From 6b394dbfcffb91dcaf51d6e133ffa7172e0a09a3 Mon Sep 17 00:00:00 2001 From: PaulWestenthanner Date: Sun, 19 Jan 2025 20:27:44 +0100 Subject: [PATCH] Use new sklearn tagging concept. fixes #448 --- .github/workflows/docs.yml | 2 +- CHANGELOG.md | 8 +- CONTRIBUTING.md | 10 +- category_encoders/base_contrast_encoder.py | 2 +- category_encoders/basen.py | 2 +- category_encoders/cat_boost.py | 8 +- category_encoders/count.py | 2 +- category_encoders/glmm.py | 8 +- category_encoders/hashing.py | 2 +- category_encoders/james_stein.py | 8 +- category_encoders/leave_one_out.py | 8 +- category_encoders/m_estimate.py | 8 +- category_encoders/one_hot.py | 2 +- category_encoders/ordinal.py | 2 +- category_encoders/quantile_encoder.py | 20 +- category_encoders/rankhot.py | 2 +- category_encoders/target_encoder.py | 2 +- category_encoders/utils.py | 45 ++-- category_encoders/woe.py | 2 +- poetry.lock | 258 +++++++++++---------- pyproject.toml | 2 +- tests/test_encoders.py | 12 +- 22 files changed, 225 insertions(+), 190 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 84126aaf..c2d72bfd 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -17,7 +17,7 @@ jobs: - name: Directly build docs run: | pip install -r docs/requirements.txt - sphinx-build -D docs/source ./docs/build/html/ + sphinx-build docs/source ./docs/build/html/ - name: Deploy Docs uses: peaceiris/actions-gh-pages@v3 with: diff --git a/CHANGELOG.md b/CHANGELOG.md index f49c283b..8b3426b1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,11 @@ +v.2.8.0 +======= + +* Fix: Support new concept of sklearn tags, now requiring sklearn >= 1.6.0 +* Fix: Docs deployment + v.2.7.0 -========== +======= * Refactor: Use poetry as packaging tool * Refactor: Add more typing diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index ff1199f4..97b0b9ca 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -35,19 +35,25 @@ The preferred workflow to contribute to git-pandas is: Guidelines ========== -This is still a very young project, but we do have a few guiding principles: 1. Maintain semantics of the scikit-learn API 2. Write detailed docstrings in numpy format 3. Support pandas dataframes and numpy arrays as inputs 4. Write tests +Styleguide: + +We're using ruff for linting. Rules are implemented in the `pyproject.toml` file. To run the linter, use: + + $ poetry run ruff check category_encoders --fix + + Running Tests ============= To run the tests, use: - $ pytest + $ poetry run pytest tests/ Easy Issues / Getting Started ============================= diff --git a/category_encoders/base_contrast_encoder.py b/category_encoders/base_contrast_encoder.py index 937c8150..f3141b2e 100644 --- a/category_encoders/base_contrast_encoder.py +++ b/category_encoders/base_contrast_encoder.py @@ -12,7 +12,7 @@ __author__ = 'paulwestenthanner' -class BaseContrastEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin): +class BaseContrastEncoder(util.UnsupervisedTransformerMixin, util.BaseEncoder): """Base class for various contrast encoders. Parameters diff --git a/category_encoders/basen.py b/category_encoders/basen.py index 78a2a530..d14ce4e8 100644 --- a/category_encoders/basen.py +++ b/category_encoders/basen.py @@ -34,7 +34,7 @@ def _ceillogint(n, base): return ret -class BaseNEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin): +class BaseNEncoder( util.UnsupervisedTransformerMixin,util.BaseEncoder): """Base-N encoder encodes the categories into arrays of their base-N representation. A base of 1 is equivalent to one-hot encoding (not really base-1, but useful), diff --git a/category_encoders/cat_boost.py b/category_encoders/cat_boost.py index b7b55254..03ebcdc6 100644 --- a/category_encoders/cat_boost.py +++ b/category_encoders/cat_boost.py @@ -9,7 +9,7 @@ __author__ = 'Jan Motl' -class CatBoostEncoder(util.BaseEncoder, util.SupervisedTransformerMixin): +class CatBoostEncoder(util.SupervisedTransformerMixin, util.BaseEncoder): """CatBoost Encoding for categorical features. Supported targets: binomial and continuous. @@ -202,10 +202,10 @@ def _transform(self, X, y=None): return X - def _more_tags(self) -> dict[str, bool]: + def __sklearn_tags__(self) -> util.EncoderTags: """Set scikit transformer tags.""" - tags = super()._more_tags() - tags['predict_depends_on_y'] = True + tags = super().