// Copyright 2016 Georg-August-Universität Göttingen, Germany
//
//   Licensed under the Apache License, Version 2.0 (the "License");
//   you may not use this file except in compliance with the License.
//   You may obtain a copy of the License at
//
//       http://www.apache.org/licenses/LICENSE-2.0
//
//   Unless required by applicable law or agreed to in writing, software
//   distributed under the License is distributed on an "AS IS" BASIS,
//   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//   See the License for the specific language governing permissions and
//   limitations under the License.


package de.ugoe.cs.cpdp.dataselection;

import java.util.Collections;
import java.util.LinkedList;

import org.apache.commons.collections4.list.SetUniqueList;
import org.apache.commons.math3.stat.descriptive.rank.Median;
import org.apache.commons.math3.util.MathArrays;

import de.ugoe.cs.cpdp.dataprocessing.MORPH;
import de.ugoe.cs.cpdp.util.WekaUtils;
import weka.core.Instance;
import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.supervised.instance.Resample;

/**
 * <p>
 * Implements LACE2 data privacy filter after Peters et al.
 * </p>
 * 
 * @author Steffen Herbold
 */
public class LACE2 implements ISetWiseDataselectionStrategy {

    private double percentage = 0.10;
    
    @Override
    public void setParameter(String parameters) {
        if( parameters!=null && !parameters.isEmpty()) {
            percentage = Double.parseDouble(parameters);
        }
    }

    @Override
    public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
        Instances selectedData = new Instances(testdata);
        selectedData.clear();
        
        LinkedList<Instances> traindataCopy = new LinkedList<>(traindataSet);
        Collections.shuffle(traindataCopy);
        
        CLIFF cliff = new CLIFF();
        cliff.setParameter(Double.toString(percentage));
        MORPH morph = new MORPH();
        Median median = new Median();
        double minDist = Double.MIN_VALUE;
        
        for( Instances traindata : traindataCopy ) {
            Instances cliffedData = cliff.applyCLIFF(traindata);
            if( minDist==Double.MIN_VALUE ) {
                // determine distance for leader-follower algorithm
                Instances sample;
                if( traindata.size()>100 ) {
                    Resample resample = new Resample();
                    resample.setSampleSizePercent(100.0/traindata.size()*100.0);
                    resample.setBiasToUniformClass(0.0);
                    resample.setNoReplacement(true);
                    try {
                        resample.setInputFormat(traindata);
                        sample = Filter.useFilter(traindata, resample);
                    }
                    catch (Exception e) {
                        throw new RuntimeException(e);
                    }
                } else {
                    sample = new Instances(traindata);
                }
                double[] distances = new double[sample.size()];
                for( int i=0; i<sample.size(); i++ ) {
                    Instance unlikeNeighbor = morph.getNearestUnlikeNeighbor(sample.get(i), sample);
                    distances[i] = MathArrays.distance(WekaUtils.instanceValues(sample.get(i)), WekaUtils.instanceValues(unlikeNeighbor));
                }
                minDist = median.evaluate(distances);
            }
            for( int i=0; i<cliffedData.size(); i++ ) {
                Instance unlikeNeighbor = morph.getNearestUnlikeNeighbor(cliffedData.get(i), selectedData);
                if( unlikeNeighbor==null ) {
                    selectedData.add(cliffedData.get(i));
                } else {
                    double distance = MathArrays.distance(WekaUtils.instanceValues(cliffedData.get(i)), WekaUtils.instanceValues(unlikeNeighbor));
                    if( distance>minDist ) {
                        morph.morphInstance(cliffedData.get(i), cliffedData);
                        selectedData.add(cliffedData.get(i));
                    }
                }
            }
        }
    }
    
}
