/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.postprocessing;
import java.util.Iterator;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
/**
* This operator applies the given threshold to an example set and maps a soft
* prediction to crisp values. If the confidence for the second class (usually
* positive for RapidMiner) is greater than the given threshold the prediction is set
* to this class.
*
* @author Ingo Mierswa, Martin Scholz
* @version $Id: ThresholdApplier.java,v 1.4 2008/07/07 07:06:46 ingomierswa Exp $
*/
public class ThresholdApplier extends Operator {
public ThresholdApplier(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
ExampleSet exampleSet = getInput(ExampleSet.class);
Threshold threshold = getInput(Threshold.class);
Attribute predictedLabel = exampleSet.getAttributes().getPredictedLabel();
if (predictedLabel == null)
throw new UserError(this, 107);
int zeroIndex = predictedLabel.getMapping().mapString(threshold.getZeroClass());
int oneIndex = predictedLabel.getMapping().mapString(threshold.getOneClass());
Iterator<Example> reader = exampleSet.iterator();
while (reader.hasNext()) {
Example example = reader.next();
double oneClassConfidence = example.getConfidence(threshold.getOneClass());
double crispPrediction = oneClassConfidence > threshold.getThreshold() ? oneIndex : zeroIndex;
example.setValue(predictedLabel, crispPrediction);
}
return new IOObject[] { exampleSet };
}
public Class<?>[] getInputClasses() {
return new Class[] { ExampleSet.class, Threshold.class };
}
public Class<?>[] getOutputClasses() {
return new Class[] { ExampleSet.class };
}
}