package edu.cmu.minorthird.classify.sequential;

import org.apache.log4j.Logger;

import edu.cmu.minorthird.classify.Dataset;
import edu.cmu.minorthird.classify.Example;
import edu.cmu.minorthird.classify.Splitter;
import edu.cmu.minorthird.classify.experiments.Evaluation;
import edu.cmu.minorthird.util.ProgressCounter;
import edu.cmu.minorthird.util.gui.ParallelViewer;
import edu.cmu.minorthird.util.gui.TransformedViewer;
import edu.cmu.minorthird.util.gui.Viewer;
import edu.cmu.minorthird.util.gui.Visible;

/** 
 * View result of some sort of train/test experiment
 * on sequential data.
 *
 * @author William Cohen
 */

public class CrossValidatedSequenceDataset implements Visible
{
	static private Logger log = Logger.getLogger(CrossValidatedSequenceDataset.class);

	private ClassifiedSequenceDataset[] cds;
	private ClassifiedSequenceDataset[] trainCds;
	private Evaluation v;

	public CrossValidatedSequenceDataset(
			SequenceClassifierLearner learner,SequenceDataset d,Splitter<Example[]> splitter)
	{
		this(learner,d,splitter,false);
	}

	public CrossValidatedSequenceDataset(
			SequenceClassifierLearner learner,SequenceDataset d,Splitter<Example[]> splitter,boolean saveTrainPartitions)
	{
		Dataset.Split s = d.splitSequence(splitter);
		cds = new ClassifiedSequenceDataset[s.getNumPartitions()];
		trainCds = saveTrainPartitions ? new ClassifiedSequenceDataset[s.getNumPartitions()] : null;
		v = new Evaluation(d.getSchema());
		ProgressCounter pc = new ProgressCounter("train/test","fold",s.getNumPartitions());
		for (int k=0; k<s.getNumPartitions(); k++) {
			SequenceDataset trainData = (SequenceDataset)s.getTrain(k);
			SequenceDataset testData = (SequenceDataset)s.getTest(k);
			log.info("splitting with "+splitter+", preparing to train on "+trainData.size()
					+" and test on "+testData.size());
			//showSubpops("subpops for test fold "+k+": ", testData);
			SequenceClassifier c = new DatasetSequenceClassifierTeacher(trainData).train(learner);
			cds[k] = new ClassifiedSequenceDataset(c, testData);
			if (trainCds!=null) trainCds[k] = new ClassifiedSequenceDataset(c, trainData);
			v.extend( cds[k].getClassifier(), testData, Evaluation.DEFAULT_PARTITION_ID );
			log.info("splitting with "+splitter+", stored classified dataset");
			pc.progress();
		}
		pc.finished();
	}

	public Evaluation getEvaluation()
	{
		return v;
	}

//	private void showSubpops(String msg,SequenceDataset d)
//	{
//		Set ids = new TreeSet();
//		for (Iterator<Example> i=d.iterator(); i.hasNext(); ) {
//			Example e = i.next();
//			ids.add(e.getSubpopulationId());
//		}
//		log.debug(msg+ids.toString());
//	}

	@Override
	public Viewer toGUI()
	{
		ParallelViewer main = new ParallelViewer();
		for (int i=0; i<cds.length; i++) {
			final int k = i;
			main.addSubView(
					"Test Partition "+(i+1), 
					new TransformedViewer(cds[0].toGUI()) {
						static final long serialVersionUID=20080207L;
						@Override
						public Object transform(Object o) {
//							CrossValidatedSequenceDataset cvd = (CrossValidatedSequenceDataset)o;
							return cds[k];
						}});
		}
		if (trainCds!=null) {
			for (int i=0; i<trainCds.length; i++) {
				final int k = i;
				main.addSubView(
						"Train Partition "+(i+1), 
						new TransformedViewer(cds[0].toGUI()) {
							static final long serialVersionUID=20080207L;
							@Override
							public Object transform(Object o) {
//								CrossValidatedSequenceDataset cvd = (CrossValidatedSequenceDataset)o;
								return trainCds[k];
							}});
			}
		}
		main.addSubView(
				"Overall Evaluation", 
				new TransformedViewer(v.toGUI()) {
					static final long serialVersionUID=20080207L;
					@Override
					public Object transform(Object o) {
						CrossValidatedSequenceDataset cvd = (CrossValidatedSequenceDataset)o;												
						return cvd.v;
					}
				});
		main.setContent(this);
		return main;
	}

	public static void main(String[] args)
	{
	}

}