Migrate ArbitraryImputer to narwhals, add polars support - #1003
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fit() never touches dataframe values - it only calls the already-
narwhals-migrated check_X/check_numerical_variables/find_numerical_variables
and builds imputer_dict_ via a plain dict comprehension over column names -
so the only change needed was dropping the module-level `import pandas as
pd` and swapping the X/y type hints for narwhals' IntoDataFrame/IntoSeries.
transform() is fully inherited from the already-migrated BaseImputer.
Benchmarked fit()+transform() (via fit_transform) at 10k/50k/100k rows x
1/2/10 cols, pandas vs polars, and old code vs migrated code on pandas
input: fit() takes ~0.06-0.13ms regardless of row count, column count, or
backend, both before and after the edit (within noise of each other) -
confirming fit() truly does no per-row work. No backend split was needed
or added; a single narwhals-agnostic path was kept (it already was one).
Numpy: not applicable - fit() has no numeric computation over data at all,
only dict/list building over variable names, so there is nothing for numpy
to accelerate.
While touching fit(), changed `if self.imputer_dict:` to
`if self.imputer_dict is not None:` per AGENTS.md's ban on truthy
container checks; this also fixes a latent edge case where imputer_dict={}
was silently treated as "not provided" and fell through to the
variables/arbitrary_number branch. Confirmed pre-existing on
origin/narwhals-imputation-base (unrelated to this migration, no test
previously covered it).
Rewrote tests/test_imputation/test_arbitrary_imputer.py to the
cross-backend parametrized style (@pytest.mark.parametrize("make_df",
[pd.DataFrame, pl.DataFrame])) in place, replacing the pandas-only df_na
fixture and pd.testing.assert_frame_equal/.isnull() assertions with a
plain DATA dict and narwhals-based null/value assertions. The
deprecation-warning test for ArbitraryNumberImputer and the
arbitrary_number-type-validation test stayed single-backend since they
never touch a dataframe.
Added a "With polars" section to both the class docstring and
docs/user_guide/imputation/ArbitraryImputer.rst, output verified by
actually running the transformer. No staleness found in the existing rst
(it builds its example from fetch_openml, no literal printed dataframe
values to go stale).
Verified: tests/test_imputation full suite 98 passed / 7 pre-existing
unrelated failures in test_check_estimator_imputers.py (same 7 as on
origin/narwhals-imputation-base's baseline of 95 passed - the 3 extra
passes here are the new cross-backend parametrization, no regressions).
flake8 and mypy clean. Module imports with pandas import blocked.
sphinx -W build clean (only the pre-existing unrelated linkcode_resolve
warning).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
check_X now returns a narwhals DataFrame; fit() passed it to check_numerical_variables / find_numerical_variables / _get_feature_names_in, which then took their non-pandas path (spurious is_pandas_dataframe warning, hard failure on integer column names). check_X is pure validation, so stop rebinding X and keep working with the native input. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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