ENH _hdbscan HIERARCHY data type introduction
#25826
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thomasjpfan
reviewed
Mar 16, 2023
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
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@thomasjpfan @jjerphan wondering what you two think of the PR now |
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
thomasjpfan
reviewed
Mar 28, 2023
| @@ -468,7 +518,7 @@ cdef get_probabilities(cnp.ndarray hierarchy, dict cluster_map, cnp.ndarray labe | |||
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| cluster = cluster_map[cluster_num] | |||
| max_lambda = deaths[cluster] | |||
| if max_lambda == 0.0 or not np.isfinite(lambda_array[n]): | |||
| if max_lambda == 0.0 or isinf(lambda_array[n]): | |||
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Should this be?
Suggested change
| if max_lambda == 0.0 or isinf(lambda_array[n]): | |
| if max_lambda == 0.0 or not isinf(lambda_array[n]): |
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The original checks that it is not finite rather than not infinite, so I think the current isinf is correct -- unless I have it further mixed up somehow?
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Ah I'm thinking about:
Suggested change
| if max_lambda == 0.0 or isinf(lambda_array[n]): | |
| if max_lambda == 0.0 or not isfinite(lambda_array[n]): |
Because of nan behavior:
not isfinite(NaN) == 1
isinf(NaN) == 0Is lambda_array[n] never nan by construction?
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Right, lambda_array stems from the computed minimum reachability distance, which is never expected to be nan since we separate those points before processing
thomasjpfan
approved these changes
Mar 28, 2023
Micky774
added a commit
to Micky774/scikit-learn
that referenced
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May 16, 2023
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
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Reference Issues/PRs
Towards #24686
What does this implement/fix? Explain your changes.
HIERARCHYdtypeandc-typefloat64_tarrays withHIERARCHYwhere appropriateAny other comments?
This is a blocker for more sweeping algorithm refactors and simplifications which rely on the structure of the dtype