A few fixes for sklearn 1.9.1

[jcrist]

Scikit-Learn 1.9.1 is being released (already on conda-forge, not yet on pypi). This PR includes a few fixes to update our support (and fix CI).

• Port a bugfix in QuantileTransformer related to subsampling of sparse matrices with ignore_implicit_zeros=True.
• Expand our test bounds

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📝 Summary

Summary by CodeRabbit

• Bug Fixes

  • Improved sparse-data handling in QuantileTransformer, including datasets with implicit zeros and subsampling.
  • Improved compatibility for sparse quantile transformation scenarios across supported scikit-learn versions.

• Compatibility

  • Extended documented and supported scikit-learn compatibility through version 1.9.1.

Walkthrough

This change fixes sparse quantile subsampling with implicit zeros, adds regression and version-aware tests, and updates scikit-learn 1.9.1 compatibility and CUDA dependency pins.

Changes

Sparse quantile and compatibility

Layer / File(s)	Summary
Sparse quantile fitting and regression coverage python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py, python/cuml/tests/test_preprocessing.py	Sparse quantile fitting uses a fixed-size subsampling buffer and host-side nonzero sampling. Tests cover implicit-zero behavior.
Version-aware preprocessing tests python/cuml/tests/test_preprocessing.py, python/cuml/cuml_accel_tests/upstream/scikit-learn/xfail-list.yaml	Tests reuse a parsed scikit-learn version and apply version-specific expectations, including scikit-learn 1.9.1 radius-search xfails.
scikit-learn and CUDA compatibility declarations dependencies.yaml, docs/source/cuml-accel/compatibility.rst, python/cuml/cuml/accel/core.py	Compatibility documentation and constraints now include scikit-learn 1.9.1. The CUDA 12 cuda-bindings pin moves to 12.9.3, and the SPDX notice is updated.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~20 minutes

Suggested reviewers: jameslamb

Merge Risk: 🟡 Moderate · up to 9e863

The PR fixes sparse quantile handling and updates compatibility pins, but environment and package minimums may still allow incompatible versions to be installed. Align those constraints before merging.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 20.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 5 functions across 3 files. (1 skipped: 1…	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies fixes related to scikit-learn 1.9.1, which matches the main changes in the pull request.
Description check	✅ Passed	The description explains the scikit-learn 1.9.1 support updates, the QuantileTransformer fix, and expanded test bounds. It is directly related to the changeset.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

Full details: Docstring Coverage

Explanation

Docstring coverage is 20.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 5 functions across 3 files. (1 skipped: 1 unsupported.)

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[jameslamb]

re-summarizing my offline comments here on GitHub so we have something to link to.

The wheel CI failures like this:

INFO: pip is looking at multiple versions of cudf-cu12 to determine which version is compatible with other requirements. This could take a while.
ERROR: Cannot install cudf-cu12==26.10.0a503.post260908235645 because these package versions have conflicting dependencies.

The conflict is caused by:
    cudf-cu12 26.10.0a503.post260908235645 depends on cuda-bindings<13.0 and >=12.9.3
    The user requested (constraint) cuda-bindings==12.9.2

Additionally, some packages in these conflicts have no matching distributions available for your environment:
    cuda-bindings

To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip to attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts

Are happening because we're getting mismatched cudf-cu12 and libcudf-cu12 nightlies:

$ pip freeze | grep cudf
cudf-cu12==26.10.0a499.post260910170209
libcudf-cu12==26.10.0a503.post260908235645
pylibcudf-cu12==26.10.0a499.post260910170209

code to reproduce (click me)

docker run
--rm
--gpus all
--env RAPIDS_DEPENDENCIES=oldest
-v $(pwd):/opt/work
-w /opt/work
-it rapidsai/citestwheel:26.10-cuda12.9.2-ubuntu22.04-py3.11
bash

source rapids-init-pip
rapids-generate-pip-constraints test_python "${PIP_CONSTRAINT}"

python -m venv libcuml-env
. libcuml-env/bin/activate

rapids-pip-retry install
-v
--prefer-binary
--constraint "${PIP_CONSTRAINT}"
'libcuml-cu12>=26.10.0a0,==26.10.*'
python -c "import libcuml; assert (libraries := libcuml.load_library()) and all(libraries)"
deactivate

rapids-pip-retry install
--prefer-binary
--constraint "${PIP_CONSTRAINT}"
'cudf-cu12>=26.10.0a503,==26.10.'
'libcudf-cu12>=26.10.0a503,==26.10.'
'libcuml-cu12>=26.10.0a0,==26.10.'
'cuml-cu12>=26.10.0a0,==26.10.'

</details>

Bumping oldest `cuda-bindings` here to `12.9.3` should resolve that (similar to https://github.com/NVIDIA/cuml/pull/8585 targeting `main`).

I don't see how exactly that dependency is getting into `cudf-cu12` 26.10 packages, I'll go look separately, but no need to wait on that.

[jcrist]

I've backported #8585 to release/26.10 in this PR, which should hopefully fix CI.

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Inline comments:
In `@conda/environments/all_cuda-129_arch-x86_64.yaml`:
- Line 11: Update both cuda-bindings declarations in
conda/recipes/cuml/recipe.yaml, covering build and run requirements, to require
CUDA 12 versions of at least 12.9.3 and CUDA 13 versions of at least 13.0.2
while preserving the existing upper bounds. The environment files
conda/environments/all_cuda-129_arch-x86_64.yaml:11,
conda/environments/all_cuda-133_arch-aarch64.yaml:11, and
conda/environments/all_cuda-133_arch-x86_64.yaml:11 require no direct changes;
they document the intended minimums.

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[dantegd]

lgtm

[jameslamb]

great, let's do it. If we end up wanting more cuda-bindings bumps that can come in a separate, focused PR. ship it.

[jcrist]

/merge