__sklearn_tags__() + tags.predict_depends_on_y = True return tags def _fit_column_map(self, series, y): diff --git a/category_encoders/count.py b/category_encoders/count.py index 2cf3e748..c9270cd0 100644 --- a/category_encoders/count.py +++ b/category_encoders/count.py @@ -11,7 +11,7 @@ __author__ = 'joshua t. dunn' -class CountEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin): +class CountEncoder( util.UnsupervisedTransformerMixin,util.BaseEncoder): """Count encoding for categorical features. For a given categorical feature, replace the names of the groups with the group counts. diff --git a/category_encoders/glmm.py b/category_encoders/glmm.py index a3f59bec..3a5b2738 100644 --- a/category_encoders/glmm.py +++ b/category_encoders/glmm.py @@ -15,7 +15,7 @@ __author__ = 'Jan Motl' -class GLMMEncoder(util.BaseEncoder, util.SupervisedTransformerMixin): +class GLMMEncoder( util.SupervisedTransformerMixin ,util.BaseEncoder): """Generalized linear mixed model. Supported targets: binomial and continuous. @@ -164,10 +164,10 @@ def _transform(self, X, y=None): X = self._score(X, y) return X - def _more_tags(self) -> dict[str, bool]: + def __sklearn_tags__(self) -> util.EncoderTags: """Set scikit transformer tags.""" - tags = super()._more_tags() - tags['predict_depends_on_y'] = True + tags = super().__sklearn_tags__() + tags.predict_depends_on_y = True return tags def _train(self, X, y): diff --git a/category_encoders/hashing.py b/category_encoders/hashing.py index 86f5c063..d100a82a 100644 --- a/category_encoders/hashing.py +++ b/category_encoders/hashing.py @@ -14,7 +14,7 @@ __author__ = 'willmcginnis', 'LiuShulun' -class HashingEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin): +class HashingEncoder( util.UnsupervisedTransformerMixin,util.BaseEncoder): """A multivariate hashing implementation with configurable dimensionality/precision. The advantage of this encoder is that it does not maintain a dictionary of observed categories. diff --git a/category_encoders/james_stein.py b/category_encoders/james_stein.py index eca34140..7b35f548 100644 --- a/category_encoders/james_stein.py +++ b/category_encoders/james_stein.py @@ -11,7 +11,7 @@ __author__ = 'Jan Motl' -class JamesSteinEncoder(util.BaseEncoder, util.SupervisedTransformerMixin): +class JamesSteinEncoder( util.SupervisedTransformerMixin,util.BaseEncoder): """James-Stein estimator. Supported targets: binomial and continuous. @@ -228,10 +228,10 @@ def _transform(self, X, y=None): X = self._score(X, y) return X - def _more_tags(self) -> dict[str, bool]: + def __sklearn_tags__(self) -> util.EncoderTags: """Set scikit transformer tags.""" - tags = super()._more_tags() - tags['predict_depends_on_y'] = True + tags = super().__sklearn_tags__() + tags.predict_depends_on_y = True return tags def _train_pooled(self, X, y): diff --git a/category_encoders/leave_one_out.py b/category_encoders/leave_one_out.py index ffa068b4..0f839d56 100644 --- a/category_encoders/leave_one_out.py +++ b/category_encoders/leave_one_out.py @@ -9,7 +9,7 @@ __author__ = 'hbghhy' -class LeaveOneOutEncoder(util.BaseEncoder, util.SupervisedTransformerMixin): +class LeaveOneOutEncoder( util.SupervisedTransformerMixin,util.BaseEncoder): """Leave one out coding for categorical features. This is very similar to target encoding but excludes the current row's @@ -124,10 +124,10 @@ def _transform(self, X, y=None): X = self.transform_leave_one_out(X, y, mapping=self.mapping) return X - def _more_tags(self) -> dict[str, bool]: + def __sklearn_tags__(self) -> util.EncoderTags: """Set scikit transformer tags.""" - tags = super()._more_tags() - tags['predict_depends_on_y'] = True + tags = super().