package se.lth.cs.nlp.opinions;

import se.lth.cs.nlp.nlputils.core.*;
import se.lth.cs.nlp.nlputils.ml_long.featuresearch.*;
import java.io.*;
import java.util.Arrays;

public class SeqLabelerEvaluator extends Function<FeatureSet, Double> {

	private String trainFileName;
	private int nCVFolds = -1;

	private String methodName;
	private String[] methodArgs;
	private boolean secondOrder;
	
	private String outputFeature;
	
	public SeqLabelerEvaluator(String[] argv) {
		trainFileName = argv[0];
		nCVFolds = Integer.parseInt(argv[1]);
		secondOrder = Boolean.parseBoolean(argv[2]);
		outputFeature = argv[3];
		methodName = argv[4];
		methodArgs = CollectionUtils.subArray(argv, 5, argv.length - 5);
		
		System.out.println("trainFile = " + trainFileName);
		System.out.println("nCVFolds = " + nCVFolds);
		System.out.println("secondOrder = " + secondOrder);
		System.out.println("outputFeature = " + outputFeature);
		System.out.println("methodName = " + methodName);

		System.out.println("methodArgs = " + Arrays.toString(methodArgs));
	}

	private static void printFeatureSet(String fileName, String outputFeature, 
			FeatureSet f) throws IOException {
		PrintWriter pw = new PrintWriter(new FileWriter(fileName));
		boolean seen = false;		
		for(String feature: f.fs) {
			if(seen)
				pw.println(",");
			seen = true;
			pw.print(outputFeature + " " + feature);
		}		
		for(Pair<String, String> p: f.ps) {
			if(seen)
				pw.println(",");
			seen = true;
			pw.print(outputFeature + " " + p.left + " " + p.right);
		}
		if(seen)
			pw.println();		
		pw.close();
	}
	
	public Double apply(FeatureSet features) {
		
		try {
			String templateFile = Util.getTempFileName("slev");
			printFeatureSet(templateFile, outputFeature, features);

			String[] argv = new String[6 + methodArgs.length];
			argv[0] = "-cv";
			argv[1] = trainFileName;
			argv[2] = templateFile;
			argv[3] = "" + nCVFolds;
			argv[4] = "" + secondOrder;
			argv[5] = methodName;
			System.arraycopy(methodArgs, 0, argv, 6, methodArgs.length);
			
			double[] stats = SeqLabeler.cv(argv);
			
			new File(templateFile).delete();
			
	    	double nGuesses = stats[1];
	    	double nInGold = stats[2];
	    	double propGuessCorrect = stats[3];
	    	double propFoundCorrect = stats[4];

	    	double fSoft;
	    	if(nGuesses == 0)
	    		fSoft = 0;
	    	else {
	    		double pSoft = propGuessCorrect / nGuesses;
	    		double rSoft = propFoundCorrect / nInGold;
	    		if(pSoft == 0 || rSoft == 0)
	    			fSoft = 0;
	    		else
	    			fSoft = 2*pSoft*rSoft / (pSoft + rSoft);
	    	}

	    	return fSoft;
		} catch(IOException e) {
			throw new RuntimeException(e);
		}
	}
	
}