import "../libraries/arff/common.eol"; import "../libraries/decent/common.eol"; import "../libraries/decent/logging.eol"; "Running addlabels".log(1); var start = Native("java.lang.System").currentTimeMillis(); var s = ","; var targetAttributes = new Map; //TODO: export options targetAttributes.put("Artifact.Target.BugFix.AverageWeight",0.1); /* targetAttributes.put("Artifact.Target.Refactoring.AverageWeight",0.1); targetAttributes.put("Artifact.Target.Fix.AverageWeight",0.1); targetAttributes.put("Artifact.Target.IssueCount.AverageWeight",0.1); targetAttributes.put("Artifact.Target.IssueReference.AverageWeight",0.1); targetAttributes.put("Artifact.Target.UsersPerIssue.AverageWeight",0.1); targetAttributes.put("Artifact.Target.CommentsPerIssue.AverageWeight",0.1); targetAttributes.put("Artifact.Target.BugFix.Shared.AverageWeight",0.1); targetAttributes.put("Artifact.Target.Refactoring.Shared.AverageWeight",0.1); targetAttributes.put("Artifact.Target.Fix.Shared.AverageWeight",0.1); targetAttributes.put("Artifact.Target.IssueReference.Shared.AverageWeight",0.1); targetAttributes.put("Artifact.Target.BugFix.Churn.AverageWeight",0.1); targetAttributes.put("Artifact.Target.Refactoring.Churn.AverageWeight",0.1); targetAttributes.put("Artifact.Target.Fix.Churn.AverageWeight",0.1); targetAttributes.put("Artifact.Target.IssueReference.Churn.AverageWeight",0.1); targetAttributes.put("Artifact.Target.BugFix.Size.AverageWeight",0.1); targetAttributes.put("Artifact.Target.Refactoring.Size.AverageWeight",0.1); targetAttributes.put("Artifact.Target.Fix.Size.AverageWeight",0.1); targetAttributes.put("Artifact.Target.IssueReference.Size.AverageWeight",0.1); */ //does not work with binary resources //var modelFile = new Native("java.io.File") (ARFFx.getModelFile()); var modelFile = new Native("java.io.File") (ARFFx.getModelImpl().getURI().toString().replaceAll("^file:","")); //var CONFIDENCE = "CONFIDENCE".getARFFAttribute(); //var LABEL = "LABEL".getARFFAttribute(); //TODO: move to common //TODO: remove once established at earlier steps var nestedAnonymousClassFilter = "\"[\\w]+\\$[\\d]+.*\""; var threshold = 100; //ARTIFACTS for (arffx in ARFFx!Model.allInstances().select(x|x.data.size() > 0)) { arffx.checkTargetAttributes(targetAttributes); arffx.checkForCompleteness(); //these will be recalculated for the bags.. arffx.setConfidenceThresholds(targetAttributes); arffx.assignClassAndConfidence(targetAttributes); } var end = Native("java.lang.System").currentTimeMillis(); var duration = end - start; ("Duration: "+duration.toMinutes().round(5)).log(1); operation ARFFx!Model checkTargetAttributes(targetAttributes : Map) { var notFound = targetAttributes.keySet().select(x|not self.attributes.exists(a|a.name = x)); for (a in notFound) { targetAttributes.remove(a); } } //slow? operation ARFFx!Model assignClassAndConfidence(targetAttributes : Map) { for (baseAttribute in targetAttributes.keySet()) { for (i in self.data) { i.assignClassAndConfidence(baseAttribute, targetAttributes.get(baseAttribute)); } } } operation ARFFx!Model setConfidenceThresholds(targetAttributes : Map) { //self.name.println(); //TODO: store as meta data //TODO: store mean divisor as meta-data for (a in targetAttributes.keySet) { //(" "+a +" -> "+ targetAttributes.get(a)).println(); var v = self.data.collect(x|x.getValue(a.getARFFAttribute(self)).asDouble()); //v = v.normalizeMinMax(0.asDouble(), 1.asDouble()); //(" "+v.getMin()+" : "+v.getMax()+" : "+v.getMean()+" : "+v.getVariance()+" : "+v.getStandardDeviation()).println(); var t = (v.getMean()/2).round(4); //(" "+a +" -> "+ t).println(); targetAttributes.put(a,t); //(" Non-zero:\t\t"+v.select(x|x <> 0).size()).println(); //(" Above threshold:\t"+v.select(x|x > t).size()).println(); } //" updated".println(); //(" "+targetAttributes).println(); } operation Collection setConfidenceThresholds(targetAttributes : Map) { //TODO: store as meta data //TODO: store mean divisor as meta-data for (a in targetAttributes.keySet) { var v = new Sequence(); for (arffx in self) { v.addAll(arffx.data.collect(x|x.getValue(a.getARFFAttribute(arffx)).asDouble())); } //v = v.normalizeMinMax(0.asDouble(), 1.asDouble()); //(" "+v.getMin()+" : "+v.getMax()+" : "+v.getMean()+" : "+v.getVariance()+" : "+v.getStandardDeviation()).println(); var t = (v.getMean()/2).round(4); targetAttributes.put(a,t); //(" Non-zero:\t\t"+v.select(x|x <> 0).size()).println(); //(" Above threshold:\t"+v.select(x|x > t).size()).println(); } } operation ARFFx!Model checkForCompleteness() { var line = 1; var NameAttribute = "Artifact.Name".getARFFAttribute(self); for (i in self.data.select(x|not x.getValue(NameAttribute).matches(nestedAnonymousClassFilter))) { //for (i in self.data) { line = line+1; if (i.values.size() <> self.attributes.size()) { (self.name+" : Line "+line+" : Value and attribute counts do not match : " + i.values.size() +" vs "+ self.attributes.size()).log(1); (i.getValues(s).substring(1)).log(1); i.printMissingAttributes(); } } } operation ARFFx!Instance printMissingAttributes() { for (a in self.eContainer.attributes) { if (not self.values.exists(v|v.ofAttribute = a)) { (" Missing attribute: "+a.name).log(1); } } } operation ARFFx!Instance assignClassAndConfidence(baseAttribute : String, threshold : Real) : OrderedSet { var confidenceAttribute = "CONFIDENCE."+baseAttribute; var labelAttribute = "LABEL."+baseAttribute; //TODO: add attributes to filter var base = self.getValue(baseAttribute.getARFFAttribute(self.eContainer())); var label = "false"; var confidence = "high"; if (base.asDouble() > threshold) { label = "true"; } //TODO: also export as parameters if (base.asDouble() < 1.01*threshold and base.asDouble() > 0.09*threshold) { confidence = "low"; } self.updateValue(confidence, confidenceAttribute); self.updateValue(label, labelAttribute); } operation ARFFx!Instance getValues(s : String) : String { var line = ""; for (v in self.values) { line = line + s + v.content; //line = line + s + v.ofAttribute.name+"="+v.content; } return line.replace("NaN","0.0").substring(0); //Substring? why? }