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package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.EuclideanDistance
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.JSDistance
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.KSDistance
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.StateData
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.PartialInterpretationGraph
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.RepMetricsReader
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeTypeMetric
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.predictor.LinearModel
import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.PartialInterpretation
import java.util.ArrayList
import java.util.HashMap
import java.util.List
import java.util.Map
import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression
import org.eclipse.xtend.lib.annotations.Accessors
class PartialInterpretationMetricDistance {
var KSDistance ks;
var JSDistance js;
var EuclideanDistance ed;
var Map<Object, StateData> stateAndHistory;
var OLSMultipleLinearRegression regression;
List<StateData> samples;
var MetricSampleGroup g;
@Accessors(PUBLIC_GETTER)
var LinearModel linearModel;
new(){
var metrics = RepMetricsReader.read(Domain.Yakinduum);
this.g = metrics;
ks = new KSDistance(g);
js = new JSDistance(g);
ed = new EuclideanDistance(g);
regression = new OLSMultipleLinearRegression();
regression.noIntercept = false;
stateAndHistory = new HashMap<Object, StateData>();
samples = new ArrayList<StateData>();
linearModel = new LinearModel(0.01);
}
def MetricDistanceGroup calculateMetricDistanceKS(PartialInterpretation partial){
val metrics = new ArrayList<Metric>();
metrics.add(new OutDegreeMetric());
metrics.add(new NodeActivityMetric());
metrics.add(new MultiplexParticipationCoefficientMetric());
metrics.add(new NodeTypeMetric());
val metricCalculator = new PartialInterpretationGraph(partial, metrics, null);
var metricSamples = metricCalculator.evaluateAllMetricsToSamples();
var mpc = ks.mpcDistance(metricSamples.mpcSamples);
var na = ks.naDistance(metricSamples.naSamples);
var outDegree = ks.outDegreeDistance(metricSamples.outDegreeSamples);
var nodeType = ks.nodeTypeDistance(metricSamples.nodeTypeSamples);
//var typedOutDegree = ks.typedOutDegreeDistance(metricSamples.typedOutDegreeSamples);
var distance = new MetricDistanceGroup(mpc, na, outDegree, nodeType);
distance.nodeTypeInfo = metricSamples.nodeTypeSamples;
return distance;
}
def MetricDistanceGroup calculateMetricEuclidean(PartialInterpretation partial){
val metrics = new ArrayList<Metric>();
metrics.add(new OutDegreeMetric());
metrics.add(new NodeActivityMetric());
metrics.add(new MultiplexParticipationCoefficientMetric());
val metricCalculator = new PartialInterpretationGraph(partial, metrics, null);
var metricSamples = metricCalculator.evaluateAllMetricsToSamples();
var mpc = ed.mpcDistance(metricSamples.mpcSamples);
var na = ed.naDistance(metricSamples.naSamples);
var outDegree = ed.outDegreeDistance(metricSamples.outDegreeSamples);
return new MetricDistanceGroup(mpc, na, outDegree);
}
def MetricDistanceGroup calculateMetricDistance(PartialInterpretation partial){
val metrics = new ArrayList<Metric>();
metrics.add(new OutDegreeMetric());
metrics.add(new NodeActivityMetric());
metrics.add(new MultiplexParticipationCoefficientMetric());
val metricCalculator = new PartialInterpretationGraph(partial, metrics, null);
var metricSamples = metricCalculator.evaluateAllMetricsToSamples();
var mpc = js.mpcDistance(metricSamples.mpcSamples);
var na = js.naDistance(metricSamples.naSamples);
var outDegree = js.outDegreeDistance(metricSamples.outDegreeSamples);
return new MetricDistanceGroup(mpc, na, outDegree);
}
def resetRegression(Object state){
samples.clear();
if(stateAndHistory.containsKey(state)){
var data = stateAndHistory.get(state);
var curState = state;
samples.add(data);
while(stateAndHistory.containsKey(data.lastState) && data.lastState != curState){
curState = data.lastState;
data = stateAndHistory.get(data.lastState);
samples.add(data);
}
if(samples.size == 0){
println('state: ' + state);
println('last state: ' + data.lastState);
}
}
println("trajectory sample size:" + samples.size)
}
def feedData(Object state, double[] features, double value, Object lastState){
var data = new StateData(features, value, lastState);
stateAndHistory.put(state, data);
samples.add(data);
}
def getPredictionForNextDataSample(double[] features, double value, double[] featuresToPredict){
if(samples.size <= 4){
println('OK');
}
var data = new StateData(features, value, null);
samples.add(data);
// create training set from current data
var double[][] xSamples = samples.map[it.features];
var double[] ySamples = samples.map[it.value];
regression.newSampleData(ySamples, xSamples);
var prediction = predict(featuresToPredict);
//remove the last element just added
samples.remove(samples.size - 1);
return prediction;
}
def private predict(double[] featuresToPredict){
var parameters = regression.estimateRegressionParameters();
// the regression will add an initial column for 1's, the first parameter is constant term
var result = parameters.get(0);
for(var i = 0; i < featuresToPredict.length; i++){
result += parameters.get(i+1) * featuresToPredict.get(i);
}
return result;
}
def double[] calculateFeature(int step, int violations){
var features = newDoubleArrayOfSize(2);
//constant term
features.set(0, 1);
features.set(0, Math.sqrt(step) + 30)
features.set(1, 1.0 / (step + 30) );
// features.set(2, violations);
// features.set(3, Math.pow(violations, 2));
return features;
}
}
class MetricDistanceGroup{
var double mpcDistance;
var double naDistance;
var double outDegreeDistance;
var double nodeTypeDistance;
protected var HashMap<String, Double> nodeTypeInfo;
new(double mpcDistance, double naDistance, double outDegreeDistance, double nodeTypeDistance){
this.mpcDistance = mpcDistance;
this.naDistance = naDistance;
this.outDegreeDistance = outDegreeDistance;
this.nodeTypeDistance = nodeTypeDistance;
}
new(double mpcDistance, double naDistance, double outDegreeDistance){
this.mpcDistance = mpcDistance;
this.naDistance = naDistance;
this.outDegreeDistance = outDegreeDistance;
}
def double getNodeTypeDistance(){
return this.nodeTypeDistance;
}
def double getMPCDistance(){
return this.mpcDistance
}
def double getNADistance(){
return this.naDistance
}
def double getOutDegreeDistance(){
return this.outDegreeDistance
}
def double getNodeTypePercentage(String typeName){
return nodeTypeInfo.getOrDefault(typeName, 0.0);
}
}
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