Chore(deps): bump scikit-learn from 1.2.2 to 1.5.1 in /jupyter/trustyai/ubi9-python-3.11

[dependabot]

Bumps scikit-learn from 1.2.2 to 1.5.1.

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.5.1

We're happy to announce the 1.5.1 release.

This release contains fixes for a few regressions introduced in 1.5.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.5.html#version-1-5-1

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Thanks to everyone who contributed to this release !

Scikit-learn 1.5.0

We're happy to announce the 1.5.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.5.html

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Scikit-learn 1.4.2

We're happy to announce the 1.4.2 release.

This release only includes support for numpy 2.

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

</tr></table> 

... (truncated)

Commits

• 70fdc84 Bump version [cd build]
• b0ec847 DOC Add missing PR number in changelog entry (#29384)
• 8179b8f DOC Set 1.5.1 release month (#29377)
• 851c0d6 FIX: accuracy and zero_loss support for multilabel with Array API (#29336)
• 99d8a32 FIX zero_one_loss breaks with multilabel and Array API (#29269)
• 059070b ENH Add Array API compatibility to cosine_similarity (#29014)
• ada571f Fix a regression in GridSearchCV for parameter grids that have arrays of diff...
• fb26476 🔒 🤖 CI Update lock files for cirrus-arm CI build(s) 🔒 🤖...
• 5a74cc0 Fix performance regression in ColumnTransformer (#29330)
• c3d69b2 MAINT Pin the ruff version on CI linters (#29359)
• Additional commits viewable in compare view

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[openshift-ci]

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[openshift-ci]

Hi @dependabot[bot]. Thanks for your PR.

I'm waiting for a opendatahub-io member to verify that this patch is reasonable to test. If it is, they should reply with /ok-to-test on its own line. Until that is done, I will not automatically test new commits in this PR, but the usual testing commands by org members will still work. Regular contributors should join the org to skip this step.

Once the patch is verified, the new status will be reflected by the ok-to-test label.

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Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes-sigs/prow repository.

[dibryant]

/ok-to-test

[dibryant]

snyk related issue: https://app.snyk.io/org/red-hat-openshift-data-science-rhods/project/e19a8b06-314b-4fea-b778-79b9862c9607#issue-SNYK-PYTHON-SCIKITLEARN-7217830

All notebooks seems to be effected on this... maybe need to look into this more or fix it in all

[jiridanek]

@dibryant I actually love this initiative, if we keep working on this, we may actually get to a much better place!

So, first, github actions for the image that's being changed are failing

======================================================================
FAIL: test_version (__main__.TestSKLearn.test_version)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/tmp/ipykernel_92/2268024713.py", line 148, in test_version
    self.assertEqual(actual_major_minor, expected_major_minor, "incorrect version")
AssertionError: '1.5' != '1.2'
- 1.5
?   ^
+ 1.2
?   ^
 : incorrect version

https://github.com/opendatahub-io/notebooks/actions/runs/15139150277/job/42558559004?pr=1126#step:41:822

[jiridanek]

@dibryant Second, I have no clue whether we have any meaningful automated scikit-learn test that tries to actually import the Python package and compute something with it, be it a hello world example, or something equally simple. From a quick search, looks like we don't have anything like that.

We haven't done any work on the feature coverage sheet https://docs.google.com/spreadsheets/d/1h_ZeQDlbxWKL6wql0K5PR_5ebfMVFolxbO0CBicVVKs/edit?gid=0#gid=0, but it seems clear that we should quickly add "scikit-learn being available and functioning in the python virtual environment in the image" as a supported feature, and write some meaningful test for that.

[jiridanek]

And, having done that, we may merge these PRs, and any scikit-learn updates that may come later, with peace of mind.

[jiridanek]

@dibryant Actually, third. The comment in Pipfile says

scikit-learn = "~=1.2.1" # Should be pinned down to this version in order to be compatible with trustyai

Therefore, we also need to have a smoke check for trusty ai, so that we can be sure that the update we are doing is not breaking Trusty AI.

[jiridanek]

With these two extra tests being written and passing, we should just merge. Not before, though!

[openshift-ci]

@dependabot[bot]: The following test failed, say /retest to rerun all failed tests or /retest-required to rerun all mandatory failed tests:

Test name	Commit	Details	Required	Rerun command
ci/prow/notebooks-ubi9-e2e-tests	ef489df	link	true	/test notebooks-ubi9-e2e-tests

Full PR test history. Your PR dashboard.

Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes-sigs/prow repository. I understand the commands that are listed here.