Use MicroAverage to aggregate MMEs per ptoject

ArDoCo · Aug 20, 2024 · 9b3f3aa · 9b3f3aa
1 parent 0ff067c
commit 9b3f3aa
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Showing 2 changed files with 11 additions and 1 deletion.
diff --git a/...a/edu/kit/kastel/mcse/ardoco/core/tests/eval/results/calculator/ResultCalculatorUtil.java b/...a/edu/kit/kastel/mcse/ardoco/core/tests/eval/results/calculator/ResultCalculatorUtil.java
@@ -42,6 +42,15 @@ public static <T> EvaluationResults<T> calculateWeightedAverageResults(Immutable
         return evaluationResults(macroAverage);
     }
 
+    public static EvaluationResults<String> calculateMicroAverageResults(ImmutableList<EvaluationResults<String>> results) {
+        var averages = getAverages(results);
+        if (averages == null)
+            return null;
+
+        var microAverage = averages.stream().filter(it -> it.getType() == AggregationType.MICRO_AVERAGE).findFirst().orElseThrow();
+        return evaluationResults(microAverage);
+    }
+
     private static <T> EvaluationResults<T> evaluationResults(AggregatedClassificationResult average) {
         var weightedAverageAsSingle = new SingleClassificationResult<T>(Sets.mutable.empty(), Sets.mutable.empty(), Sets.mutable.empty(), null, average
                 .getPrecision(), average.getRecall(), average.getF1(), average.getAccuracy(), average.getSpecificity(), average.getPhiCoefficient(), average
@@ -61,4 +70,5 @@ private static <T> List<AggregatedClassificationResult> getAverages(ImmutableLis
 
         return calculator.calculateAverages(classifications, null);
     }
+
 }
diff --git a/...a/edu/kit/kastel/mcse/ardoco/id/tests/integration/InconsistencyDetectionEvaluationIT.java b/...a/edu/kit/kastel/mcse/ardoco/id/tests/integration/InconsistencyDetectionEvaluationIT.java
@@ -116,7 +116,7 @@ protected void runMissingModelElementInconsistencyEval(GoldStandardProject goldS
 
         OVERALL_MME_RESULTS.addAll(results);
 
-        EvaluationResults<String> weightedResults = ResultCalculatorUtil.calculateWeightedAverageResults(results.toImmutable());
+        EvaluationResults<String> weightedResults = ResultCalculatorUtil.calculateMicroAverageResults(results.toImmutable());
 
         MME_RESULTS.put(goldStandardProject, Tuples.pair(weightedResults, expectedInconsistencyResults));
         logResultsMissingModelInconsistency(goldStandardProject, weightedResults, expectedInconsistencyResults);