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CI: broaden kmeans 2025.0 deselections #2081

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ethanglaser
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@ethanglaser ethanglaser commented Oct 2, 2024

Description

Full list of conformance tests that have failed in nightly:

  • cluster/tests/test_k_means.py::test_kmeans_elkan_results[42-1e-100-sparse_array-normal]
  • cluster/tests/test_k_means.py::test_kmeans_elkan_results[42-0-sparse_array-normal]
  • cluster/tests/test_k_means.py::test_kmeans_elkan_results[42-0-sparse-normal]
  • cluster/tests/test_k_means.py::test_kmeans_elkan_results[42-0.01-sparse_array-normal]
  • cluster/tests/test_k_means.py::test_kmeans_elkan_results[1e-08-sparse-normal]
  • cluster/tests/test_k_means.py::test_kmeans_elkan_results[42-1e-08-sparse_matrix-normal]

Planning to just deselect the entire suite unless specific deselection is desired.

Add a comprehensive description of proposed changes

List associated issue number(s) if exist(s): #6 (for example)

Documentation PR (if needed): #1340 (for example)

Benchmarks PR (if needed): IntelPython/scikit-learn_bench#155 (for example)


Checklist to comply with before moving PR from draft:

PR completeness and readability

  • I have reviewed my changes thoroughly before submitting this pull request.
  • I have commented my code, particularly in hard-to-understand areas.
  • I have updated the documentation to reflect the changes or created a separate PR with update and provided its number in the description, if necessary.
  • Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
  • I have added a respective label(s) to PR if I have a permission for that.
  • I have resolved any merge conflicts that might occur with the base branch.

Testing

  • I have run it locally and tested the changes extensively.
  • All CI jobs are green or I have provided justification why they aren't.
  • I have extended testing suite if new functionality was introduced in this PR.

Performance

  • I have measured performance for affected algorithms using scikit-learn_bench and provided at least summary table with measured data, if performance change is expected.
  • I have provided justification why performance has changed or why changes are not expected.
  • I have provided justification why quality metrics have changed or why changes are not expected.
  • I have extended benchmarking suite and provided corresponding scikit-learn_bench PR if new measurable functionality was introduced in this PR.

@ethanglaser ethanglaser added the testing Tests for sklearnex/daal4py/onedal4py & patching sklearn label Oct 2, 2024
@ethanglaser
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/intelci: run

@samir-nasibli
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@ethanglaser does it fail on both CPU and GPU?

@ethanglaser
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@ethanglaser does it fail on both CPU and GPU?

No, just CPU from what I have observed. But looks like some GPU tests are already deselected so maybe: https://github.com/intel/scikit-learn-intelex/blob/main/deselected_tests.yaml#L468-L469

icfaust added a commit to icfaust/scikit-learn-intelex that referenced this pull request Oct 2, 2024
@samir-nasibli samir-nasibli merged commit 4c9ea3e into uxlfoundation:main Oct 2, 2024
13 of 32 checks passed
samir-nasibli pushed a commit that referenced this pull request Oct 4, 2024
…ild (#2076)

* Update ci.yml

* Update activate_components.bat

* Update test_linear.py

* Update test_incremental_linear.py

* Update test_kmeans.py

* Update deselected_tests.yaml

* Update deselected_tests.yaml

* add deselction mechanism for Non-Intel Hardware

* remove warnings

* address codefactor recommendations

* make explicit

* mistake in deselection process

* remove bad code

* remove bad code

* isort fixes

* forgotten change to incremental_linear

* add more deselections

* match #2081

* fix errors in formatting

* correct english

* second english correction

* remove some deselections

* set 2025.2 fail for recheck
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2 participants