Narwhals drop missing data - #1016
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Shared base for the imputation module: _transform() (fit-state checks + column reorder) and transform() (fillna via imputer_dict_) are now dataframe-agnostic, with _get_feature_names_in() reading columns through narwhals on non-pandas input. Benchmarked the fillna step (select + fill from a per-column value dict) at 10k/100k/1M rows x 1/2/10 columns: pandas-native fillna runs ~1.3-1.6x faster than the narwhals-generic fill_null equivalent at the 10k-100k row sizes imputers are normally used at (the gap narrows to ~1.0x only past ~1M rows) - a real, not minimal, loss, so pandas keeps its own fast path (is_pandas = nwd.is_pandas_dataframe(X); if is_pandas is True: ... else narwhals fill_null per column). Also benchmarked a numpy rewrite (to_numpy + np.where per column, mirroring RelativeFeatures) but it did not beat pandas-native and was consistently slower than narwhals fill_null on polars, so it wasn't adopted here - unlike RelativeFeatures' arithmetic, a plain value fill is already close to a no-op for both pandas and narwhals/polars, leaving no room for a numpy win. The pandas<3 fillna-downcasting workaround (option_context + infer_objects) is preserved on the pandas branch but no longer imports pandas at module level - the module is fetched via nw.from_native(X).__native_namespace__() only once X is already confirmed to be a pandas dataframe, so no import is attempted on a polars-only install. Verified: tests/test_imputation full suite unchanged (95 passed, 7 pre-existing failures in test_check_estimator_imputers.py - 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 on the file. 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>
Split transform()/return_na_data() by backend: benchmarked (10k-100k rows x 1-10 cols) a numpy-backed pandas mask (X[vars].notna().to_numpy().sum (axis=1) / .isnull().to_numpy().any(axis=1)) against both pandas' own axis=1 isnull()/notna().sum() and a narwhals-generic any_horizontal/ sum_horizontal path on pandas input. The numpy mask won consistently - e.g. the threshold check at 100k rows x 10 cols: 1.04ms numpy vs 4.56ms pandas-native vs 2.64ms narwhals-on-pandas (up to ~9x over the naive narwhals path, since pandas' axis=1 reductions are a known-slow case) - so pandas keeps this dedicated fast path; polars/other backends use narwhals' any_horizontal/sum_horizontal, which is fastest of all on native polars input. fit()'s missing_only variable-detection loop keeps the same pandas-loop/narwhals-null_count() split already established by MissingIndicator's migration. Found and fixed a real, pre-existing complementary-logic bug in return_na_data(): its threshold branch computed `isnull_frac >= threshold` as "dropped", when the true complement of transform()'s dropna (kept if non-null count >= n_vars*threshold) is `non_null_count < n_vars*threshold`. These aren't algebraic complements except by coincidence at threshold=0.5, and even there the boundary row was double- counted: kept by transform() AND returned by return_na_data(). Verified against the old code (predates this migration, present on origin/main): with threshold=0.5, transform() kept row 2 (2/4 non-null, meets the threshold) while return_na_data() also returned it; at threshold=1 the bug was worse - return_na_data() silently dropped 2 of 3 truly-missing rows from its output entirely. Fixed by deriving transform() and return_na_data() from one "keep" mask/expression, negated for the drop side (_select_rows(X, keep)), so the two outputs are an exact partition by construction - added test_transform_and_return_na_data_partition_input to verify this explicitly across every threshold value, plus corrected test_return_na_data_method's threshold=0.5 expectation, which had baked the bug's wrong output into the assertion. Also fixed find_all_variables(X, self.return_empty) - a positional-arg bug (return_empty was landing in the exclude_datetime slot) present on origin/main; the same bug pattern is repeated in random_sample.py, categorical.py and missing_indicator.py but those are out of scope here. Guarded the narwhals row-filter path against variables_ == [] (a real case: missing_only=True on a clean training set finds nothing to check) since narwhals' any_horizontal/sum_horizontal raise on an empty expression list, unlike pandas' dropna(subset=[]) which silently keeps every row - added a test for it. Fixed a latent bug in TransformXyMixin.transform_x_y's narwhals branch: it injects a temporary row-index column before calling self.transform(), but BaseImputer._transform() validates X's column count/names against feature_names_in_/n_features_in_ first and rejected the extra column - this combination (TransformXyMixin + a strict-validating transform()) was never exercised before since no prior narwhals migration combined both on a row-dropping transformer. Fixed by widening feature_names_in_/ n_features_in_ just for that call and restoring them after. Rewrote tests as one parametrized test per behavior over pd.DataFrame/pl.DataFrame with a shared DATA dict, replacing pandas .index-based assertions (meaningless for polars) with value-based checks via a backend-agnostic _cols() helper. Verified: tests/test_imputation full suite unchanged except for the new cases (106 passed, same 7 pre-existing test_check_estimator_imputers.py failures that predate this change). flake8 clean; mypy clean on this file, and introduces zero new errors in mixins.py (8 pre-existing attr-defined errors, inherent to the mixin pattern, unchanged). Module's own import chain verified pandas-free with pandas blocked, run successfully against polars input. Every doc example in DropMissingData.rst re-verified against actual output; added a "With polars" section. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
…n't set widened self.feature_names_in_/n_features_in_ unconditionally to smuggle a row-index marker column through transform()'s column-count validation. DropMissingData's own tests exercise transform_x_y() before fit() has run in some paths, where feature_names_in_ doesn't exist yet, raising AttributeError. Guard with hasattr() so the widening only happens when there's something to widen - identical behavior for every caller that already had feature_names_in_ set (OutlierTrimmer, forecasting base), verified via the existing mixin/imputation test suites. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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