mulan.examples.ReliefFDimensionalityReduction.java Source code

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Here is the source code for mulan.examples.ReliefFDimensionalityReduction.java

Source

/*
 *    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.
 */
package mulan.examples;

import java.util.Arrays;

import mulan.data.MultiLabelInstances;
import mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator;
import mulan.dimensionalityReduction.Ranker;
import weka.attributeSelection.ASEvaluation;
import weka.attributeSelection.GainRatioAttributeEval;
import weka.attributeSelection.ReliefFAttributeEval;
import weka.core.Instances;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;

public class ReliefFDimensionalityReduction {

    public static void main(String[] args) throws Exception {
        String path = Utils.getOption("path", args);
        String filestem = Utils.getOption("filestem", args);
        MultiLabelInstances mlData = new MultiLabelInstances(path + filestem + ".arff", path + filestem + ".xml");
        String attributesToKeep = Utils.getOption("numattribs", args);
        final int NUM_TO_KEEP = Integer.parseInt(attributesToKeep);

        ASEvaluation ase = new ReliefFAttributeEval();
        BinaryRelevanceAttributeEvaluator ae = new BinaryRelevanceAttributeEvaluator(ase, mlData, "max", "dl",
                "eval");

        System.out.println(mlData.getDataSet().numAttributes());

        if (NUM_TO_KEEP == 0) {
            for (int i = 0; i < mlData.getFeatureIndices().length; i++) {
                System.out
                        .println("Attribute " + mlData.getDataSet().attribute(mlData.getFeatureIndices()[i]).name()
                                + " : " + ae.evaluateAttribute(
                                        mlData.getDataSet().attribute(mlData.getFeatureIndices()[i]).index()));
            }
        } else {

            Ranker r = new Ranker();
            int[] result = r.search(ae, mlData);
            System.out.println(Arrays.toString(result));

            int[] toKeep = new int[NUM_TO_KEEP + mlData.getNumLabels()];
            System.arraycopy(result, 0, toKeep, 0, NUM_TO_KEEP);
            int[] labelIndices = mlData.getLabelIndices();
            System.arraycopy(labelIndices, 0, toKeep, NUM_TO_KEEP, mlData.getNumLabels());

            Remove filterRemove = new Remove();
            filterRemove.setAttributeIndicesArray(toKeep);
            filterRemove.setInvertSelection(true);
            filterRemove.setInputFormat(mlData.getDataSet());
            Instances filtered = Filter.useFilter(mlData.getDataSet(), filterRemove);
            MultiLabelInstances mlFiltered = new MultiLabelInstances(filtered, mlData.getLabelsMetaData());

            System.out.println("\n\n\n\n" + mlFiltered.getDataSet());
        }

        // You can now work on the reduced multi-label dataset mlFiltered
    }
}