package mulan;

/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 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 General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

import java.io.FileReader;
import java.io.Serializable;
import java.util.HashMap;
import java.util.Set;

import weka.core.Instance;
import weka.core.Instances;
import weka.core.Utils;

/**
 <!-- globalinfo-start -->
 * Class for calculating statistics of a multilabel dataset <p>
 * <br/>
 * For more information, see<br/>
 * <br/>
 * G. Tsoumakas, I. Katakis (2007). Multi-Label Classification: An Overview. International Journal of Data Warehousing and Mining, 3(3):1-13.
 * </p>
 <!-- globalinfo-end -->
 * 
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;article{tsoumakas+katakis:2007,
 *    author = {G. Tsoumakas, I. Katakis},
 *    journal = {International Journal of Data Warehousing and Mining},
 *    pages = {1-13},
 *    title = {Multi-Label Classification: An Overview},
 *    volume = {3},
 *    number = {3},
 *    year = {2007}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -F &lt;filename&gt;
 *  The filename (including full path) of the multilabel data set).</pre>
 * 
 * <pre> -L &lt;number of labels&gt;
 *  Number of labels. </pre>
 * 
 <!-- options-end -->
 *
 * @author Grigorios Tsoumakas 
 * @author Robert Friberg
 * @version $Revision: 0.03 $ 
 */
public class Statistics implements Serializable
{    
    private static final long serialVersionUID = 1206845794397561633L;

    /** the number of instances */
    private int numInstances;
    
    /** the number of predictive attributes */
    private int numPredictors = 0;
    
    /** the number of nominal predictive attributes */
    private int numNominal = 0;
    
    /** the number of numeric attributes */
    private int numNumeric = 0;
    
    /** the number of labels */
    private int numLabels;
    
    /** the label density  */
    private double labelDensity;

    /** the label cardinality */
    private double labelCardinality;
        
    /** percentage of instances per label */
    private double[] examplesPerLabel;
    
    /** number of examples per cardinality, <br><br>
     *  note that this array has size equal to the number of elements plus one, <br>
     *  because the first element is the number of examples for cardinality=0  */
    private double[] cardinalityDistribution;
    
    /** labelsets and their frequency */
    private HashMap<LabelSet,Integer> labelsets;
    
    public Statistics()
    {
    }
    
    /** 
     * returns the HashMap containing the distinct labelsets and their frequencies
     */
    public HashMap<LabelSet,Integer> labelCombCount() {
    	return labelsets;
    }
    
    /** 
     * This method calculates and prints a matrix with the coocurrences of <br>
     * pairs of labels (under concstruction).
     */
    public double[][] calculateCoocurrence(Instances data, int labels) {
        double[][] coocurrenceMatrix = new double[labels][labels];
        
        int numPredictors = data.numAttributes()-labels;
        for (int k=0; k<data.numInstances(); k++) {
            Instance temp = data.instance(k);
            for (int i=0; i<labels; i++)
                for (int j=0; j<labels; j++) {
                    if (i >= j)
                        continue;
                    if (data.attribute(numPredictors+i).value((int) temp.value(numPredictors+i)).compareTo("1") == 0 && 
                        data.attribute(numPredictors+j).value((int) temp.value(numPredictors+j)).compareTo("1") == 0 )
                        coocurrenceMatrix[i][j]++;
                }
        }
        
        for (int i=0; i<labels; i++) {
            for (int j=0; j<labels; j++) {
                System.out.print(coocurrenceMatrix[i][j] + "\t");
            }
            System.out.println();
        }
        
        return coocurrenceMatrix;
    }    
       
    /** 
     * calculates various multilabel statistics, such as label cardinality, <br>
     * label density and the set of dinstinct labels along with their frequency
     */
    public void calculateStats(Instances data, int labels) {        
        // initialize statistics
        numLabels = labels;
        numPredictors = data.numAttributes()-numLabels;
        labelCardinality = 0;
        numNominal = 0;
        numNumeric = 0;
        examplesPerLabel = new double[numLabels];
        cardinalityDistribution = new double[numLabels+1];
        labelsets = new HashMap<LabelSet,Integer>();
             
