featureselector.java

常用机器学习算法,java编写源代码,内含常用分类算法,包括说明文档

/* Copyright (C) 2002 Univ. of Massachusetts Amherst, Computer Science Dept.
   This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
   http://www.cs.umass.edu/~mccallum/mallet
   This software is provided under the terms of the Common Public License,
   version 1.0, as published by http://www.opensource.org.  For further
   information, see the file `LICENSE' included with this distribution. */




/**
	 Given an arbitrary scheme for ranking features, set of feature selection of
	 an InstanceList.

   @author Andrew McCallum <a href="mailto:[e-mail]">[e-mail]</a>
 */

package edu.umass.cs.mallet.base.types;

import edu.umass.cs.mallet.base.util.MalletLogger;
import java.util.logging.*;

public class FeatureSelector
{
	private static Logger logger = MalletLogger.getLogger(FeatureSelector.class.getName());
	// Only one of the following two will be non-null
	RankedFeatureVector.Factory ranker;
	RankedFeatureVector.PerLabelFactory perLabelRanker;
	// Only one of the following two will be changed
	int numFeatures = -1;
	double minThreshold = Double.POSITIVE_INFINITY;	// Not yet used

	public FeatureSelector (RankedFeatureVector.Factory ranker,
													int numFeatures)
	{
		this.ranker = ranker;
		this.numFeatures = numFeatures;
	}

	public FeatureSelector (RankedFeatureVector.Factory ranker,
													double minThreshold)
	{
		this.ranker = ranker;
		this.minThreshold = minThreshold;
	}
	
	public FeatureSelector (RankedFeatureVector.PerLabelFactory perLabelRanker,
													int numFeatures)
	{
		this.perLabelRanker = perLabelRanker;
		this.numFeatures = numFeatures;
	}

	public FeatureSelector (RankedFeatureVector.PerLabelFactory perLabelRanker,
													double minThreshold)
	{
		this.perLabelRanker = perLabelRanker;
		this.minThreshold = minThreshold;
	}

	
	public void selectFeaturesFor (InstanceList ilist, InstanceList validationList)
	{
		if (perLabelRanker != null)
			selectFeaturesForPerLabel (ilist, validationList);
		else
			selectFeaturesForAllLabels (ilist, validationList);
	}

	public void selectFeaturesForAllLabels (InstanceList ilist, InstanceList validationList)
		
	{
		RankedFeatureVector ranking = ranker.newRankedFeatureVector (ilist);
		FeatureSelection fs = new FeatureSelection (ilist.getDataAlphabet());
		int nf = Math.min (numFeatures, ranking.singleSize());
		for (int i = 0; i < nf; i++) {
			logger.info ("adding feature "+i+" word="+ilist.getDataAlphabet().lookupObject(ranking.getIndexAtRank(i)));
			fs.add (ranking.getIndexAtRank(i));
		}
		ilist.setPerLabelFeatureSelection (null);
		ilist.setFeatureSelection (fs);
	}

	public void selectFeaturesForPerLabel (InstanceList ilist, InstanceList validationList)
	{
		RankedFeatureVector[] rankings = perLabelRanker.newRankedFeatureVectors (ilist);
		int numClasses = rankings.length;
		FeatureSelection[] fs = new FeatureSelection[numClasses];
		for (int i = 0; i < numClasses; i++) {
			fs[i] = new FeatureSelection (ilist.getDataAlphabet());
			RankedFeatureVector ranking = rankings[i];
			int nf = Math.min (numFeatures, ranking.singleSize());
			if (nf >= 0) {
				for (int j = 0; j < nf; j++)
					fs[i].add (ranking.getIndexAtRank(j));
			} else {
				for (int j = 0; j < ranking.singleSize(); j++) {
					if (ranking.getValueAtRank(j) > minThreshold)
						fs[i].add (ranking.getIndexAtRank(j));
					else
						break;
				}
			}
		}
		ilist.setFeatureSelection (null);
		ilist.setPerLabelFeatureSelection (fs);
	}
	
}