blob: ec481563336ede3b46cc0618f918f546c97072ac [file] [log] [blame]
package org.apache.samoa.evaluation;
/*
* #%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%
*/
import org.apache.samoa.instances.Instance;
import org.apache.samoa.moa.AbstractMOAObject;
import org.apache.samoa.moa.core.Measurement;
/**
* Regression evaluator that performs basic incremental evaluation.
*
* @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
* @version $Revision: 7 $
*/
public class BasicRegressionPerformanceEvaluator extends AbstractMOAObject
implements RegressionPerformanceEvaluator {
private static final long serialVersionUID = 1L;
protected double weightObserved;
protected double squareError;
protected double averageError;
protected double sumTarget;
protected double squareTargetError;
protected double averageTargetError;
@Override
public void reset() {
this.weightObserved = 0.0;
this.squareError = 0.0;
this.averageError = 0.0;
this.sumTarget = 0.0;
this.averageTargetError = 0.0;
this.squareTargetError = 0.0;
}
@Override
public void addResult(Instance inst, double[] prediction) {
double weight = inst.weight();
double classValue = inst.classValue();
if (weight > 0.0) {
if (prediction.length > 0) {
double meanTarget = this.weightObserved != 0 ?
this.sumTarget / this.weightObserved : 0.0;
this.squareError += (classValue - prediction[0]) * (classValue - prediction[0]);
this.averageError += Math.abs(classValue - prediction[0]);
this.squareTargetError += (classValue - meanTarget) * (classValue - meanTarget);
this.averageTargetError += Math.abs(classValue - meanTarget);
this.sumTarget += classValue;
this.weightObserved += weight;
}
}
}
@Override
public Measurement[] getPerformanceMeasurements() {
return new Measurement[] {
new Measurement("classified instances",
getTotalWeightObserved()),
new Measurement("mean absolute error",
getMeanError()),
new Measurement("root mean squared error",
getSquareError()),
new Measurement("relative mean absolute error",
getRelativeMeanError()),
new Measurement("relative root mean squared error",
getRelativeSquareError())
};
}
public double getTotalWeightObserved() {
return this.weightObserved;
}
public double getMeanError() {
return this.weightObserved > 0.0 ? this.averageError
/ this.weightObserved : 0.0;
}
public double getSquareError() {
return Math.sqrt(this.weightObserved > 0.0 ? this.squareError
/ this.weightObserved : 0.0);
}
public double getTargetMeanError() {
return this.weightObserved > 0.0 ? this.averageTargetError
/ this.weightObserved : 0.0;
}
public double getTargetSquareError() {
return Math.sqrt(this.weightObserved > 0.0 ? this.squareTargetError
/ this.weightObserved : 0.0);
}
@Override
public void getDescription(StringBuilder sb, int indent) {
Measurement.getMeasurementsDescription(getPerformanceMeasurements(),
sb, indent);
}
private double getRelativeMeanError() {
return this.averageTargetError > 0 ?
this.averageError / this.averageTargetError : 0.0;
}
private double getRelativeSquareError() {
return Math.sqrt(this.squareTargetError > 0 ?
this.squareError / this.squareTargetError : 0.0);
}
}