package edu.cmu.minorthird.classify.ranking;

import java.io.File;
import java.io.IOException;
import java.util.Iterator;
import java.util.List;
import java.util.Map;

import edu.cmu.minorthird.classify.BinaryClassifier;
import edu.cmu.minorthird.classify.Dataset;
import edu.cmu.minorthird.classify.DatasetClassifierTeacher;
import edu.cmu.minorthird.classify.DatasetLoader;
import edu.cmu.minorthird.classify.Example;
import edu.cmu.minorthird.classify.Splitter;
import edu.cmu.minorthird.classify.experiments.CrossValSplitter;
import edu.cmu.minorthird.classify.experiments.Expt;
import edu.cmu.minorthird.util.BasicCommandLineProcessor;
import edu.cmu.minorthird.util.CommandLineProcessor;
import edu.cmu.minorthird.util.IOUtil;
import edu.cmu.minorthird.util.ProgressCounter;
import edu.cmu.minorthird.util.gui.ViewerFrame;

/** 
 * Learn from examples a GraphSearcher that re-ranks examples based on
 * scores from a learned classifier.
 */

public class RankingExpt
{
	private String dataFileName = null;
	private BatchRankingLearner classifierLearner = new RankingPerceptron();
	private Splitter<Example> splitter = new CrossValSplitter<Example>();
	private String saveFile = null;
//	private StringEncoder encoder = new StringEncoder('%',"$ \t\n");
	private boolean guiFlag = false;

	public CommandLineProcessor getCLP() { return new MyCLP(); }
	public class MyCLP extends BasicCommandLineProcessor 
	{
		public void data(String s) { dataFileName = s; }
		public void splitter(String s) { splitter = Expt.toSplitter(s);	}
		public void saveAs(String s) { saveFile = s; }
		public void learner(String s) { classifierLearner = (BatchRankingLearner)Expt.toLearner(s); }
		public void gui() { guiFlag = true; }
	}

	private RankingEvaluation doExpt() throws IOException,NumberFormatException
	{
		Dataset data = DatasetLoader.loadFile(new File(dataFileName));
		System.out.println("loaded "+data.size()+" examples");

		RankingEvaluation eval = new RankingEvaluation();
		Dataset.Split split = data.split(splitter);
		ProgressCounter pc = new ProgressCounter("train/test", "fold", split.getNumPartitions());
		for (int k=0; k<split.getNumPartitions(); k++) {
//			Dataset train = split.getTrain(k);
			Dataset test = split.getTest(k);
			DatasetClassifierTeacher teacher = new DatasetClassifierTeacher(data);
			BinaryClassifier classifier = (BinaryClassifier)teacher.train(classifierLearner);
			doTest( classifier, test, eval );
			pc.progress();
		}
		pc.finished();
		return eval;
	}

	private void doTest( BinaryClassifier classifier, Dataset test, RankingEvaluation eval)
	{
		Map<String,List<Example>> bySubpopMap = BatchRankingLearner.splitIntoRankings(test);
		for (Iterator<String> i=bySubpopMap.keySet().iterator(); i.hasNext(); ) {
			String subpop = i.next();
			List<Example> subdata = bySubpopMap.get(subpop);
			eval.extend( subpop, subdata, classifier ); 
		}
	}

	public static void main(String[] args) throws IOException,NumberFormatException
	{
		RankingExpt x = new RankingExpt();
		x.getCLP().processArguments(args);
		RankingEvaluation eval = x.doExpt();
		System.out.println(eval.toTable());
		if (x.guiFlag) new ViewerFrame("result", eval.toGUI());
		if (x.saveFile!=null) 
			IOUtil.saveSomehow(eval, new File(x.saveFile),true);
	}
}