TrainClassifier.java

package edu.cmu.minorthird.ui;

import java.io.IOException;

import edu.cmu.minorthird.classify.Classifier;
import edu.cmu.minorthird.classify.Dataset;
import edu.cmu.minorthird.classify.DatasetClassifierTeacher;
import edu.cmu.minorthird.text.learn.ClassifierAnnotator;
import edu.cmu.minorthird.util.CommandLineProcessor;
import edu.cmu.minorthird.util.IOUtil;
import edu.cmu.minorthird.util.JointCommandLineProcessor;
import edu.cmu.minorthird.util.gui.SmartVanillaViewer;
import edu.cmu.minorthird.util.gui.Viewer;
import edu.cmu.minorthird.util.gui.ViewerFrame;

/**
 * Train a text classifier.
 *
 * @author William Cohen
 */

public class TrainClassifier extends UIMain{

	// private data needed to train a classifier

	private CommandLineUtil.SaveParams save=new CommandLineUtil.SaveParams();

	private CommandLineUtil.ClassificationSignalParams signal=
			new CommandLineUtil.ClassificationSignalParams(base);

	private CommandLineUtil.TrainClassifierParams train=
			new CommandLineUtil.TrainClassifierParams();

	private Classifier classifier=null;

	public CommandLineUtil.SaveParams getSaveParameters(){
		return save;
	}

	public void setSaveParameters(CommandLineUtil.SaveParams p){
		save=p;
	}

	public CommandLineUtil.ClassificationSignalParams getSignalParameters(){
		return signal;
	}

	public void setSignalParameters(CommandLineUtil.ClassificationSignalParams p){
		signal=p;
	}

	public CommandLineUtil.TrainClassifierParams getAdditionalParameters(){
		return train;
	}

	public void setAdditionalParameters(CommandLineUtil.TrainClassifierParams p){
		train=p;
	}

	public String getTrainClassifierHelp(){
		return "<A HREF=\"http://minorthird.sourceforge.net/tutorials/TrainClassifier%20Tutorial.htm\">TrainClassifier Tutorial</A></html>";
	}

	@Override
	public CommandLineProcessor getCLP(){
		return new JointCommandLineProcessor(new CommandLineProcessor[]{gui,base,
				save,signal,train});
	}

	//
	// do the experiment
	// 

	@Override
	public void doMain(){
		// check that inputs are valid
		if(train.learner==null)
			throw new IllegalArgumentException("-learner must be specified");
		if(signal.spanProp==null&&signal.spanType==null)
			throw new IllegalArgumentException(
					"one of -spanProp or -spanType must be specified");
		if(signal.spanProp!=null&&signal.spanType!=null)
			throw new IllegalArgumentException(
					"only one of -spanProp or -spanType can be specified");

		// construct the dataset
		Dataset d=
				CommandLineUtil.toDataset(base.labels,train.fe,signal.spanProp,
						signal.spanType,signal.candidateType);
		if(train.showData){
			System.out.println("Trying to show the Dataset");
			new ViewerFrame("Dataset",d.toGUI());
		}

		/*Dataset seqDataset = 
		  CommandLineUtil.toSeqDataset(base.labels,train.fe,signal.spanProp,"combined");
			
		  if (train.showData) {
		  System.out.println("Trying to create Sequential Dataset");
		  new ViewerFrame("SequenceDataset", seqDataset.toGUI());
		  }*/

		// train the classifier
		classifier=new DatasetClassifierTeacher(d).train(train.learner);

		if(base.showResult){
			Viewer cv=new SmartVanillaViewer();
			cv.setContent(classifier);
			new ViewerFrame("Classifier",cv);
		}

		String type=signal.getOutputType(train.output);
		String prop=signal.getOutputProp(train.output);
		ClassifierAnnotator ann=
				new ClassifierAnnotator(train.fe,classifier,type,prop,
						signal.candidateType);

		if(save.saveAs!=null){
			try{
				IOUtil.saveSerialized(ann,save.saveAs);
			}catch(IOException e){
				throw new IllegalArgumentException("can't save to "+save.saveAs+": "+e);
			}
		}
	}

	@Override
	public Object getMainResult(){
		return classifier;
	}

	public static void main(String args[]){
		new TrainClassifier().callMain(args);
	}
}