update: update neighbors.py to use pybind11 instead of daal4py

[yuejiaointel]

Description

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)

---

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[icfaust]

@yuejiaointel you are on the right path before the reverts. The design pattern test issues are to be expected. This was a PR which I did a little bit of testing towards it. https://github.com/uxlfoundation/scikit-learn-intelex/pull/2213/files please message me with questions to save time.

[icfaust]

design patterns key off of to_table showing that onedal rather than daal4py was run, but the design of kneighbors is so wrong that now those tests show that the incorrect ordering of operations is occurring with validate_data etc., you'll have to either deselect and make a new ticket, or fix them in this PR.

[codecov]

Codecov Report

❌ Patch coverage is 94.73684% with 1 line in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
onedal/neighbors/neighbors.py	94.73%	0 Missing and 1 partial ⚠️

Flag	Coverage Δ
azure	80.31% <94.73%> (-0.35%)	⬇️
github	72.91% <94.73%> (-0.24%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
onedal/neighbors/neighbors.py	76.90% <94.73%> (-5.84%)	⬇️

... and 4 files with indirect coverage changes

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[yuejiaointel]

design patterns key off of to_table showing that onedal rather than daal4py was run, but the design of kneighbors is so wrong that now those tests show that the incorrect ordering of operations is occurring with validate_data etc., you'll have to either deselect and make a new ticket, or fix them in this PR.

Hi Ian,
Thx for the pointers, I can replicate the error locally and I plan to fix it in this PR, and will ask questions later : D
Best
Yue

[yuejiaointel]

/intelci: run ml_benchmarks

[yuejiaointel]

/intelci: run

[yuejiaointel]

/intelci: run

[yuejiaointel]

/intelci: run

[yuejiaointel]

/intelci: run

[yuejiaointel]

/intelci: run

[yuejiaointel]

/intelci: run

[yuejiaointel]

CI failures look like they come from this PR:

self = array([[5, 3, 1, 0],
       [4, 3, 1, 0],
       [4, 3, 1, 0],
       [4, 3, 1, 0],
       [5, 3, 1, 0],
       [5, 3,...
       [6, 3, 5, 2],
       [6, 2, 5, 1],
       [6, 3, 5, 2],
       [6, 3, 5, 2],
       [5, 3, 5, 1]], dtype=uint8)
dtype = dtype('float64')

    def __array__(self, dtype=None, /, *, copy=None):
>       raise TypeError(
            "Implicit conversion to a NumPy array is not allowed. "
            "Please use `.asnumpy()` to construct a NumPy array explicitly."
        )
E       TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.asnumpy()` to construct a NumPy array explicitly.

/usr/share/miniconda/envs/CB/lib/python3.11/site-packages/dpnp/dpnp_array.py:189: TypeError
_ test_special_estimator_patching[NearestNeighbors(algorithm='brute')-kneighbors_graph-dpnp-SyclQueue_CPU-uint16] _

Thx for reviewing it! Fixed it now. Added some validate_data check tests in to violation array, these will be added based on array api rules in the followup PR

[yuejiaointel]

/intelci: run

[icfaust]

You'll need to temporarily re-add check_feature_names to the various sklearnex estimators. It'll get replaced with validate_data in the array API PR. This is for scikit-learn conformance in private CI.

So far this PR looks great, and looks like its going to be merged quite soon.

[yuejiaointel]

/intelci: run

[yuejiaointel]

You'll need to temporarily re-add check_feature_names to the various sklearnex estimators. It'll get replaced with validate_data in the array API PR. This is for scikit-learn conformance in private CI.

So far this PR looks great, and looks like its going to be merged quite soon.

thx, added!