Is it possible to integrate a metric of imblearn as a scorer?

[moelliDo]

I would like to use the geometric mean as a metric to optimize classification models trained with the fit() method.
I thought that the autosklearn.metrics.make_scorer() would allow to define the geometric mean as a scorer like:
classifier.fit(X_train, y_train, feat_type=feat_type, metric=autosklearn.metrics.make_scorer("gm", imblearn.metrics.geometric_mean_score))
as the imblearn-package is fully compatible with sklearn.

However, after the model has been fitted, the sprint statistics indicate, that the definition as I did it does not seem to be working:

auto-sklearn results:
Dataset name: 6b31930a65e59cca700a5844fbab91a0
Metric: gm
Best validation score: 0.000000
Number of target algorithm runs: 187
Number of successful target algorithm runs: 82
Number of crashed target algorithm runs: 74
Number of target algorithms that exceeded the time limit: 18
Number of target algorithms that exceeded the memory limit: 13

Is it somehow possible to define the geometric mean as a metric to optimize the model?

[github-actions]

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