/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* ThresholdPrediction.java
* Copyright (C) 2009 Aristotle University of Thessaloniki, Thessaloniki, Greece
*/
package mulan.classifier.meta.thresholding;
import java.util.ArrayList;
import java.util.Collections;
import java.util.logging.Level;
import java.util.logging.Logger;
import mulan.classifier.MultiLabelLearner;
import mulan.classifier.MultiLabelOutput;
import mulan.data.DataUtils;
import mulan.data.MultiLabelInstances;
import weka.classifiers.Classifier;
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
/**
*
* @author Marios Ioannou
* @author George Sakkas
* @author Grigorios Tsoumakas
* @version 2010.12.14
*/
public class ThresholdPrediction extends Meta {
/**
* Constructor that initializes the learner
*
* @param baseLearner the underlying multi-label learner
* @param classifier the binary classification
* @param metaDataChoice the type of meta-data
* @param folds the number of internal cv folds
*/
public ThresholdPrediction(MultiLabelLearner baseLearner, Classifier classifier, String metaDataChoice, int folds) {
super(baseLearner, classifier, metaDataChoice);
try {
foldLearner = baseLearner.makeCopy();
} catch (Exception ex) {
Logger.getLogger(ThresholdPrediction.class.getName()).log(Level.SEVERE, null, ex);
}
kFoldsCV = folds;
}
@Override
protected MultiLabelOutput makePredictionInternal(Instance instance) throws Exception {
boolean[] predictedLabels = new boolean[numLabels];
Instance modifiedIns = modifiedInstanceX(instance, metaDatasetChoice);
modifiedIns.insertAttributeAt(modifiedIns.numAttributes());
// set dataset to instance
modifiedIns.setDataset(classifierInstances);
double bipartition_key = classifier.classifyInstance(modifiedIns);
MultiLabelOutput mlo = baseLearner.makePrediction(instance);
double[] arrayOfScores = new double[numLabels];
arrayOfScores = mlo.getConfidences();
for (int i = 0; i < numLabels; i++) {
if (arrayOfScores[i] >= bipartition_key) {
predictedLabels[i] = true;
} else {
predictedLabels[i] = false;
}
}
MultiLabelOutput final_mlo = new MultiLabelOutput(predictedLabels, mlo.getConfidences());
return final_mlo;
}
public Instances transformData(MultiLabelInstances trainingData) throws Exception {
// initialize classifier instances
classifierInstances = prepareClassifierInstances(trainingData);
classifierInstances.insertAttributeAt(new Attribute("Threshold"), classifierInstances.numAttributes());
classifierInstances.setClassIndex(classifierInstances.numAttributes() - 1);
for (int k = 0; k < kFoldsCV; k++) {
//Split data to train and test sets
MultiLabelLearner tempLearner;
MultiLabelInstances mlTest;
if (kFoldsCV == 1) {
tempLearner = baseLearner;
mlTest = trainingData;
} else {
Instances train = trainingData.getDataSet().trainCV(kFoldsCV, k);
Instances test = trainingData.getDataSet().testCV(kFoldsCV, k);
MultiLabelInstances mlTrain = new MultiLabelInstances(train, trainingData.getLabelsMetaData());
mlTest = new MultiLabelInstances(test, trainingData.getLabelsMetaData());
tempLearner = foldLearner.makeCopy();
tempLearner.build(mlTrain);
}
// copy features and labels, set metalabels
for (int instanceIndex = 0; instanceIndex < mlTest.getDataSet().numInstances(); instanceIndex++) {
Instance instance = mlTest.getDataSet().instance(instanceIndex);
// initialize new class values
double[] newValues = new double[classifierInstances.numAttributes()];
// create features
valuesX(tempLearner, instance, newValues, metaDatasetChoice);
boolean[] trueLabels = new boolean[numLabels];
// Indices of labels to take the truelabels for test instances
for (int i = 0; i < numLabels; i++) {
int labelIndice = labelIndices[i];
String classValue = mlTest.getDataSet().attribute(labelIndice).value((int) mlTest.getDataSet().instance(instanceIndex).value(labelIndice));
trueLabels[i] = classValue.equals("1");
}
MultiLabelOutput mlo = baseLearner.makePrediction(mlTest.getDataSet().instance(instanceIndex));
double[] arrayOfScores = mlo.getConfidences();
ArrayList<Double> list = new ArrayList();
for (int i = 0; i < numLabels; i++) {
list.add(arrayOfScores[i]);
}
Collections.sort(list);
double tempThresshold = 0, threshold = 0;
double prev = list.get(0);
int t = numLabels, tempT = 0;
for (Double x : list) {
tempThresshold = (x + prev) / 2;
for (int i = 0; i < numLabels; i++) {
if ((trueLabels[i] == true) && (arrayOfScores[i] <= tempThresshold)) {
tempT++;
} else if ((trueLabels[i] == false) && (arrayOfScores[i] >= tempThresshold)) {
tempT++;
}
}
if (tempT < t) {
t = tempT;
threshold = tempThresshold;
}
tempT = 0;
prev = x;
}
newValues[newValues.length - 1] = threshold;
// add the new instance to classifierInstances
Instance newInstance = DataUtils.createInstance(mlTest.getDataSet().instance(instanceIndex), mlTest.getDataSet().instance(instanceIndex).weight(), newValues);
classifierInstances.add(newInstance);
}
}
return classifierInstances;
}
@Override
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result = new TechnicalInformation(Type.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Elisseeff, Andre and Weston, Jason");
result.setValue(Field.TITLE, "A kernel method for multi-labelled classification");
result.setValue(Field.BOOKTITLE, "Proceedings of NIPS 14");
result.setValue(Field.YEAR, "2002");
return result;
}
}