Comments (3)
Hi @Justin-Tan,
The simplest computation is here:
Lines 600 to 617 in 0ef7e34
Basically we first get the mass_pass
and mass_fail
numpy arrays of mass values. These are turned into binned, normalized mass distributions spec_ohe_pass_sum
and spec_ohe_fail_sum
.
We take the average M
of these two distributions. Then we compute the two KL divergences and average them to get the JS divergence.
from in.
Thanks for the response! That makes sense, I didn't realize the scipy.stats.entropy
function calculates the relative entropy, though its obvious in hindsight ... :\
from in.
By the way, did you find the metric sensitive to the choice of binning at all?
from in.
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