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In the last passing test, scikit-learn is version 1.3.2
setup.py
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| base_extras_heavy = { | ||
| 'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn==1.2.2'], | ||
| 'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn==1.3.2'], |
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Please do not edit dependencies without a specific clear reason
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(pydata GPU dependencies are fragile and require substantial testing for changes )
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@lmeyerov , checked with my colleague, he pinned this dependencies because there is an exception thrown with latest scikit-learn
TypeError: _iter() missing 3 required positional arguments: 'column_as_labels', 'skip_drop', and 'skip_empty_columns'
pinning to version 1.3.2 can prevent this error.
The error is coming from dirty_cat SuperVectorizer that was using scikit-learn. But SuperVectorizer is depreciated and replaced with TableVectorizer
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It's already pinned, not latest
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We should remove from this PR either way, if worthwhile, it should go in its own PR
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ok, I'll remove it.
it will cause the test script failure, therefore, we pin it.
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if we don't pin, test script will fail
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If it is about tests, we can patch test (non-prod) deps?
Working through a prod dep here would take more work to chase down, eg, GPU RAPIDS alignment
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From discussion:
- we can revert to original of scikit > 1.0...
- ... and in
testaicfg, do a<,>to skip the problematic recent scikits (I forgot exact syntax):- see:
Line 30 in e74123e
- see:
setup.py
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| 'testai': [ | ||
| 'numba>=0.57.1' # https://github.com/numba/numba/issues/8615 | ||
| 'numba>=0.57.1', # https://github.com/numba/numba/issues/8615 | ||
| 'scikit-learn<=1.3.2' |
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Also > the known bad version?
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scikit-learn<=1.3.2,>1.4.0 give:
ERROR: No matching distribution found for scikit-learn<=1.3.2,>1.4.0
I use not equal instead
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#548 is landing, should solve CI fail where are we on this? |
4 participants
Changes to work with nexus' dev/switch_org_api.
Reviewed by Koa in progress.