FIX: Allow KNN with missing values

[david-cortes-intel]

Description

KNN algorithms on sklearn support data with missing values, but sklearnex makes a check for 'all finite'. This PR makes it fall back to sklearn when receiving data that has missing values.

Disclaimer: I'm not 100% sure about whether oneDAL supports KNN with missing values or not.

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Checklist:

Completeness and readability

• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• 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.

[david-cortes-intel]

/intelci: run

[david-cortes-intel]

/azp run Nightly

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[david-cortes-intel]

/intelci: run

[david-cortes-intel]

/azp run Nightly

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Azure Pipelines successfully started running 1 pipeline(s).

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.

Flag	Coverage Δ
azure	80.42% <ø> (-0.01%)	⬇️
github	82.02% <ø> (-0.01%)	⬇️

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Files with missing lines	Coverage Δ
sklearnex/neighbors/common.py	92.44% <ø> (-0.05%)	⬇️

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[icfaust]

scikit-learn/scikit-learn#25319 @david-cortes-intel . Can you detail the missing value use case with an example? As of now we support these metrics: https://github.com/uxlfoundation/scikit-learn-intelex/blob/main/sklearnex/neighbors/common.py#L221 which does not include nan_euclidean which would be the use case of this missing value support.

[david-cortes-intel]

scikit-learn/scikit-learn#25319 @david-cortes-intel . Can you detail the missing value use case with an example? As of now we support these metrics: https://github.com/uxlfoundation/scikit-learn-intelex/blob/main/sklearnex/neighbors/common.py#L221 which does not include nan_euclidean which would be the use case of this missing value support.

It still fails this test:
https://github.com/scikit-learn/scikit-learn/blob/4a10d0ed8d85e6ed24a647bd28a65c0c64b101ef/sklearn/neighbors/tests/test_neighbors.py#L2356

[icfaust]

@david-cortes-intel then I think there is a bug in the metrics check. I think that should be the avenue taken to fix the issue.

[icfaust]

Just wanted to note that I know that its a bit convoluted and annoying on the neighbors side of things here, I just want to make sure that if we can solve it without a separate finite check, then we should do that.

[david-cortes-intel]

@david-cortes-intel then I think there is a bug in the metrics check. I think that should be the avenue taken to fix the issue.

Switched it to use the metric checks instead.

[david-cortes-intel]

/intelci: run