migrate missing indicator to narwhals, add polar support - #1001
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Removed the module-level `import pandas as pd`; X/y type hints now use
narwhals' IntoDataFrame/IntoSeries. This file overrides transform() rather
than extending BaseImputer's, so both the fit() null-count filter and the
transform() indicator-column step needed their own narwhals path.
Benchmarked both operations at 10k/50k/100k rows x 1/2/10 columns (varying
how many columns need indicators), plus a mixed string+numeric-dtype
dataset matching MissingIndicator's real "all variable types" usage:
- fit()'s `[var for var in variables_ if X[var].isnull().sum() > 0]` loop
is ~2-5x faster on pandas than a narwhals-generic `null_count()` call
(e.g. 100k rows x 10 cols: 0.41ms loop vs 0.78ms narwhals-on-pandas).
A vectorized `X[variables_].isnull().sum()` alternative didn't beat the
loop either. narwhals-on-polars was consistently fastest of all (its own
native path), so the split is pandas-loop vs narwhals-generic (used for
polars/other backends), matching BaseImputer's is_pandas branch pattern.
- transform()'s `X[vars].isna().astype("int8").add_suffix("_na")` +
`pd.concat` is ~2-5x faster on pandas than narwhals' with_columns
equivalent (100k rows x 10 cols: 0.28ms concat vs 1.27ms narwhals-on-
pandas), and also beats `assign()`-per-column (0.91ms) and `join()`
(0.44ms) alternatives - concat already batches all new columns in one
op. So transform() keeps the same pandas fast path, split from a
narwhals with_columns path for other backends.
Both losses are >1.7x, past the "keep pandas fast path" threshold, so
merging into one narwhals-generic path (as BaseImputer's docstring
discusses for its own fillna step) was not justified here either.
Numpy: converting columns via `.to_numpy()` + `pd.isna()` (the only numpy
op that works across MissingIndicator's mixed string/numeric columns,
since np.isnan raises on object arrays) was consistently ~1.7-2x slower
than pandas-native isnull()/isna() for both fit and transform on mixed
dtypes - the extra .to_numpy() copy plus pd.isna() dispatch outweighs any
gain, same conclusion as BaseImputer's fillna numpy experiment.
Tests: converted tests/test_imputation/test_missing_indicator.py from the
pandas-only `df_na` fixture to a plain DATA dict parametrized over
`make_df` in [pd.DataFrame, pl.DataFrame], asserting identical variables_
selection and identical `<var>_na` column values on both backends for the
same input (one cross-backend PerformanceWarning regression test stays
pandas-only, since it targets the pandas fast path specifically).
Docs: docs/user_guide/imputation/MissingIndicator.rst has no inline
printed output to go stale (it references a screenshot image instead of
doctest-style text) - verified its house_prices code example's logic
against the migrated transformer with a synthetic stand-in dataset (no
network access in this environment) and it behaves identically. Added a
verified "With polars" example to the class docstring.
Verified: tests/test_imputation/test_missing_indicator.py 29 passed.
tests/test_imputation full suite: 107 passed / 7 pre-existing failures
in test_check_estimator_imputers.py (confirmed identical failures against
a baseline run of origin/narwhals-imputation-base: 95 passed / same 7
failures - sklearn's check_estimator feeds raw numpy arrays, which
check_X() has always rejected per the narwhals migration's dataframe-only
contract; predates this change). 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 find/check_all_variables, the is_pandas null-count fast path and _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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