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package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
import java.util.HashMap
import java.util.HashSet
import java.util.List
import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
class KSDistance extends CostDistance {
var static ksTester = new KolmogorovSmirnovTest();
var MetricSampleGroup g;
new(MetricSampleGroup g){
this.g = g;
}
override double mpcDistance(List<Double> samples){
//if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
if(samples.size < 2) return 1;
return ksTester.kolmogorovSmirnovStatistic(g.mpcSamples, samples);
}
override double naDistance(List<Double> samples){
//if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
if(samples.size < 2) return 1;
return ksTester.kolmogorovSmirnovStatistic(g.naSamples as double[], samples);
}
override double outDegreeDistance(List<Double> samples){
//if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
if(samples.size < 2) return 1;
return ksTester.kolmogorovSmirnovStatistic(g.outDegreeSamples, samples);
}
def double typedOutDegreeDistance(HashMap<String, List<Integer>> map){
var value = 0.0;
// map list to array
val keySet = new HashSet<String>(map.keySet);
keySet.addAll(g.typedOutDegreeSamples.keySet);
for(key : keySet){
if(!map.containsKey(key) ){
value += 1;
}else if(!g.typedOutDegreeSamples.containsKey(key)){
value += map.get(key).size * 100;
}else{
var double[] rep = g.typedOutDegreeSamples.get(key).stream().mapToDouble([it|it]).toArray();
var double[] ins = map.get(key).stream().mapToDouble([it|it]).toArray();
if((rep.size < 2 || ins.size < 2) ){
if(rep.size < 2 && rep.containsAll(ins)){
value += 0;
}else{
value += 1;
}
}else if(rep.size >= 2 && ins.size >= 2){
value += ksTester.kolmogorovSmirnovStatistic(rep, ins);
}
}
}
return value;
}
def nodeTypeDistance(HashMap<String, Double> samples){
var typesDistMap = g.nodeTypeSamples;
var sourceDist = newArrayList();
var instanceDist = newArrayList();
for(key : typesDistMap.keySet()){
sourceDist.add(typesDistMap.get(key));
instanceDist.add(samples.getOrDefault(key, 0.0));
}
return ks_distance_two_dist(sourceDist, instanceDist);
}
def edgeTypeDistance(HashMap<String, Double> samples){
var typesDistMap = g.edgeTypeSamples;
var sourceDist = newArrayList();
var instanceDist = newArrayList();
for(key : typesDistMap.keySet()){
sourceDist.add(typesDistMap.get(key));
instanceDist.add(samples.getOrDefault(key, 0.0));
}
return ks_distance_two_dist(sourceDist, instanceDist);
}
def double ks_distance_two_dist(List<Double> dist1, List<Double> dist2){
// Since we already know the pdf, we compute the ks-test manully
var ksStatistics = 0.0;
var sum1 = 0.0;
var sum2 = 0.0;
for(var i = 0; i < dist1.size(); i++){
sum1 += dist1.get(i);
sum2 += dist2.get(i);
ksStatistics = Math.max(ksStatistics, Math.abs(sum1 - sum2));
}
return ksStatistics;
}
}
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