https://github.com/mir-group/flare/blob/07a6ff51a26c382eb67d944aa0053d816392c29e/flare/learners/otf.py#L352C1-L359C18
Hi,
I was looking at the environment selection during an OTF step. In this section of code from the OTF learner, the environment selection is performed by is_std_in_bound for regular on-the-fly active learning. The function gets as argument the force noise hyperparameter and multiplies the threshold for its value (https://github.com/mir-group/flare/blob/07a6ff51a26c382eb67d944aa0053d816392c29e/flare/learners/utils.py#L50C1-L56C42) - however, if the variance_type is "local", the variance is already normalized wrt the kernel prefactor, and this would result in a (generally) lower threshold which is also depending on the hyperparameters - with, on a side note, no connection between the local uncertainty on environments and the force noise hyperparameter. Is this wanted/taken care of somewhere (e.g. should the thresholds always be set to negative values), or it is a problem in the code?
Thank you in advance.
https://github.com/mir-group/flare/blob/07a6ff51a26c382eb67d944aa0053d816392c29e/flare/learners/otf.py#L352C1-L359C18
Hi,
I was looking at the environment selection during an OTF step. In this section of code from the OTF learner, the environment selection is performed by is_std_in_bound for regular on-the-fly active learning. The function gets as argument the force noise hyperparameter and multiplies the threshold for its value (https://github.com/mir-group/flare/blob/07a6ff51a26c382eb67d944aa0053d816392c29e/flare/learners/utils.py#L50C1-L56C42) - however, if the variance_type is "local", the variance is already normalized wrt the kernel prefactor, and this would result in a (generally) lower threshold which is also depending on the hyperparameters - with, on a side note, no connection between the local uncertainty on environments and the force noise hyperparameter. Is this wanted/taken care of somewhere (e.g. should the thresholds always be set to negative values), or it is a problem in the code?
Thank you in advance.