a ;Za*@sddlZddlZddlmZmZmZddlmZddl m Z ddl m Z m Z ddlmZddlmZd gZGd d d eeZdS) N) BaseEstimatorRegressorMixinclone)check_is_fitted) _safe_tags) check_array_safe_indexing)FunctionTransformer)NotFittedErrorTransformedTargetRegressorc@sReZdZdZddddddddZddZd d Zd d Zd dZe ddZ dS)r aMeta-estimator to regress on a transformed target. Useful for applying a non-linear transformation to the target `y` in regression problems. This transformation can be given as a Transformer such as the :class:`~sklearn.preprocessing.QuantileTransformer` or as a function and its inverse such as `np.log` and `np.exp`. The computation during :meth:`fit` is:: regressor.fit(X, func(y)) or:: regressor.fit(X, transformer.transform(y)) The computation during :meth:`predict` is:: inverse_func(regressor.predict(X)) or:: transformer.inverse_transform(regressor.predict(X)) Read more in the :ref:`User Guide `. .. versionadded:: 0.20 Parameters ---------- regressor : object, default=None Regressor object such as derived from :class:`~sklearn.base.RegressorMixin`. This regressor will automatically be cloned each time prior to fitting. If `regressor is None`, :class:`~sklearn.linear_model.LinearRegression` is created and used. transformer : object, default=None Estimator object such as derived from :class:`~sklearn.base.TransformerMixin`. Cannot be set at the same time as `func` and `inverse_func`. If `transformer is None` as well as `func` and `inverse_func`, the transformer will be an identity transformer. Note that the transformer will be cloned during fitting. Also, the transformer is restricting `y` to be a numpy array. func : function, default=None Function to apply to `y` before passing to :meth:`fit`. Cannot be set at the same time as `transformer`. The function needs to return a 2-dimensional array. If `func is None`, the function used will be the identity function. inverse_func : function, default=None Function to apply to the prediction of the regressor. Cannot be set at the same time as `transformer`. The function needs to return a 2-dimensional array. The inverse function is used to return predictions to the same space of the original training labels. check_inverse : bool, default=True Whether to check that `transform` followed by `inverse_transform` or `func` followed by `inverse_func` leads to the original targets. Attributes ---------- regressor_ : object Fitted regressor. transformer_ : object Transformer used in :meth:`fit` and :meth:`predict`. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying regressor exposes such an attribute when fit. .. versionadded:: 0.24 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. .. versionadded:: 1.0 See Also -------- sklearn.preprocessing.FunctionTransformer : Construct a transformer from an arbitrary callable. Notes ----- Internally, the target `y` is always converted into a 2-dimensional array to be used by scikit-learn transformers. At the time of prediction, the output will be reshaped to a have the same number of dimensions as `y`. See :ref:`examples/compose/plot_transformed_target.py `. Examples -------- >>> import numpy as np >>> from sklearn.linear_model import LinearRegression >>> from sklearn.compose import TransformedTargetRegressor >>> tt = TransformedTargetRegressor(regressor=LinearRegression(), ... func=np.log, inverse_func=np.exp) >>> X = np.arange(4).reshape(-1, 1) >>> y = np.exp(2 * X).ravel() >>> tt.fit(X, y) TransformedTargetRegressor(...) >>> tt.score(X, y) 1.0 >>> tt.regressor_.coef_ array([2.]) NT) transformerfunc inverse_func check_inversecCs"||_||_||_||_||_dS)N) regressorr rrr)selfrr rrrr6lib/python3.9/site-packages/sklearn/compose/_target.py__init__s z#TransformedTargetRegressor.__init__cCs|jdur(|jdus|jdur(tdnL|jdur@t|j|_n4|jdur\|jdur\tdt|j|jd|jd|_|j||jrt ddt d|j dd}t ||}|j |}t||j|std tdS) zCheck transformer and fit transformer. Create the default transformer, fit it and make additional inverse check on a subset (optional). NzE'transformer' and functions 'func'/'inverse_func' cannot both be set.z=When 'func' is provided, 'inverse_func' must also be providedT)rrZvalidaterr zThe provided functions or transformer are not strictly inverse of each other. If you are sure you want to proceed regardless, set 'check_inverse=False')r rr ValueErrorr transformer_r rfitslicemaxshaper transformnpZallcloseinverse_transformwarningswarn UserWarning)ryZ idx_selectedZy_selZy_sel_trrr_fit_transformers<     z+TransformedTargetRegressor._fit_transformercKst|dddddd}|j|_|jdkr4|dd}n|}|||j|}|jdkrr|jddkrr|jdd}|j d urdd l m }||_ n t |j |_ |j j||fi|t|j d r|j j|_|S) aBFit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. **fit_params : dict Parameters passed to the `fit` method of the underlying regressor. Returns ------- self : object Fitted estimator. FTZnumeric)Z accept_sparseZforce_all_finiteZ ensure_2dZdtypeZallow_ndrrZaxisNLinearRegressionfeature_names_in_)rndim _training_dimreshaper%rrrsqueezer linear_modelr) regressor_rrhasattrr*)rXr$Z fit_paramsZy_2dZy_transr)rrrrs0           zTransformedTargetRegressor.fitcKszt||jj|fi|}|jdkr<|j|dd}n |j|}|jdkrv|jdkrv|jddkrv|j dd}|S)aKPredict using the base regressor, applying inverse. The regressor is used to predict and the `inverse_func` or `inverse_transform` is applied before returning the prediction. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Samples. **predict_params : dict of str -> object Parameters passed to the `predict` method of the underlying regressor. Returns ------- y_hat : ndarray of shape (n_samples,) Predicted values. rr&rr') rr0predictr+rr r-r,rr.)rr2Zpredict_paramsZpredZ pred_transrrrr3s    z"TransformedTargetRegressor.predictcCs2|j}|dur ddlm}|}dt|dddS)Nrr(T multioutput)key)Z poor_scorer4)rr/r)r)rrr)rrr _more_tags s  z%TransformedTargetRegressor._more_tagsc CsNz t|Wn8tyD}z td|jj|WYd}~n d}~00|jjS)z+Number of features seen during :term:`fit`.z*{} object has no n_features_in_ attribute.N)rr AttributeErrorformat __class____name__r0n_features_in_)rZnferrrr;,s z)TransformedTargetRegressor.n_features_in_)N) r: __module__ __qualname____doc__rr%rr3r6propertyr;rrrrr sp ,@# )r!ZnumpyrbaserrrZutils.validationrZ utils._tagsrZutilsrr Z preprocessingr exceptionsr __all__r rrrrs