        // gather statistics
        for (int i=0; i<numPredictors; i++) {
            if (data.attribute(i).isNominal())
                numNominal++;
            if (data.attribute(i).isNumeric())
                numNumeric++;
        }
        
        
        numInstances = data.numInstances(); 
        for (int i=0; i<numInstances; i++)
        {
            int exampleCardinality=0;
            double[] dblLabels = new double[numLabels];
            for (int j=0; j<numLabels; j++)
            {
            	double value = Double.parseDouble(data.attribute(numPredictors+j).value((int) data.instance(i).value(numPredictors + j))); 
                dblLabels[j] = value; 
                
                if (Utils.eq(value, 1.0))
                {
                    exampleCardinality++;
                    labelCardinality++;
                    examplesPerLabel[j]++;
                }
            }
            cardinalityDistribution[exampleCardinality]++;
            
            LabelSet labelSet = new LabelSet(dblLabels);
            if (labelsets.containsKey(labelSet))
            {
                labelsets.put(labelSet, labelsets.get(labelSet) + 1);
            }
            else labelsets.put(labelSet, 1);
        }
        
        labelCardinality /= numInstances;
        labelDensity = labelCardinality / numLabels;
        for (int j=0; j<numLabels; j++)
            examplesPerLabel[j] /= numInstances;        
    }
    
    /** 
     * returns various multilabel statistics in textual representation 
     */
    public String toString() {
        String description = "";
        
        description += "Examples: " + numInstances + "\n";
        description += "Predictors: " + numPredictors + "\n";
        description += "--Nominal: " + numNominal + "\n";
        description += "--Numeric: " + numNumeric + "\n";
        
        description += "Labels: " + numLabels + "\n";

        description += "\n";       
        description += "Cardinality: " + labelCardinality + "\n";
        description += "Density: " + labelDensity + "\n";
        description += "Distinct Labelsets: " + labelsets.size() +"\n";
        
        description += "\n";
        for (int j=0; j<numLabels; j++)
            description += "Percentage of examples with label " + (j+1) + ": " + examplesPerLabel[j] + "\n";        
        
        description += "\n";
        for (int j=0; j<=numLabels; j++)
            description += "Examples of cardinality " + j + ": " + cardinalityDistribution[j] + "\n";            	

        description += "\n";
        for(LabelSet set : labelsets.keySet())
            description += "Examples of combination " + set + ": " +	labelsets.get(set) + "\n";

        return description;
    }
           
    /** 
     * returns the prior probabilities of the labels
     */
    public double[] priors() {
        double[] pr = new double[numLabels];
        for (int i=0; i<numLabels; i++)
            pr[i] = examplesPerLabel[i]/numInstances;
        return pr;
    }

    /** 
     * returns the label cardinality of the dataset
     */
    public double cardinality() {
        return labelCardinality;
    }    
    
    /** 
     * returns the label density of the dataset
     */
    public double density() {
        return labelDensity;
    }

    /** 
     * returns a set with the distinct labelsets of the dataset
     */
    public Set<LabelSet> labelSets() {
        return labelsets.keySet();
    }
    
    /** 
     * returns the frequency of a labelset in the dataset
     */
    public int labelFrequency(LabelSet x) {
        return labelsets.get(x);
    }
    
    public static void main(String[] args) throws Exception {
        String filename = Utils.getOption('F', args);
        FileReader frData = new FileReader(filename);
        Instances data = new Instances(frData);                
        int numLabels = Integer.parseInt(Utils.getOption('L',args));
        
        Statistics stats = new Statistics();
        stats.calculateStats(data, numLabels);
        System.out.println(stats.toString());
    }
    
}