/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 by Rapid-I and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapid-i.com
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Affero General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Affero General Public License for more details.
 *
 *  You should have received a copy of the GNU Affero General Public License
 *  along with this program.  If not, see http://www.gnu.org/licenses/.
 */
package com.rapidminer.operator.learner.meta;

import java.util.Arrays;
import java.util.HashMap;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.Tools;


/**
 * This class is associated to the MetaCost operator and supports the 
 * evaluation procedures of the MetaCost method.
 *  
 * @author Helge Homburg
 */
public class MetaCostModel extends PredictionModel implements MetaModel {

	private static final long serialVersionUID = -7378871544357578954L;

	private Model[] models;

	private double[][] costMatrix;

	public MetaCostModel(ExampleSet exampleSet, Model[] models, double[][] costMatrix) {
		super(exampleSet);
		this.models = models;
		this.costMatrix = costMatrix;
	}	

	public int getNumberOfModels() {
		return models.length;
	}

	/** Returns a binary decision model for the given classification index. */
	public Model getModel(int index) {
		return models[index];
	}


	/** Returns a single value from the cost matrix. */
	public double getCostValue(int i, int j) {
		return costMatrix[i][j];
	}

	@Override
	public ExampleSet performPrediction(ExampleSet originalExampleSet, Attribute predictedLabel) throws OperatorException {
		ExampleSet exampleSet = (ExampleSet)originalExampleSet.clone();
		int numberOfClasses = getLabel().getMapping().getValues().size();

		double[][] confidences = new double[exampleSet.size()][numberOfClasses];

		// Hash maps are used for addressing particular class values using indices without relying 
		// upon a consistent index distribution of the corresponding substructure.
		int currentNumber = 0;		
		HashMap<Integer, String>  classIndexMap = new HashMap<Integer, String> (numberOfClasses);		
		for (String currentClass : getLabel().getMapping().getValues()) {
			classIndexMap.put(currentNumber, currentClass);							
			currentNumber++;
		}			

		// 1. Iterate over all models and all examples for every model to receive all confidence values.
		for (int k = 0; k < getNumberOfModels(); k++) {
			Model model = getModel(k);			
			exampleSet = model.apply(exampleSet);

			Iterator<Example> reader = exampleSet.iterator();
			int counter = 0;				

			while (reader.hasNext()) {				
				Example example = reader.next();
				int currentClassNumber = 0;
				for (String currentClass : getLabel().getMapping().getValues()) {					
					final double confidence = example.getConfidence(currentClass);
					confidences[counter][currentClassNumber] += confidence;
					currentClassNumber++;
				}
				counter++;
			}
			PredictionModel.removePredictedLabel(exampleSet);		
		}

		// 2. Iterate again over all examples to compute a prediction and a confidence distribution for 
		//    all examples depending on the results of step 1 and the cost matrix. 
		Attribute classificationCost = AttributeFactory.createAttribute(Attributes.CLASSIFICATION_COST, Ontology.REAL);
		originalExampleSet.getExampleTable().addAttribute(classificationCost);
		originalExampleSet.getAttributes().setCost(classificationCost);		
		int counter = 0;				
		for(Example example: originalExampleSet) {	
			for (int i = 0; i < numberOfClasses; i++) { 				
				confidences[counter][i] = confidences[counter][i] / getNumberOfModels(); 				
			}			

			double[] expectedCosts = new double[numberOfClasses];
			int bestIndex = 0; // if confidences evaluate to NaN: use first
			double bestValue = Double.POSITIVE_INFINITY;

			for (int i = 0; i < numberOfClasses; i++) {
				for (int j = 0; j < numberOfClasses; j++) {
					expectedCosts[i] += confidences[counter][j] * costMatrix[i][j];					
				}
				if (expectedCosts[i] < bestValue) {
					bestValue = expectedCosts[i];
					bestIndex = i;						
				} 
			}

			// setting prediction, expectedCost and confidences
			example.setValue(predictedLabel, getLabel().getMapping().mapString(classIndexMap.get(bestIndex)));
			example.setValue(classificationCost, expectedCosts[bestIndex]);

			for (int i = 0; i < numberOfClasses; i++) {			
				example.setConfidence(classIndexMap.get(i), confidences[counter][i]);
			}
			counter++;
		}		
		return originalExampleSet;
	}

	@Override
	public String toString() {
		StringBuffer result = new StringBuffer(super.toString() + Tools.getLineSeparator());
		for (int i = 0; i < models.length; i++)
			result.append((i > 0 ? Tools.getLineSeparator() : "") + models[i].toString());
		return result.toString();
	}

	@Override
	public List<String> getModelNames() {
		List<String> names = new LinkedList<String>();
		for (int i = 0; i < this.getNumberOfModels(); i++) {
			names.add("Model " + (i + 1));
		}
		return names;
	}

	@Override
	public List<Model> getModels() {
		return Arrays.asList(models);
	}

}