/*
 *    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.
 */

/*
 *    AttributeSelectionTest.java
 *    Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
 *
 */

package wekaexamples.attributeSelection;

import weka.attributeSelection.AttributeSelection;
import weka.attributeSelection.CfsSubsetEval;
import weka.attributeSelection.GreedyStepwise;
import weka.classifiers.Evaluation;
import weka.classifiers.meta.AttributeSelectedClassifier;
import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.core.Utils;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;

import java.util.Random;

/**
 * performs attribute selection using CfsSubsetEval and GreedyStepwise
 * (backwards) and trains J48 with that. Needs 3.5.5 or higher to compile.
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class AttributeSelectionTest {

  /**
   * uses the meta-classifier
   */
  protected static void useClassifier(Instances data) throws Exception {
    System.out.println("\n1. Meta-classfier");
    AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
    CfsSubsetEval eval = new CfsSubsetEval();
    GreedyStepwise search = new GreedyStepwise();
    search.setSearchBackwards(true);
    J48 base = new J48();
    classifier.setClassifier(base);
    classifier.setEvaluator(eval);
    classifier.setSearch(search);
    Evaluation evaluation = new Evaluation(data);
    evaluation.crossValidateModel(classifier, data, 10, new Random(1));
    System.out.println(evaluation.toSummaryString());
  }

  /**
   * uses the filter
   */
  protected static void useFilter(Instances data) throws Exception {
    System.out.println("\n2. Filter");
    weka.filters.supervised.attribute.AttributeSelection filter = new weka.filters.supervised.attribute.AttributeSelection();
    CfsSubsetEval eval = new CfsSubsetEval();
    GreedyStepwise search = new GreedyStepwise();
    search.setSearchBackwards(true);
    filter.setEvaluator(eval);
    filter.setSearch(search);
    filter.setInputFormat(data);
    Instances newData = Filter.useFilter(data, filter);
    System.out.println(newData);
  }

  /**
   * uses the low level approach
   */
  protected static void useLowLevel(Instances data) throws Exception {
    System.out.println("\n3. Low-level");
    AttributeSelection attsel = new AttributeSelection();
    CfsSubsetEval eval = new CfsSubsetEval();
    GreedyStepwise search = new GreedyStepwise();
    search.setSearchBackwards(true);
    attsel.setEvaluator(eval);
    attsel.setSearch(search);
    attsel.SelectAttributes(data);
    int[] indices = attsel.selectedAttributes();
    System.out.println("selected attribute indices (starting with 0):\n" + Utils.arrayToString(indices));
  }

  /**
   * takes a dataset as first argument
   *
   * @param args        the commandline arguments
   * @throws Exception  if something goes wrong
   */
  public static void main(String[] args) throws Exception {
    // load data
    System.out.println("\n0. Loading data");
    DataSource source = new DataSource(args[0]);
    Instances data = source.getDataSet();
    if (data.classIndex() == -1)
      data.setClassIndex(data.numAttributes() - 1);

    // 1. meta-classifier
    useClassifier(data);

    // 2. filter
    useFilter(data);

    // 3. low-level
    useLowLevel(data);
  }
}