Add: Docstring return empty#938
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Docstring return empty
solegalli
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* add return_empty attr to base_numerical.py (#937) * add return_empty attr to base_numerical * Apply suggestion from @solegalli * Apply suggestion from @solegalli * code-style --------- Co-authored-by: Soledad Galli <solegalli@protonmail.com> * Add: Docstring return empty (#938) * add: return_empty docstring * chore: minor fix for tests * push str to the next line * expand substitute * minor fix for flake8 --------- Co-authored-by: Soledad Galli <solegalli@protonmail.com> * fix mypy error on categorical imputer (#939) * add return_empty on creation fix typo in module name (#942) * add return_empty on creation * typo * adding _check_return_empty_is_bool * add return_empty to discretisers (#943) * adding return_empty attr * codestyle * more codestyle * add return_empty in imputation (#945) * add: return_empty in imputation * minor-fix: flake8 style * clearn deprecation, future and other warnings (#944) * clearn deprecation warnings * fix warnings in pipeline tests * remove more warnings in tests * more changes * revert changes * add return_false to encoder + fix mock (#947) * add return_empty to transformation, scaling, outliers, datetime, time… (#948) * add return_empty to transformation, scaling, outliers, datetime, timeseries and wrapper transformers Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * remove missplaced tests --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> * fix return_empty exposure in discretisers (#949) * fix return_empty exposure in discretisers and add tests for existing return_empty transformers EqualFrequencyDiscretiser, EqualWidthDiscretiser and GeometricWidthDiscretiser advertised return_empty in their docstrings but did not accept it in __init__, so users could not enable it. ArbitraryDiscretiser selects variables from binning_dict and does not support the parameter, so the unused docstring wiring is removed. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * remove missplaced tests --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> * [WIP] first changes towards deprecation warning (#950) * add return_empty tests via shared estimator_checks (#951) * add return_empty tests via shared estimator check, plus family-specific tests Adds check_return_empty to fit_functionality_checks.py and wires it into check_feature_engine_estimator, so it runs automatically for every transformer that already goes through that shared pipeline (transformation, discretisation, encoding, imputation, outliers, most of creation, forecasting). For transformers not fed through check_feature_engine_estimator, tests are added directly in their existing per-class test files, following the file's established conventions: DecisionTreeFeatures, DatetimeSubtraction, MeanNormalizationScaler and SklearnTransformerWrapper. DatetimeOrdinal needs a clone-free test, since its __init__ never stores self.start_date, which breaks sklearn's get_params()/clone() for this class independently of return_empty. DecisionTreeEncoder's return_empty=True path is documented as currently raising ValueError (a real bug: it builds a nested OrdinalEncoder(variables=[]) whose constructor rejects the explicit empty list), rather than silently asserting the intended contract. AddMissingIndicator, DropMissingData and RandomSampleImputer (variables="all") and SklearnTransformerWrapper wrapping an "all-types" transformer are not testable this way: find_all_variables only raises on a zero-column dataframe, which check_X rejects before variable selection ever runs. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * remove unnecessary filter * remove return_empty from shared base classes where subclasses don't need it BaseCreation, BaseDiscretiser and CategoricalInitMixinNA each set self.return_empty unconditionally, even though some of their subclasses never read it: MathFeatures and RelativeFeatures (variables is mandatory, never None), and ArbitraryDiscretiser (fit() is fully overridden, selecting variables from binning_dict instead of find_numerical_variables). Those classes ended up with a dead return_empty attribute that isn't a real, settable constructor parameter (hasattr is True, get_params() is False), which also meant the test gate had to use the less obvious get_params() check instead of the hasattr() pattern used by every other check. return_empty is removed from BaseCreation and BaseDiscretiser; the subclasses that genuinely need it (DatetimeSubtraction; EqualFrequencyDiscretiser, EqualWidthDiscretiser, GeometricWidthDiscretiser) now validate and store it directly, the same way DecisionTreeDiscretiser already did. CategoricalInitMixinNA is different: MatchCategories (feature_engine.preprocessing) was the only user not exposing return_empty, but it does exercise the return_empty-consuming code path (auto-detects categorical variables via the same shared _check_or_select_variables used by its sibling encoders), so removing the leaked attribute broke it outright. Rather than special-case it to a hardcoded False, return_empty is added as a proper, documented parameter to MatchCategories, matching OrdinalEncoder, RareLabelEncoder, CountFrequencyEncoder and MeanEncoder. The shared test gate in check_feature_engine_estimator now uses hasattr(estimator, "return_empty"), consistent with the other init-parameter checks (cv, missing_values, drop_original), instead of the get_params()-based gate needed to route around the leaks. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * replace try/except probing in check_return_empty with tag-based lookup Instead of trying each of 3 candidate dataframes against the transformer and catching whichever one raises TypeError, look up the right "no variables of this type" dataframe directly from the transformer's own `variables` tag (numerical/categorical/datetime), the same tag the other variable-selection checks already use. Two transformers don't carry a usable tag for unrelated reasons: LogTransformer's _more_tags() doesn't set "variables" at all (a pre-existing gap), and DatetimeSubtraction inherits BaseCreation's "skip" tag (meant to opt out of the *generic* variable-assignment checks, not a statement about its variable type). Both are handled with a small, explicit, named override instead of silently falling back to trial and error. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * final changes to tests * tities test and fixes transform tests * expands docstring test * fix and expand test for datetime ordinal * remove comment * fix test to sklearn wrapper and updates implementation * remove comment * fix code style * add error text matching to tests --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> * Add test for get feature names out within return empty tests (#954) * reposition return_empty to after variables (#955) --------- Co-authored-by: Jordi Pastor <37837678+PastorJordi@users.noreply.github.com> Co-authored-by: Míriam Méndez <60411598+miriam-mendez@users.noreply.github.com> Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
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