aamini / chemprop Goto Github PK
View Code? Open in Web Editor NEWFast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
License: MIT License
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
License: MIT License
In the CalibrationAUC, the standard_devs are defined by:
standard_devs = [np.abs(set_['error'])/set_['confidence'] for set_ in data['sets_by_confidence']]
I am confused about the set_['confidence']
here because the confidence is calculated by 1. / ((alphas-1) * lambdas)
as in the "predict.py" file, while this value is not the square root of the variance of mean values (betas / ((alphas-1) * lambdas)
.
By the way, I also noticed that the confidence calculated here is different from the uncertainty defined in Figure 2B of your paper, may I ask why using this metric (1. / ((alphas-1) * lambdas)
) to evaluate confidence (or uncertainty).
In your repository of "evidential-deep-learning", I found the calibration plot is drawn with the standard deviation betas / ((alphas-1) * lambdas)
, and the confidence is also measured by this value. I wonder why it changes in these two repositories.
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