__sklearn_tags__() + tags.predict_depends_on_y = True return tags def fit_leave_one_out( diff --git a/category_encoders/m_estimate.py b/category_encoders/m_estimate.py index d6862c5a..0567be94 100644 --- a/category_encoders/m_estimate.py +++ b/category_encoders/m_estimate.py @@ -9,7 +9,7 @@ __author__ = 'Jan Motl' -class MEstimateEncoder(util.BaseEncoder, util.SupervisedTransformerMixin): +class MEstimateEncoder( util.SupervisedTransformerMixin,util.BaseEncoder): """M-probability estimate of likelihood. Supported targets: binomial and continuous. @@ -150,10 +150,10 @@ def _transform(self, X, y=None): X = self._score(X, y) return X - def _more_tags(self) -> dict[str, bool]: + def __sklearn_tags__(self) -> util.EncoderTags: """Set scikit transformer tags.""" - tags = super()._more_tags() - tags['predict_depends_on_y'] = True + tags = super().__sklearn_tags__() + tags.predict_depends_on_y = True return tags def _train(self, X, y): diff --git a/category_encoders/one_hot.py b/category_encoders/one_hot.py index 7726402a..9c98e123 100644 --- a/category_encoders/one_hot.py +++ b/category_encoders/one_hot.py @@ -11,7 +11,7 @@ __author__ = 'willmcginnis' -class OneHotEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin): +class OneHotEncoder( util.UnsupervisedTransformerMixin,util.BaseEncoder): """Onehot (or dummy) coding for categorical features, produces a binary feature per category. Parameters diff --git a/category_encoders/ordinal.py b/category_encoders/ordinal.py index 4237d447..35e1f4c3 100644 --- a/category_encoders/ordinal.py +++ b/category_encoders/ordinal.py @@ -12,7 +12,7 @@ __author__ = 'willmcginnis' -class OrdinalEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin): +class OrdinalEncoder( util.UnsupervisedTransformerMixin,util.BaseEncoder): """Encodes categorical features as ordinal, in one ordered feature. Ordinal encoding uses a single column of integers to represent the classes. diff --git a/category_encoders/quantile_encoder.py b/category_encoders/quantile_encoder.py index a5df8118..e8088a93 100644 --- a/category_encoders/quantile_encoder.py +++ b/category_encoders/quantile_encoder.py @@ -18,7 +18,7 @@ from category_encoders.ordinal import OrdinalEncoder -class QuantileEncoder(util.BaseEncoder, util.SupervisedTransformerMixin): +class QuantileEncoder(util.SupervisedTransformerMixin, util.BaseEncoder): """Quantile Encoding for categorical features. This a statistically modified version of target MEstimate encoder where selected features @@ -204,7 +204,7 @@ def quantile_encode(self, X_in: pd.DataFrame) -> pd.DataFrame: # todo does not fit in schema since it is an ensemble of other encoders -class SummaryEncoder(BaseEstimator, util.TransformerWithTargetMixin): +class SummaryEncoder(BaseEstimator): """Summary Encoding for categorical features. It's an encoder designed for creating richer representations by applying quantile @@ -418,6 +418,22 @@ def transform( else: return transformed_df.to_numpy() + def __sklearn_tags__(self) -> util.EncoderTags: + """Set scikit transformer tags.""" + sk_tags = super().