[ArrayAPI] Refactor KMeans estimator to follow oneDAL estimator design pattern #2654
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| "The default value of `n_init` will change from " | ||
| f"{default_n_init} to 'auto' in 1.4. Set the value of `n_init`" | ||
| " explicitly to suppress the warning" | ||
| f"{default_n_init} to 'auto' in 1.4. Set `n_init` explicitly " |
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Since we have a utility to check the sklearn version, this could be placed under an if-else, or removed altogether considering we appear to not support versions 1.1 through 1.3.
| elif callable(init): | ||
| cc_arr = init(X, self.n_clusters, random_state) | ||
| cc_arr = np.ascontiguousarray(cc_arr, dtype=dtype) | ||
| if hasattr(cc_arr, "__array_namespace__"): |
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Isn't the function get_namespace supposed to be doing these checks?
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Refactor KMeans estimator to follow oneDAL estimator design pattern
Depends on #2641
This PR refactors the KMeans estimator to align with the standardized design pattern used across oneDAL estimators, such as DummyEstimator outlined in #2534 . The main goal is to make the estimator consistent, maintainable, and compatible with future extensions (e.g., other algorithms or backends).
Changes Made
Reorganized KMeans and _BaseKMeans classes to follow the oneDAL estimator model pattern.
Added backend bindings using @bind_default_backend decorators:
train() → kmeans.clustering
infer() → kmeans.clustering
_is_same_clustering() → kmeans_common (no policy)
Centralized creation of the params dictionary in _get_onedal_params(...).
Ensured type dispatch and method dispatch use fptype and method respectively (e.g., by_default, lloyd_csr).
Wrapped all input/output in to_table / from_table.
Separated backend logic from estimator logic using _fit_backend() and _predict_backend().
Deferred model creation and attribute assignment to follow a minimalistic and clean init/finalization process.
Applied Sycl queue support via @supports_queue decorators for fit/predict/score.
Removed redundant or sklearn-only attributes that aren’t required by oneDAL estimators.
Preserved full feature parity (e.g., init modes, scoring, CSR support, random_state handling, etc.).
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Checklist to comply with before moving PR from draft:
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Testing
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