How to add f1_macro as a metric for multi-class classification?

[tiefenthaler]

I want to use f1_score(average='macro') as a metric for multi-class classification.
In the documentation we can add metrics like this:
from sklearn.metrics import log_loss
add_metric('logloss', 'Log Loss', log_loss, greater_is_better = False)
This does not work for sklearn.metrics.f1_score when I want to use f1_score(average='macro'). Since setting f1_score to 'macro' does call the method. So I can only add the plain f1_score as a metric to the setup. But plan f1_score has a 'weighted' average as default.
The compare_models method where we actually call the scoring does not provide any settings to configure our metrics. I wonder if there is any easy way to use f1_score(average='macro')? Of course I can implement a custom f1_score_macro metric that uses 'macro' as default, but it would be nice to be able to use the sklearn implementation. Also to mention that sklearn metric are using parent classes which would make it some effort to translate them to a custom metric.