/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 by RapidMiner and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapidminer.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.tree.criterions;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.learner.tree.FrequencyCalculator;
import com.rapidminer.operator.learner.tree.MinimalGainHandler;

/**
 * This criterion implements the well known information gain in 
 * order to calculate the benefit of a split. The information gain
 * is defined as the change in entropy from a prior state to a 
 * state that takes some information as given by the entropy.
 * 
 * @author Sebastian Land, Ingo Mierswa
 */
public class InfoGainCriterion extends AbstractCriterion implements MinimalGainHandler {
    
    private static double LOG_FACTOR = 1d / Math.log(2);
    
    private FrequencyCalculator frequencyCalculator = new FrequencyCalculator();
    
    private double minimalGain = 0.1;
    
    public InfoGainCriterion() {}
    
    public InfoGainCriterion(double minimalGain) {
    	this.minimalGain = minimalGain;
    }
    
    public void setMinimalGain(double minimalGain) {
    	this.minimalGain = minimalGain;    	
    }
    
    public double getNominalBenefit(ExampleSet exampleSet, Attribute attribute) {
    	double[][] weightCounts = frequencyCalculator.getNominalWeightCounts(exampleSet, attribute);
    	return getBenefit(weightCounts);
    }
    
    public double getNumericalBenefit(ExampleSet exampleSet, Attribute attribute, double splitValue) {
    	double[][] weightCounts = frequencyCalculator.getNumericalWeightCounts(exampleSet, attribute, splitValue);
    	return getBenefit(weightCounts);
    }
    
    public double getBenefit(double[][] weightCounts) {
    	int numberOfValues = weightCounts.length;
    	int numberOfLabels = weightCounts[0].length;

    	// calculate entropies
    	double[] entropies = new double[numberOfValues];
    	double[] totalWeights = new double[numberOfValues]; 
    	for (int v = 0; v < numberOfValues; v++) {
    		for (int l = 0; l < numberOfLabels; l++) {
    			totalWeights[v] += weightCounts[v][l];
    		}
    		
    		for (int l = 0; l < numberOfLabels; l++) {
                if (weightCounts[v][l] > 0) {
                    double proportion = weightCounts[v][l] / totalWeights[v];
                    entropies[v] -= (Math.log(proportion) * LOG_FACTOR) * proportion;
                }
    		}
    	}
    	
    	// calculate information amount WITH this attribute
    	double totalWeight = 0.0d;
    	for (double w : totalWeights) {
    		totalWeight += w;
    	}
    	
    	double information = 0.0d;
    	for (int v = 0; v < numberOfValues; v++) {
    		information += totalWeights[v] / totalWeight * entropies[v];
    	}
    	
    	
    	// calculate information amount WITHOUT this attribute
    	double[] classWeights = new double[numberOfLabels];
    	for (int l = 0; l < numberOfLabels; l++) {
        	for (int v = 0; v < numberOfValues; v++) {
        		classWeights[l] += weightCounts[v][l];
    		}
    	}
    	
    	double totalClassWeight = 0.0d;
    	for (double w : classWeights) {
    		totalClassWeight += w;
    	}
    	
    	double classEntropy = 0.0d;
		for (int l = 0; l < numberOfLabels; l++) {
            if (classWeights[l] > 0) {
                double proportion = classWeights[l] / totalClassWeight;
                classEntropy -= (Math.log(proportion) * LOG_FACTOR) * proportion;
            }
		}
		
    	// calculate and return information gain
    	double informationGain = classEntropy - information;
    	if (informationGain < minimalGain * classEntropy) {
    		informationGain = 0;
    	}
    	return informationGain;
    }
    
    protected double getEntropy(double[] labelWeights, double totalWeight) {
        double entropy = 0;
        for (int i = 0; i < labelWeights.length; i++) {
            if (labelWeights[i] > 0) {
                double proportion = labelWeights[i] / totalWeight;
                entropy -= (Math.log(proportion) * LOG_FACTOR) * proportion;
            }
        }
        return entropy;
    }
    
    @Override
	public boolean supportsIncrementalCalculation() {
    	return true;
    }
    
	@Override
	public double getIncrementalBenefit() {
		 double totalEntropy = getEntropy(totalLabelWeights, totalWeight);
		 double gain = getEntropy(leftLabelWeights, leftWeight) * leftWeight / totalWeight;
		 gain += getEntropy(rightLabelWeights, rightWeight) * rightWeight / totalWeight;
		 return totalEntropy - gain;
	}
}