make empirical accuracy calculation more robust #109
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This pull request addresses [this issue](#106), which was originally pointed out by @fc-jian. The key point is that the computational of the empirical accuracy (`consensus.empirical_accuracy`) ran into numerical issues if the numbers were large as it involved computing very large numbers and then taking their logs. In #106, @fc-jian originally proposed using Stirling's approximation, and made a draft pull request #108 to fix that. However, in looking more I discovered that the built-in python `gammaln` function is even a better way to do this. I also updated the docs to describe the new math being done. This pull request therefore solves #106 and is in lieu of #108, as I think it is a better solution. @fc-jian, thanks so much for noting and flagging all of this!
This was referenced Oct 24, 2025
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Thanks for the discussion and fixing. Sorry I am not familiar with the format requirements of this repo haha. |
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This pull request addresses this issue, which was originally pointed out by @fc-jian.
The key point is that the computational of the empirical accuracy (
consensus.empirical_accuracy) ran into numerical issues if the numbers were large as it involved computing very large numbers and then taking their logs. In #106, @fc-jian originally proposed using Stirling's approximation, and made a draft pull request #108 to fix that.However, in looking more I discovered that the built-in python
gammalnfunction is even a better way to do this. I also updated the docs to describe the new math being done.This pull request therefore solves #106 and is in lieu of #108, as I think it is a better solution.
@fc-jian, thanks so much for noting and flagging all of this!