You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Thanks for the wonderful tool. I'm testing the library by following the tutorial with some of my own prediction models. I noticed that the text explanation is a filtered list of the descriptors displayed on the images. Based on the code, part of the filter is a check on the presence_threshold. What is the logic behind this check? From the code one can infer that in the case where present=nbases=1, the condition "if present / nbases < (1 - presence_thresh) and v < 0" will always fail for negatively contributing descriptors with finite presence_thresh, which means negatively contributing descriptors will always be excluded from the text description. Is there a rational supporting such exclusion?
Thanks for the wonderful tool. I'm testing the library by following the tutorial with some of my own prediction models. I noticed that the text explanation is a filtered list of the descriptors displayed on the images. Based on the code, part of the filter is a check on the presence_threshold. What is the logic behind this check? From the code one can infer that in the case where present=nbases=1, the condition "if present / nbases < (1 - presence_thresh) and v < 0" will always fail for negatively contributing descriptors with finite presence_thresh, which means negatively contributing descriptors will always be excluded from the text description. Is there a rational supporting such exclusion?
https://github.com/ur-whitelab/exmol/blob/67873897bbcd2deb60c69a5116bc7feea69fdefa/exmol/exmol.py#L1462C1-L1475C21
The text was updated successfully, but these errors were encountered: