[WIP] Add array-api support to metrics.confusion_matrix #28867
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ogrisel
reviewed
May 7, 2024
| # import array_api_strict as xp | ||
| X = xp.asarray(X, copy=copy) | ||
| dtype = X.dtype | ||
| isscaler = X.ndim == 0 |
| msg = ( | ||
| "Cannot return indices with the torch backend yet. See" " array_api_compat." | ||
| ) | ||
| raise NotImplementedError(msg) |
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I have opened data-apis/array-api-compat#135. If array_api_compat maintainers accept this suggestion, then it might be worth contributing such a temporary workaround to array_api_compat. If not, we can implement our own temporary workaround for torch in scikit-learn.
| yield namespace, "cuda" | ||
| yield namespace, "mps" | ||
| else: | ||
| yield namespace, None |
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+1 for this. There once this is in, we should do a follow-up PR for occurrences of yield_namespace_device_dtype_combinations that discard the dtype value to avoid redundant test cases.
|
Please also add a changelog entry in |
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Reference Issues/PRs
See #26024 for the array-api meta-issue tracking the "tools" in sklearn.
What does this implement/fix? Explain your changes.
This PR adds array-api compatibility to the
sklearn.metrics.confusion_matrixmethod, aiming to support all of its current API surface. Many other classification metrics are or can be computed based on a confusion matrix so it seems fairly high value to port.TODO:
Any other comments?
None for now :)