// Copyright 2015 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.dataprocessing;

import java.util.HashSet;
import java.util.LinkedList;
import java.util.List;
import java.util.Set;
import java.util.TreeSet;
import java.util.logging.Level;
import java.util.stream.IntStream;

import org.apache.commons.collections4.list.SetUniqueList;
import org.apache.commons.math3.stat.correlation.SpearmansCorrelation;

import com.google.common.collect.Sets;

import de.ugoe.cs.cpdp.util.SortUtils;
import de.ugoe.cs.util.console.Console;
import weka.attributeSelection.AttributeSelection;
import weka.attributeSelection.CfsSubsetEval;
import weka.attributeSelection.GreedyStepwise;
import weka.classifiers.trees.J48;
import weka.core.Instances;

/**
 * <p>
 * Implements the OPTTOPk filter after P. He et al. (2015).
 * </p>
 * 
 * @author Steffen Herbold
 */
public class TopMetricFilter implements ISetWiseProcessingStrategy {

    /**
     * Internally used correlation threshold.
     */
    double correlationThreshold = 0.5;
    
    /*
     * (non-Javadoc)
     * 
     * @see de.ugoe.cs.cpdp.IParameterizable#setParameter(java.lang.String)
     */
    @Override
    public void setParameter(String parameters) {
        if( parameters!=null && !parameters.equals("")) {
            correlationThreshold = Double.parseDouble(parameters);
        }
    }

    @Override
    public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
        try {
            determineTopKAttributes(testdata, traindataSet);
        }
        catch (Exception e) {
            Console.printerr("Failure during metric selection: " + e.getMessage());
            throw new RuntimeException(e);
        }
    }

    private void determineTopKAttributes(Instances testdata, SetUniqueList<Instances> traindataSet) throws Exception {
        Integer[] counts = new Integer[traindataSet.get(0).numAttributes()-1];
        IntStream.range(0,counts.length).forEach(val -> counts[val] = 0);
        for( Instances traindata : traindataSet ) {
            J48 decisionTree = new J48();
            decisionTree.buildClassifier(traindata);
            int k=0;
            for( int j=0; j<traindata.numAttributes(); j++) {
                if(j!=traindata.classIndex()){
                    if( decisionTree.toString().contains(traindata.attribute(j).name()) ) {
                        counts[k] = counts[k]+1;
                    }
                    k++;
                }
            }
        }
        int[] topkIndex = new int[counts.length];
        IntStream.range(0,counts.length).forEach(val -> topkIndex[val] = val);
        SortUtils.quicksort(counts, topkIndex, true);
        
        // get CFSs for each training set
        List<Set<Integer>> cfsSets = new LinkedList<>();
        for( Instances traindata : traindataSet ) {
            AttributeSelection attsel = new AttributeSelection();
            CfsSubsetEval eval = new CfsSubsetEval();
            GreedyStepwise search = new GreedyStepwise();
            search.setSearchBackwards(true);
            attsel.setEvaluator(eval);
            attsel.setSearch(search);
            attsel.SelectAttributes(traindata);
            Set<Integer> cfsSet = new HashSet<>();
            for( int attr : attsel.selectedAttributes() ) {
                cfsSet.add(attr);
            }
            cfsSets.add(cfsSet);
        }
        
        double[] coverages = new double[topkIndex.length];
        for( Set<Integer> cfsSet : cfsSets ) {
            Set<Integer> topkSet = new HashSet<>();
            for( int k=0; k<topkIndex.length ; k++ ) {
                topkSet.add(topkIndex[k]);
                coverages[k] += (coverage(topkSet, cfsSet)/traindataSet.size());
            }
        }
        double bestCoverageValue = Double.MIN_VALUE;
        int bestCoverageIndex = 0;
        for( int i=0; i<coverages.length; i++ ) {
            if( coverages[i]>bestCoverageValue) {
                bestCoverageValue = coverages[i];
                bestCoverageIndex = i;
            }
        }
        // build correlation matrix
        SpearmansCorrelation corr = new SpearmansCorrelation();
        double[][] correlationMatrix = new double[bestCoverageIndex][bestCoverageIndex];
        for( Instances traindata : traindataSet ) {
            double[][] vectors = new double[bestCoverageIndex][traindata.size()];
            for( int i=0; i<traindata.size(); i++ ) {
                for( int j=0; j<bestCoverageIndex; j++) {
                    vectors[j][i] = traindata.get(i).value(topkIndex[j]);
                }
            }
            for( int j=0; j<bestCoverageIndex; j++ ) {
                for( int k=j+1; k<bestCoverageIndex; k++ ) {
                    correlationMatrix[j][k] = Math.abs(corr.correlation(vectors[j], vectors[k]));
                }
            }
        }
        Set<Integer> topkSetIndexSet = new TreeSet<>();
        for( int j=0; j<bestCoverageIndex; j++ ) {
            topkSetIndexSet.add(j);
        }
        Set<Set<Integer>> allCombinations = Sets.powerSet(topkSetIndexSet);
        double bestOptCoverage = Double.MIN_VALUE;
        Set<Integer> opttopkSetIndexSet = null;
        for( Set<Integer> combination : allCombinations ) {
            if( isUncorrelated(correlationMatrix, combination) ) {
                double currentCoverage = 0.0;
                Set<Integer> topkCombination = new TreeSet<>();
                for( Integer index : combination ) {
                    topkCombination.add(topkIndex[index]);
                }
                for( Set<Integer> cfsSet : cfsSets ) {
                    currentCoverage += (coverage(combination, cfsSet)/traindataSet.size());
                }
                if( currentCoverage > bestOptCoverage ) {
                    bestOptCoverage = currentCoverage;
                    opttopkSetIndexSet = combination;
                }
            }
        }
        Set<Integer> opttopkIndex = new TreeSet<>();
        for( Integer index : opttopkSetIndexSet) {
            opttopkIndex.add(topkIndex[index]);
        }
        Console.traceln(Level.FINE, "selected the following metrics:");
        for( Integer index : opttopkIndex) {
            Console.traceln(Level.FINE, traindataSet.get(0).attribute(index).name());
        }
        // finally remove attributes
        for( int j=testdata.numAttributes()-1; j>=0; j-- ) {
            if( j!=testdata.classIndex() && !opttopkIndex.contains(j) ) {
                testdata.deleteAttributeAt(j);
                for( Instances traindata : traindataSet ) {
                    traindata.deleteAttributeAt(j);
                }
            }
        }
    }
    
    private boolean isUncorrelated(double[][] correlationMatrix, Set<Integer> combination) {
        Integer[] intCombination = combination.toArray(new Integer[0]);
        boolean areUncorrelated = true;
        for( int i=0 ; areUncorrelated && i<intCombination.length ; i++ ) {
            for( int j=i+1; areUncorrelated && j<intCombination.length ; j++ ) {
                areUncorrelated &= correlationMatrix[intCombination[i]][intCombination[j]]>correlationThreshold;
            }
        }
        return areUncorrelated;
    }
    
    private double coverage(Set<Integer> topkSet, Set<Integer> cfsSet) {
        Set<Integer> topkSetCopy1 = new HashSet<>(topkSet);
        topkSetCopy1.retainAll(cfsSet);
        Set<Integer> topkSetCopy2 = new HashSet<>(topkSet);
        topkSetCopy2.addAll(cfsSet);
        return ((double) topkSetCopy1.size())/topkSetCopy2.size();
    }
}
