source: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/AbstractCharacteristicSelection.java @ 37

package de.ugoe.cs.cpdp.dataselection;

import java.util.ArrayList;

import org.apache.commons.collections4.list.SetUniqueList;

import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.experiment.Stats;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;

/**
 * Abstract class that implements the foundation of setwise data selection strategies using distributional characteristics.
 * This class provides the means to transform the data sets into their characteristic vectors.  
 * @author Steffen Herbold
 */
public abstract class AbstractCharacteristicSelection implements
                ISetWiseDataselectionStrategy {

        /**
         * vector with the distributional characteristics 
         */
        private String[] characteristics = new String[]{"mean","stddev"};
        
        /**
         * Sets the distributional characteristics. The names of the characteristics are separated by blanks. 
         */
        @Override
        public void setParameter(String parameters) {
                if( !"".equals(parameters) ) {
                        characteristics = parameters.split(" ");
                }
        }
        
        /**
         * Transforms the data into the distributional characteristics. The first instance is the test data, followed by the training data. 
         * @param testdata test data
         * @param traindataSet training data sets
         * @return distributional characteristics of the data
         */
        protected Instances characteristicInstances(Instances testdata, SetUniqueList<Instances> traindataSet) {
                // setup weka Instances for clustering
                final ArrayList<Attribute> atts = new ArrayList<Attribute>();
                
                final Attribute classAtt = testdata.classAttribute();
                for( int i=0 ; i<testdata.numAttributes() ; i++ ) {
                        Attribute dataAtt = testdata.attribute(i);
                        if( !dataAtt.equals(classAtt) ) {
                                for( String characteristic : characteristics ) {
                                        atts.add(new Attribute(dataAtt.name() + "_" + characteristic));
                                }
                        }
                }
                final Instances data = new Instances("distributional_characteristics", atts, 0);
                
                // setup data for clustering
                double[] instanceValues = new double[atts.size()];
                for( int i=0 ; i<testdata.numAttributes() ; i++ ) {
                        Attribute dataAtt = testdata.attribute(i);
                        if( !dataAtt.equals(classAtt) ) {
                                Stats stats = testdata.attributeStats(i).numericStats;
                                for( int j=0; j<characteristics.length; j++ ) {
                                        if( "mean".equals(characteristics[j]) ) {
                                                instanceValues[i*characteristics.length+j] = stats.mean;
                                        } else if( "stddev".equals(characteristics[j])) {
                                                instanceValues[i*characteristics.length+j] = stats.stdDev;
                                        } else if( "var".equals(characteristics[j])) {
                                                instanceValues[i*characteristics.length+j] = testdata.variance(j);
                                        } else {
                                                throw new RuntimeException("Unkown distributional characteristic: " + characteristics[j]);
                                        }
                                }
                        }
                }               
                data.add(new DenseInstance(1.0, instanceValues));
                
                for( Instances traindata : traindataSet ) {
                        instanceValues = new double[atts.size()];
                        for( int i=0 ; i<traindata.numAttributes() ; i++ ) {
                                Attribute dataAtt = traindata.attribute(i);
                                if( !dataAtt.equals(classAtt) ) {
                                        Stats stats = traindata.attributeStats(i).numericStats;
                                        for( int j=0; j<characteristics.length; j++ ) {
                                                if( "mean".equals(characteristics[j]) ) {
                                                        instanceValues[i*characteristics.length+j] = stats.mean;
                                                } else if( "stddev".equals(characteristics[j])) {
                                                        instanceValues[i*characteristics.length+j] = stats.stdDev;
                                                } else if( "var".equals(characteristics[j])) {
                                                        instanceValues[i*characteristics.length+j] = testdata.variance(j);
                                                } else {
                                                        throw new RuntimeException("Unkown distributional characteristic: " + characteristics[j]);
                                                }
                                        }
                                }
                        }               
                        Instance instance = new DenseInstance(1.0, instanceValues);
                        
                        data.add(instance);
                }
                return data;
        }
        
        /**
         * Returns the normalized distributional characteristics of the training data. 
         * @param testdata test data
         * @param traindataSet training data sets
         * @return normalized distributional characteristics of the data
         */
        protected Instances normalizedCharacteristicInstances(Instances testdata, SetUniqueList<Instances> traindataSet) {
                Instances data = characteristicInstances(testdata, traindataSet);
                try {
                        final Normalize normalizer = new Normalize();
                        normalizer.setInputFormat(data);
                        data = Filter.useFilter(data, normalizer);
                } catch (Exception e) {
                        throw new RuntimeException("Unexpected exception during normalization of distributional characteristics.", e);
                }
                return data;
        }
}