from numbers import Integral, Real import numpy as np from ..base import OneToOneFeatureMixin, _fit_context from ..utils._param_validation import Interval, StrOptions from ..utils.multiclass import type_of_target from ..utils.validation import _check_y, check_consistent_length from ._encoders import _BaseEncoder from ._target_encoder_fast import _fit_encoding_fast, _fit_encoding_fast_auto_smooth class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): """Target Encoder for regression and classification targets. Each category is encoded based on a shrunk estimate of the average target values for observations belonging to the category. The encoding scheme mixes the global target mean with the target mean conditioned on the value of the category. [MIC]_ :class:`TargetEncoder` considers missing values, such as `np.nan` or `None`, as another category and encodes them like any other category. Categories that are not seen during :meth:`fit` are encoded with the target mean, i.e. `target_mean_`. Read more in the :ref:`User Guide `. .. note:: `fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a cross fitting scheme is used in `fit_transform` for encoding. See the :ref:`User Guide ` for details. .. versionadded:: 1.3 Parameters ---------- categories : "auto" or list of shape (n_features,) of array-like, default="auto" Categories (unique values) per feature: - `"auto"` : Determine categories automatically from the training data. - list : `categories[i]` holds the categories expected in the i-th column. The passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values. The used categories are stored in the `categories_` fitted attribute. target_type : {"auto", "continuous", "binary"}, default="auto" Type of target. - `"auto"` : Type of target is inferred with :func:`~sklearn.utils.multiclass.type_of_target`. - `"continuous"` : Continuous target - `"binary"` : Binary target .. note:: The type of target inferred with `"auto"` may not be the desired target type used for modeling. For example, if the target consisted of integers between 0 and 100, then :func:`~sklearn.utils.multiclass.type_of_target` will infer the target as `"multiclass"`. In this case, setting `target_type="continuous"` will specify the target as a regression problem. The `target_type_` attribute gives the target type used by the encoder. smooth : "auto" or float, default="auto" The amount of mixing of the target mean conditioned on the value of the category with the global target mean. A larger `smooth` value will put more weight on the global target mean. If `"auto"`, then `smooth` is set to an empirical Bayes estimate. cv : int, default=5 Determines the number of folds in the cross fitting strategy used in :meth:`fit_transform`. For classification targets, `StratifiedKFold` is used and for continuous targets, `KFold` is used. shuffle : bool, default=True Whether to shuffle the data in :meth:`fit_transform` before splitting into folds. Note that the samples within each split will not be shuffled. random_state : int, RandomState instance or None, default=None When `shuffle` is True, `random_state` affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. Attributes ---------- encodings_ : list of shape (n_features,) of ndarray Encodings learnt on all of `X`. For feature `i`, `encodings_[i]` are the encodings matching the categories listed in `categories_[i]`. categories_ : list of shape (n_features,) of ndarray The categories of each feature determined during fitting or specified in `categories` (in order of the features in `X` and corresponding with the output of :meth:`transform`). target_type_ : str Type of target. target_mean_ : float The overall mean of the target. This value is only used in :meth:`transform` to encode categories. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. See Also -------- OrdinalEncoder : Performs an ordinal (integer) encoding of the categorical features. Contrary to TargetEncoder, this encoding is not supervised. Treating the resulting encoding as a numerical features therefore lead arbitrarily ordered values and therefore typically lead to lower predictive performance when used as preprocessing for a classifier or regressor. OneHotEncoder : Performs a one-hot encoding of categorical features. This unsupervised encoding is better suited for low cardinality categorical variables as it generate one new feature per unique category. References ---------- .. [MIC] :doi:`Micci-Barreca, Daniele. "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems" SIGKDD Explor. Newsl. 