/*
* 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.
*/
/*
* Ranker.java
* Copyright (C) 2009-2010 Aristotle University of Thessaloniki, Thessaloniki, Greece
*/
package mulan.dimensionalityReduction;
import mulan.data.MultiLabelInstances;
import mulan.transformations.RemoveAllLabels;
import weka.attributeSelection.ASEvaluation;
import weka.attributeSelection.AttributeEvaluator;
import weka.core.Instances;
/**
* Ranks attributes according to an AttributeEvaluator. It internally uses Weka's
* Ranker, initialized so as to neglect the labels.
*
* @author Grigorios Tsoumakas
* @version 10 August 2010
*/
public class Ranker {
/**
* Calls a specified {@link AttributeEvaluator} to evaluate each feature
* attribute of specified {@link MultiLabelInstances} data set, excluding
* labels. Internally it uses {@link weka.attributeSelection.Ranker}
*
* @param attributeEval the attribute evaluator to guide the search
* @param mlData the multi-label instances data set
* @return an array (not necessarily ordered) of selected attribute indexes
* @throws Exception if an error occur in search
*/
public int[] search(AttributeEvaluator attributeEval, MultiLabelInstances mlData) throws Exception {
Instances data = RemoveAllLabels.transformInstances(mlData);
weka.attributeSelection.Ranker wekaRanker = new weka.attributeSelection.Ranker();
return wekaRanker.search((ASEvaluation) attributeEval, data);
}
}