U 9%e-@sddlZddlZddlmZmZmZmZddlm Z ddl m Z ddl m Z mZddlmZddlmZdd lmZd gZGd d d eeZdS) N) BaseEstimatorRegressorMixin _fit_contextclone)NotFittedError)FunctionTransformer)_safe_indexing check_array) HasMethods) _safe_tags)check_is_fittedTransformedTargetRegressorc@seZdZUdZeddgdgeddgedgedgdgdZeed<ddddd d d d Z d dZ e ddddZ ddZ ddZeddZdS)raMeta-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`. 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.]) For a more detailed example use case refer to :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py`. fitpredictN transformboolean regressor transformerfunc inverse_func check_inverse_parameter_constraintsT)rrrrcCs"||_||_||_||_||_dS)Nr)selfrrrrrrV/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/sklearn/compose/_target.py__init__s z#TransformedTargetRegressor.__init__cCs|jdk r(|jdk s|jdk r(tdnL|jdk r@t|j|_n4|jdk r\|jdkr\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)rrvalidaterr 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')rrr ValueErrorr transformer_rrrslicemaxshaper rnpZallcloseinverse_transformwarningswarn UserWarning)ryZ idx_selectedZy_selZy_sel_trrr_fit_transformers<     z+TransformedTargetRegressor._fit_transformerF)Zprefer_skip_nested_validationc Ks|dkrtd|jjdt|ddddddd}|j|_|jd krR|d d }n|}|||j |}|jd kr|j d d kr|j d d }|j dkrd d l m}||_n t|j |_|jj||f|t|jdr|jj|_|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. NzThis z= estimator requires y to be passed, but the target y is None.r+FTnumeric)Z input_nameZ accept_sparseZforce_all_finiteZ ensure_2dZdtypeZallow_ndrrZaxisLinearRegressionfeature_names_in_)r! __class____name__r ndim _training_dimreshaper,r"rr%squeezer linear_modelr1 regressor_rrhasattrr2)rXr+Z fit_paramsZy_2dZy_transr1rrrrs:           zTransformedTargetRegressor.fitcKsvt||jj|f|}|jdkr8|j|dd}n |j|}|jdkrr|jdkrr|jddkrr|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/) r r:rr5r"r'r7r6r%r8)rr<Zpredict_paramspredZ pred_transrrrrs    z"TransformedTargetRegressor.predictcCs2|j}|dkr ddlm}|}dt|dddS)Nrr0T multioutput)key)Z poor_scorer>)rr9r1r )rrr1rrr _more_tags5s  z%TransformedTargetRegressor._more_tagsc CsLz t|Wn6tk rB}ztd|jj|W5d}~XYnX|jjS)z+Number of features seen during :term:`fit`.z*{} object has no n_features_in_ attribute.N)r rAttributeErrorformatr3r4r:n_features_in_)rZnferrrrCAs z)TransformedTargetRegressor.n_features_in_)N)r4 __module__ __qualname____doc__r callablerdict__annotations__rr,rrrr@propertyrCrrrrrs. o   . F# )r(numpyr&baserrrr exceptionsrZ preprocessingrutilsr r Zutils._param_validationr Z utils._tagsr Zutils.validationr __all__rrrrrs