Describe the peak calling in ratio_consistent_peaks as implemented - #132
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* Document the standardisation step and the actual height/prominence thresholds * Drop persistent_peak_count() and the scikit-tda dependency, it was never called * Only import matplotlib and seaborn when actually plotting * Say case 2 where the code means case 2
ghar1821
approved these changes
Jul 30, 2026
LuLeom
approved these changes
Jul 30, 2026
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Describe your changes
The description of
ratio_consistent_peaksdoesn't match what the component does, which matters here because those thresholds effectively are the metric.The description says:
but
helper.pyusesheight = 0.1andprominence = 0.01, both as absolute values rather than relative to the max density. The description also doesn't mention that the two splits are standardised together on the pooled mean/sd before the KDE, which is what makes an absolute height threshold meaningful in the first place.I've updated the description to match the code rather than the other way around, since changing the thresholds would change every score. If the described behaviour (prominence 0.1, height relative to max density) was the intent, that's worth doing as a separate PR so we can see the effect on the scores -- happy to open one, just let me know.
While in there:
persistent_peak_count()fromhelper.pyand thescikit-tdasetup dependency from the config. The function is never called, butfrom ripser import ripserat module level meant the metric image had to carry the whole package. It's recoverable from git history if it's wanted later.matplotlib/seabornimports into theif plot:branch, they're only used when debugging locally.n_case2, and the comment block above the loop used the old case numbering. Both now say case 2.No behaviour changes.
Found while reviewing the task ahead of the next full benchmark run -- see also the sibling PRs.
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Proposed changes are described in the CHANGELOG.md
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