/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.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.learner.meta;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.tools.Tools;
/**
* This model is created by the {@link CostBasedThresholdLearner}.
*
* @author Ingo Mierswa
*/
public class ThresholdModel extends PredictionModel implements DelegationModel {
private static final long serialVersionUID = -4224958349396815500L;
private double[] thresholds;
private Model innerModel;
public ThresholdModel(ExampleSet exampleSet, Model innerModel, double[] thresholds) {
super(exampleSet, null, null);
this.innerModel = innerModel;
this.thresholds = thresholds;
}
@Override
public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
exampleSet = innerModel.apply(exampleSet);
for (Example example : exampleSet) {
int predictionIndex = (int) example.getPredictedLabel();
String className = getLabel().getMapping().mapIndex(predictionIndex);
double confidence = example.getConfidence(className);
if (confidence < thresholds[predictionIndex]) {
example.setPredictedLabel(Double.NaN);
}
}
return exampleSet;
}
@Override
public String toString() {
List<String> thresholdList = new LinkedList<String>();
for (double d : thresholds) {
thresholdList.add(Tools.formatIntegerIfPossible(d));
}
return "Thresholds: " + thresholdList + Tools.getLineSeparator() + innerModel.toString();
}
@Override
public Model getBaseModel() {
return innerModel;
}
@Override
public String getShortInfo() {
List<String> thresholdList = new LinkedList<String>();
for (double d : thresholds) {
thresholdList.add(Tools.formatIntegerIfPossible(d));
}
return "Thresholds: " + thresholdList;
}
}