'monitor' is ignored to compute the score

[AlessandroLovo]

In the function ln.train_model the score of a fold is returned as

score = np.min(history[return_metric])

This means that is monitor != return_metric we early stop according to monitor, but still take the minimum wrt return_metric. So early stopping doesn't make much sense.

A better solution would be

score = history[return_metric][np.argmin(history[monitor])]

This may complicate matters with optimal_chekpoint, as there to have consistency one should have metric == monitor instead of metric == return_metric

[AlessandroLovo]

The confusion free scenario is when metric == return_metric == monitor. Outside this condition things could get messy