__sklearn_tags__() + tags = util.EncoderTags.from_sk_tags(sk_tags) + tags.target_tags.required = True + return tags + + def fit_transform(self, X: util.X_type, y: util.y_type | None = None): + """Fit and transform using target. + + This also uses the target for transforming, not only for training. + """ + if y is None: + raise TypeError('fit_transform() missing argument: ' 'y' '') + return self.fit(X, y).transform(X, y) + def get_feature_names(self) -> np.ndarray: """Deprecated method to get feature names. Use `get_feature_names_out` instead.""" msg = ( diff --git a/category_encoders/rankhot.py b/category_encoders/rankhot.py index 264ad219..0cd0bee4 100644 --- a/category_encoders/rankhot.py +++ b/category_encoders/rankhot.py @@ -9,7 +9,7 @@ from category_encoders import OrdinalEncoder -class RankHotEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin): +class RankHotEncoder( util.UnsupervisedTransformerMixin,util.BaseEncoder): """Rank Hot Encoder. The rank-hot encoder is similar to a one-hot encoder, diff --git a/category_encoders/target_encoder.py b/category_encoders/target_encoder.py index f60706ac..f9e495a9 100644 --- a/category_encoders/target_encoder.py +++ b/category_encoders/target_encoder.py @@ -14,7 +14,7 @@ __author__ = 'chappers' -class TargetEncoder(util.BaseEncoder, util.SupervisedTransformerMixin): +class TargetEncoder( util.SupervisedTransformerMixin,util.BaseEncoder): """Target encoding for categorical features. Supported targets: binomial and continuous. diff --git a/category_encoders/utils.py b/category_encoders/utils.py index 537dc43c..c5830e25 100644 --- a/category_encoders/utils.py +++ b/category_encoders/utils.py @@ -4,6 +4,7 @@ import warnings from abc import abstractmethod +from dataclasses import dataclass, fields from enum import Enum, auto from typing import Hashable, Sequence @@ -16,6 +17,7 @@ from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError from sklearn.preprocessing import LabelEncoder +from sklearn.utils import Tags __author__ = 'willmcginnis' @@ -345,6 +347,21 @@ def get_docstring_output_shape(in_out_relation: EncodingRelation) -> str: return 'M features (M can be anything)' +@dataclass +class EncoderTags(Tags): + """Custom Tags for encoders.""" + + predict_depends_on_y: bool = False + + @classmethod + def from_sk_tags(cls, tags: Tags) -> EncoderTags: + """Initialize EncoderTags from given sklearn Tags.""" + as_dict = { + field.name: getattr(tags, field.name) + for field in fields(tags) + } + return cls(**as_dict) + class BaseEncoder(BaseEstimator): """BaseEstimator class for all encoders. @@ -437,7 +454,7 @@ def fit(self, X: X_type, y: y_type | None = None, **kwargs): self.feature_names_in_ = X.columns.tolist() self.n_features_in_ = len(self.feature_names_in_) - if self._get_tags().get('supervised_encoder'): + if self.__sklearn_tags__().target_tags.required: if not is_numeric_dtype(y): self.lab_encoder_ = LabelEncoder() y = self.lab_encoder_.fit_transform(y) @@ -475,7 +492,7 @@ def fit(self, X: X_type, y: y_type | None = None, **kwargs): return self def _check_fit_inputs(self, X: X_type, y: y_type) -> None: - if self._get_tags().get('supervised_encoder'): + if self.__sklearn_tags__().target_tags.required: if y is None: raise ValueError( 'Supervised encoders need a target for the fitting. The target cannot be None' @@ -573,9 +590,12 @@ def _fit(self, X: pd.DataFrame, y: pd.Series | None, **kwargs): ... class SupervisedTransformerMixin(sklearn.base.TransformerMixin): """Mixin for supervised transformers (with target).""" - def _more_tags(self) -> dict[str, bool]: + def __sklearn_tags__(self) -> EncoderTags: """Set scikit transformer tags.""" - return {'supervised_encoder': True} + sk_tags = super().