Currently, the implementation of SimpleDetectorAggregator does not allow for novelty detection usages.
The method _create_scores(self, X) does apply standardization based on the tensor of scores X. The data evaluated for novelty detection are not transformed the same way as the data used to fit the SimpleDetectorAggregator, and thus the threshold defined at fitting can not be applied to determine wether or not the data is a novelty.
One notable consequence is that running SimpleDetectorAggregator(...).predict(X[0, :]) (novelty detection on a single point) will always output the a score of 0.
SimpleDetectorAggregator should instead keep the scalers used when fitting and use them to process the data when creating new scores. This would add support for novelty detection.
Let me know if I am missing something as I am new to combo. If some help is wanted, I might have bandwidth to contribute to this cool project !
Currently, the implementation of
SimpleDetectorAggregatordoes not allow for novelty detection usages.The method
_create_scores(self, X)does apply standardization based on the tensor of scores X. The data evaluated for novelty detection are not transformed the same way as the data used to fit theSimpleDetectorAggregator, and thus the threshold defined at fitting can not be applied to determine wether or not the data is a novelty.One notable consequence is that running
SimpleDetectorAggregator(...).predict(X[0, :])(novelty detection on a single point) will always output the a score of 0.SimpleDetectorAggregatorshould instead keep the scalers used when fitting and use them to process the data when creating new scores. This would add support for novelty detection.Let me know if I am missing something as I am new to combo. If some help is wanted, I might have bandwidth to contribute to this cool project !