def produceScore(self, analyzedDatasets: Dataset,
currentScore: ClassificationResults,
completeDataset: Dataset, oa: OperatorAssignment,
candidateAttribute) -> float:
if candidateAttribute != None:
analyzedDatasets.addColumn(candidateAttribute)
evaluationResults = self.runClassifier(
Properties.classifier, analyzedDatasets.generateSet(True),
analyzedDatasets.generateSet(False))
# in order to deal with multi-class datasets we calculate an average of all AUC scores (we may need to make this weighted)
auc = self.CalculateAUC(evaluationResults, analyzedDatasets,
evaluationResults.actualPred)
if currentScore != None:
return auc - currentScore.getAuc()
else:
return auc
def GenerateAndAddColumnToDataset(self, dataset: Dataset,
oaList: List[OperatorAssignment]):
for oa in oaList:
ci = OperatorsAssignmentsManager.generateColumn(dataset, oa, True)
dataset.addColumn(ci)