__sklearn_tags__() + tags = EncoderTags.from_sk_tags(sk_tags) + tags.target_tags.required = True + return tags def transform(self, X: X_type, y: y_type | None = None, override_return_df: bool = False): """Perform the transformation to new categorical data. @@ -653,20 +673,3 @@ def transform(self, X: X_type, override_return_df: bool = False): @abstractmethod def _transform(self, X: pd.DataFrame) -> pd.DataFrame: ... - - -class TransformerWithTargetMixin: - """Mixin for transformers with target information.""" - - def _more_tags(self) -> dict[str, bool]: - """Set scikit transformer tags.""" - return {'supervised_encoder': True} - - def fit_transform(self, X: X_type, y: y_type | None = None, **fit_params): - """Fit and transform using target. - - This also uses the target for transforming, not only for training. - """ - if y is None: - raise TypeError('fit_transform() missing argument: ' 'y' '') - return self.fit(X, y, **fit_params).transform(X, y) diff --git a/category_encoders/woe.py b/category_encoders/woe.py index 0bba6d10..51815aa9 100644 --- a/category_encoders/woe.py +++ b/category_encoders/woe.py @@ -12,7 +12,7 @@ __author__ = 'Jan Motl' -class WOEEncoder(util.BaseEncoder, util.SupervisedTransformerMixin): +class WOEEncoder( util.SupervisedTransformerMixin,util.BaseEncoder): """Weight of Evidence coding for categorical features. Supported targets: binomial. For polynomial target support, see PolynomialWrapper. diff --git a/poetry.lock b/poetry.lock index 8b318f57..de81f1a0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -328,66 +328,66 @@ files = [ [[package]] name = "numpy" -version = "2.2.1" +version = "2.2.2" description = "Fundamental package for array computing in Python" optional = false python-versions = ">=3.10" files = [ - {file = "numpy-2.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5edb4e4caf751c1518e6a26a83501fda79bff41cc59dac48d70e6d65d4ec4440"}, - {file = "numpy-2.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:aa3017c40d513ccac9621a2364f939d39e550c542eb2a894b4c8da92b38896ab"}, - {file = "numpy-2.2.1-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:61048b4a49b1c93fe13426e04e04fdf5a03f456616f6e98c7576144677598675"}, - {file = "numpy-2.2.1-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:7671dc19c7019103ca44e8d94917eba8534c76133523ca8406822efdd19c9308"}, - {file = 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test_tmp_column_name(self): ) for encoder_name in encoders.__all__: enc = getattr(encoders, encoder_name)() - if not enc._get_tags().get('supervised_encoder'): + if not enc.__sklearn_tags__().target_tags.required: continue with self.subTest(encoder_name=encoder_name): _ = enc.fit_transform(binary_cat_example, binary_cat_example['target']) @@ -579,7 +579,7 @@ def test_get_feature_names_out(self): with self.subTest(encoder_name=encoder_name): enc = getattr(encoders, encoder_name)() # Target encoders also need y - if enc._get_tags().get('supervised_encoder'): + if enc.__sklearn_tags__().target_tags.required: obtained = enc.fit(X, y).get_feature_names_out() expected = np.array(enc.transform(X, y).columns) else: @@ -595,7 +595,7 @@ def test_get_feature_names_out_drop_invariant(self): with self.subTest(encoder_name=encoder_name): enc = getattr(encoders, encoder_name)(drop_invariant=True) # Target encoders also need y - if enc._get_tags().get('supervised_encoder'): + if enc.__sklearn_tags__().target_tags.required: obtained = enc.fit(X, y).get_feature_names_out() expected = np.array(enc.transform(X, y).columns) else: @@ -686,7 +686,7 @@ def test_error_messages(self): y_bad = pd.Series([1, 0, 1, 0]) for encoder_name in encoders.__all__: enc = getattr(encoders, encoder_name)() - if not enc._get_tags().get('supervised_encoder'): + if not enc.__sklearn_tags__().target_tags.required: continue with self.subTest(encoder_name=encoder_name): self.assertRaises(ValueError, enc.fit, x, y_bad) @@ -725,7 +725,7 @@ def test_target_encoders(self): """ for encoder_name in encoders.__all__: enc = getattr(encoders, encoder_name)() - if not enc._get_tags().get('supervised_encoder'): + if not enc.__sklearn_tags__().target_tags.required: continue with self.subTest(encoder_name=encoder_name): enc = getattr(encoders, encoder_name)(return_df=False)