source: trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaBaggingTraining2.java @ 6

package de.ugoe.cs.cpdp.training;

import java.io.PrintStream;
import java.util.HashSet;
import java.util.LinkedList;
import java.util.List;
import java.util.Set;

import org.apache.commons.collections4.list.SetUniqueList;
import org.apache.commons.io.output.NullOutputStream;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Programmatic WekaBaggingTraining
 *
 * first parameter is Trainer Name.
 * second parameter is class name
 * 
 * all subsequent parameters are configuration params (for example for trees)
 * 
 * XML Configurations for Weka Classifiers:
 * <pre>
 * {@code
 * <!-- examples -->
 * <setwisetrainer name="WekaBaggingTraining2" param="NaiveBayesBagging weka.classifiers.bayes.NaiveBayes" />
 * <setwisetrainer name="WekaBaggingTraining2" param="LogisticBagging weka.classifiers.functions.Logistic -R 1.0E-8 -M -1" />
 * }
 * </pre>
 * 
 */
public class WekaBaggingTraining2 extends WekaBaseTraining2 implements ISetWiseTrainingStrategy {

        private final TraindatasetBagging classifier = new TraindatasetBagging();
        
        @Override
        public Classifier getClassifier() {
                return classifier;
        }
        
        @Override
        public void apply(SetUniqueList<Instances> traindataSet) {
                PrintStream errStr      = System.err;
                System.setErr(new PrintStream(new NullOutputStream()));
                try {
                        classifier.buildClassifier(traindataSet);
                } catch (Exception e) {
                        throw new RuntimeException(e);
                } finally {
                        System.setErr(errStr);
                }
        }
        
        public class TraindatasetBagging extends AbstractClassifier {
                
                private static final long serialVersionUID = 1L;

                private List<Instances> trainingData = null;
                
                private List<Classifier> classifiers = null;
        
                @Override
                public double classifyInstance(Instance instance) {
                        if( classifiers==null ) {
                                return 0.0; // TODO check how WEKA expects classifyInstance to behave if no classifier exists yet
                        }
                        
                        double classification = 0.0;
                        for( int i=0 ; i<classifiers.size(); i++ ) {
                                Classifier classifier = classifiers.get(i);
                                Instances traindata = trainingData.get(i);
                                
                                Set<String> attributeNames = new HashSet<>();
                                for( int j=0; j<traindata.numAttributes(); j++ ) {
                                        attributeNames.add(traindata.attribute(j).name());
                                }
                                
                                double[] values = new double[traindata.numAttributes()];
                                int index = 0;
                                for( int j=0; j<instance.numAttributes(); j++ ) {
                                        if( attributeNames.contains(instance.attribute(j).name())) {
                                                values[index] = instance.value(j);
                                                index++;
                                        }
                                }
                                
                                Instances tmp = new Instances(traindata);
                                tmp.clear();
                                Instance instCopy = new DenseInstance(instance.weight(), values);
                                instCopy.setDataset(tmp);
                                try {
                                        classification += classifier.classifyInstance(instCopy);
                                } catch (Exception e) {
                                        throw new RuntimeException("bagging classifier could not classify an instance", e);
                                }
                        }
                        classification /= classifiers.size();
                        return (classification>=0.5) ? 1.0 : 0.0;
                }
                
                public void buildClassifier(SetUniqueList<Instances> traindataSet) throws Exception {
                        classifiers = new LinkedList<>();
                        trainingData = new LinkedList<>();
                        for( Instances traindata : traindataSet ) {
                                Classifier classifier = setupClassifier();
                                classifier.buildClassifier(traindata);
                                classifiers.add(classifier);
                                trainingData.add(new Instances(traindata));
                        }
                }
        
                @Override
                public void buildClassifier(Instances traindata) throws Exception {
                        classifiers = new LinkedList<>();
                        trainingData = new LinkedList<>();
                        final Classifier classifier = setupClassifier();
                        classifier.buildClassifier(traindata);
                        classifiers.add(classifier);
                        trainingData.add(new Instances(traindata));
                }
        }
}