $
*/
public class MAEModule extends ErrorModule {
/**
* Return the short identifying name of this evaluation module
*
* @return the short identifying name of this evaluation module
*/
public String getEvalName() {
return "MAE";
}
/**
* Return the longer (single sentence) description
* of this evaluation module
*
* @return the longer description of this module
*/
public String getDescription() {
return "Mean absolute error";
}
/**
* Return the mathematical formula that this
* evaluation module computes.
*
* @return the mathematical formula that this module
* computes.
*/
public String getDefinition() {
return "sum(abs(predicted - actual)) / N";
}
/**
* Calculate the measure that this module represents.
*
* @return the value of the measure for this module for each
* of the target(s).
* @throws Exception if the measure can't be computed for some reason.
*/
public double[] calculateMeasure() throws Exception {
double[] result = new double[m_targetFieldNames.size()];
for (int i = 0; i < result.length; i++) {
result[i] = Utils.missingValue();
}
for (int i = 0; i < m_targetFieldNames.size(); i++) {
double sumAbs = 0;
List<NumericPrediction> preds = m_predictions.get(i);
int count = 0;
for (NumericPrediction p : preds) {
if (!Utils.isMissingValue(p.error())) {
sumAbs += Math.abs(p.error());
count++;
}
}
if (m_counts[i] > 0) {
sumAbs /= m_counts[i];
}
if (count > 0) {
result[i] = sumAbs;
} else {
result[i] = Utils.missingValue();
}
}
return result;
}
}