[MRG] Support pd.NA in StringDtype columns for SimpleImputer #21114
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Thank you for the PR!
You can find the failing errors in the CI here: https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=32878&view=results
For the linting errors, running black . should resolve the issue.
I left comments on some of my concerns.
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We need to add a test to check that SimpleImputer works with pd.NA and string extension arrays.
This PR treats pd.NA explicitly when the dataframe is converted into an object dtype. This works well when the extension array is naturally convert into a object dtype, i.e. strings.
- Make private API: is_pd_na ==> _is_pandas_na. - Add comments about suppressing `AttributeError`. - Move the `_more_tags` function from `_BaseImputer` to its children. - Add comments about skip validation in `_check_inputs_dtype`.
- Add unit test for floating point array - Fix linter errors on "line too long" - Add notes to doc/whats_new/v1.1.rst
- Move `_more_tags` back to the base class - Revert doc/whats_new/_contributors.rst - Update the docstring of `SimpleImputer` and add another test for using `np.nan` as missing value
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Thanks again for the code review, @thomasjpfan and @ogrisel . Please take another look. |
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Friendly ping @thomasjpfan @ogrisel . Please let me know if you have other comments. If things look good, what is the right procedure to merge this into the main branch? |
yxiong
changed the title
- Add unit test for 'median' strategy on integer-type arrays - Add xfailing test for 'median' strategy on float-typed arrays - Update code style to only suppress `ImportError`, not `AttributeError`
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Updated and all tests passed. Please take another look. |
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Thanks again for the review @thomasjpfan ! @ogrisel please take another look and let me know if you have other comments. |
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Friendly ping on this PR @thomasjpfan @ogrisel . Could you kindly advice what's the right procedure to get this merged into the main branch? Is there any other actions needed from my side at the moment? Thanks! |
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Sorry for the slow feedback @yxiong.
Overall this looks good but there are things to improve with the dtype handling I think. See below.
@thomasjpfan let me know if you agree.
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@ogrisel Please take another look. |
- Add test case for `strategy="mean"` - Use `assert_allclose` for float arrays
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Friendly ping @thomasjpfan @ogrisel . Please take another look. |
…learn#21114) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
…learn#21114) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
…learn#21114) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Reference Issues/PRs
Fixes #21112 .
What does this implement/fix? Explain your changes.
This is a starting point for discussing potential fixes for #21112 , containing two parts:
sklearn.utils.is_scalar_nan(x)return true whenxispd.NA. This is necessary forimputer._validate_inputto successfully validatepd.StringDtypedata withpd.NA.pd.NAinsklearn.utils._mask._get_dense_mask.With these changes, the code snippet in #21112 will run successfully and imputes
pd.NAto empty strings.Any other comments?
I am new in contributing to sklearn and unfamiliar with the custom and norm (e.g. what's the proper way to import pandas). This PR is just a proof-of-concept to initiate some discussion. If the direction looks promising, I can update the code to adhere to package's convention, add documentation and unit tests, etc. Please kindly advice. Thanks!