3, 1 (July 2001), 27–32. <10.1145/507533.507538>` Examples -------- With `smooth="auto"`, the smoothing parameter is set to an empirical Bayes estimate: >>> import numpy as np >>> from sklearn.preprocessing import TargetEncoder >>> X = np.array([["dog"] * 20 + ["cat"] * 30 + ["snake"] * 38], dtype=object).T >>> y = [90.3] * 5 + [80.1] * 15 + [20.4] * 5 + [20.1] * 25 + [21.2] * 8 + [49] * 30 >>> enc_auto = TargetEncoder(smooth="auto") >>> X_trans = enc_auto.fit_transform(X, y) >>> # A high `smooth` parameter puts more weight on global mean on the categorical >>> # encodings: >>> enc_high_smooth = TargetEncoder(smooth=5000.0).fit(X, y) >>> enc_high_smooth.target_mean_ 44... >>> enc_high_smooth.encodings_ [array([44..., 44..., 44...])] >>> # On the other hand, a low `smooth` parameter puts more weight on target >>> # conditioned on the value of the categorical: >>> enc_low_smooth = TargetEncoder(smooth=1.0).fit(X, y) >>> enc_low_smooth.encodings_ [array([20..., 80..., 43...])] """ _parameter_constraints: dict = { "categories": [StrOptions({"auto"}), list], "target_type": [StrOptions({"auto", "continuous", "binary"})], "smooth": [StrOptions({"auto"}), Interval(Real, 0, None, closed="left")], "cv": [Interval(Integral, 2, None, closed="left")], "shuffle": ["boolean"], "random_state": ["random_state"], } def __init__( self, categories="auto", target_type="auto", smooth="auto", cv=5, shuffle=True, random_state=None, ): self.categories = categories self.smooth = smooth self.target_type = target_type self.cv = cv self.shuffle = shuffle self.random_state = random_state @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y): """Fit the :class:`TargetEncoder` to X and y. Parameters ---------- X : array-like of shape (n_samples, n_features) The data to determine the categories of each feature. y : array-like of shape (n_samples,) The target data used to encode the categories. Returns ------- self : object Fitted encoder. """ self._fit_encodings_all(X, y) return self @_fit_context(prefer_skip_nested_validation=True) def fit_transform(self, X, y): """Fit :class:`TargetEncoder` and transform X with the target encoding. .. note:: `fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a cross fitting scheme is used in `fit_transform` for encoding. See the :ref:`User Guide `. for details. Parameters ---------- X : array-like of shape (n_samples, n_features) The data to determine the categories of each feature. y : array-like of shape (n_samples,) The target data used to encode the categories. Returns ------- X_trans : ndarray of shape (n_samples, n_features) Transformed input. """ from ..model_selection import KFold, StratifiedKFold # avoid circular import X_ordinal, X_known_mask, y, n_categories = self._fit_encodings_all(X, y) # The cv splitter is voluntarily restricted to *KFold to enforce non # overlapping validation folds, otherwise the fit_transform output will # not be well-specified. if self.target_type_ == "continuous": cv = KFold(self.cv, shuffle=self.shuffle, random_state=self.random_state) else: cv = StratifiedKFold( self.cv, shuffle=self.shuffle, random_state=self.random_state ) X_out = np.empty_like(X_ordinal, dtype=np.float64) X_unknown_mask = ~X_known_mask for train_idx, test_idx in cv.split(X, y): X_train, y_train = X_ordinal[train_idx, :], y[train_idx] y_mean = np.mean(y_train) if self.smooth == "auto": y_variance = np.var(y_train) encodings = _fit_encoding_fast_auto_smooth( X_train, y_train, n_categories, y_mean, y_variance ) else: encodings = _fit_encoding_fast( X_train, y_train, n_categories, self.smooth, y_mean ) self._transform_X_ordinal( X_out, X_ordinal, X_unknown_mask, test_idx, encodings, y_mean ) return X_out def transform(self, X): """Transform X with the target encoding. .. note:: `fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a cross fitting scheme is used in `fit_transform` for encoding. See the :ref:`User Guide `. for details. Parameters ---------- X : array-like of shape (n_samples, n_features) The data to determine the categories of each feature. Returns ------- X_trans : ndarray of shape (n_samples, n_features) Transformed input. """ X_ordinal, X_valid = self._transform( X, handle_unknown="ignore", force_all_finite="allow-nan" ) X_out = np.empty_like(X_ordinal, dtype=np.float64) self._transform_X_ordinal( X_out, X_ordinal, ~X_valid, slice(None), self.encodings_, self.target_mean_, ) return X_out def _fit_encodings_all(self, X, y): """Fit a target encoding with all the data.""" from ..preprocessing import LabelEncoder # avoid circular import check_consistent_length(X, y) self._fit(X, handle_unknown="ignore", force_all_finite="allow-nan") if self.target_type == "auto": accepted_target_types = ("binary", "continuous") inferred_type_of_target = type_of_target(y, input_name="y") if inferred_type_of_target not in accepted_target_types: raise ValueError( f"Target type was inferred to be {inferred_type_of_target!r}. Only" f" {accepted_target_types} are supported." ) self.target_type_ = inferred_type_of_target else: self.target_type_ = self.target_type if self.target_type_ == "binary": y = LabelEncoder().fit_transform(y) else: # continuous y = _check_y(y, y_numeric=True, estimator=self) self.target_mean_ = np.mean(y) X_ordinal, X_known_mask = self._transform( X, handle_unknown="ignore", force_all_finite="allow-nan" ) n_categories = np.fromiter( (len(category_for_feature) for category_for_feature in self.categories_), dtype=np.int64, count=len(self.categories_), ) if self.smooth == "auto": y_variance = np.var(y) self.encodings_ = _fit_encoding_fast_auto_smooth( X_ordinal, y, n_categories, self.target_mean_, y_variance ) else: self.encodings_ = _fit_encoding_fast( X_ordinal, y, n_categories, self.smooth, self.target_mean_ ) return X_ordinal, X_known_mask, y, n_categories @staticmethod def _transform_X_ordinal( X_out, X_ordinal, X_unknown_mask, indices, encodings, y_mean ): """Transform X_ordinal using encodings.""" for f_idx, encoding in enumerate(encodings): X_out[indices, f_idx] = encoding[X_ordinal[indices, f_idx]] X_out[X_unknown_mask[:, f_idx], f_idx] = y_mean def _more_tags(self): return {"requires_y": True}