cc.mallet.classify.ConfidencePredictingClassifierTrainer Maven / Gradle / Ivy

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MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
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/* 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. */




/** 
   @author Andrew McCallum [email protected]
 */

package cc.mallet.classify;

import java.util.ArrayList;
import java.util.logging.*;

import cc.mallet.classify.evaluate.*;
import cc.mallet.pipe.Classification2ConfidencePredictingFeatureVector;
import cc.mallet.pipe.Pipe;
import cc.mallet.types.*;
import cc.mallet.util.MalletLogger;
import cc.mallet.util.PropertyList;

public class ConfidencePredictingClassifierTrainer extends ClassifierTrainer implements Boostable
{
	private static Logger logger =
		MalletLogger.getLogger(ConfidencePredictingClassifierTrainer.class.getName());

	ClassifierTrainer underlyingClassifierTrainer;
	MaxEntTrainer confidencePredictingClassifierTrainer;
	//DecisionTreeTrainer confidencePredictingClassifierTrainer;
	//NaiveBayesTrainer confidencePredictingClassifierTrainer;
	Pipe confidencePredictingPipe;
	static ConfusionMatrix confusionMatrix = null;
	ConfidencePredictingClassifier classifier;
	public ConfidencePredictingClassifier getClassifier () { return classifier; }

	public ConfidencePredictingClassifierTrainer (ClassifierTrainer underlyingClassifierTrainer,
			InstanceList validationSet,
			Pipe confidencePredictingPipe)
	{
		this.confidencePredictingPipe = confidencePredictingPipe;
		this.confidencePredictingClassifierTrainer = new MaxEntTrainer();
		this.validationSet = validationSet;
		//this.confidencePredictingClassifierTrainer = new DecisionTreeTrainer();
		//this.confidencePredictingClassifierTrainer = new NaiveBayesTrainer();
		this.underlyingClassifierTrainer = underlyingClassifierTrainer;

	}

	public ConfidencePredictingClassifierTrainer (ClassifierTrainer underlyingClassifierTrainer, InstanceList validationSet)
	{
		this (underlyingClassifierTrainer, validationSet, new Classification2ConfidencePredictingFeatureVector());
	}

	public ConfidencePredictingClassifier train (InstanceList trainList)
	{
		FeatureSelection selectedFeatures = trainList.getFeatureSelection();
		logger.fine ("Training underlying classifier");
		Classifier c = underlyingClassifierTrainer.train (trainList);
		confusionMatrix = new ConfusionMatrix(new Trial(c, trainList));

		assert (validationSet != null) : "This ClassifierTrainer requires a validation set.";
		Trial t = new Trial (c, validationSet);
		double accuracy = t.getAccuracy();
		InstanceList confidencePredictionTraining = new InstanceList (confidencePredictingPipe);
		logger.fine ("Creating confidence prediction instance list");
		double weight;
		for (int i = 0; i < t.size(); i++) {
			Classification classification = t.get(i);
			confidencePredictionTraining.add (classification, null, classification.getInstance().getName(), classification.getInstance().getSource());			
		}

		logger.info("Begin training ConfidencePredictingClassifier . . . ");
		Classifier cpc = confidencePredictingClassifierTrainer.train (confidencePredictionTraining);
		logger.info("Accuracy at predicting correct/incorrect in training = " + cpc.getAccuracy(confidencePredictionTraining));

		// get most informative features per class, then combine to make
		// new feature conjunctions
		PerLabelInfoGain perLabelInfoGain = new PerLabelInfoGain (trainList);




		/*		AdaBoostTrainer adaTrainer = new AdaBoostTrainer (confidencePredictingClassifierTrainer, 10);
			Classifier ada = adaTrainer.train (confidencePredictionTraining);
			System.out.println ("Accuracy at predicting correct/incorrect in BOOSTING training = " + ada.getAccuracy(confidencePredictionTraining));
		 */


//		print out most informative features
		/*		InfoGain ig = new InfoGain (confidencePredictionTraining);
		for (int i = 0; i < ig.numLocations(); i++)
		logger.info ("InfoGain["+ig.getObjectAtRank(i)+"]="+ig.getValueAtRank(i));
		 */
		this.classifier = new ConfidencePredictingClassifier (c, cpc);
		return classifier;
//		return new ConfidencePredictingClassifier (c, ada);
	}

}