h-ddlZddlZddlmZmZmZmZddlm Z ddl m Z ddl m Z mZddlmZddlmZdd lmZd gZGd d eeZdS) N) BaseEstimatorRegressorMixin _fit_contextclone)NotFittedError)FunctionTransformer)_safe_indexing check_array) HasMethods) _safe_tags)check_is_fittedTransformedTargetRegressorceZdZUdZeddgdgeddgedgedgdgdZeed< ddddd d d Z d Z e d dZ dZ dZedZdS)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)rrrrcL||_||_||_||_||_dSNr)selfrrrrrs _/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/sklearn/compose/_target.py__init__z#TransformedTargetRegressor.__init__s0#& (*c |j|j|jtd|jt |j|_nD|j|jtdt |j|jd|j|_|j||jrtddtd|j ddz}t||}|j |}tj||j|st#jd t&dSdSdS) 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_r rrslicemaxshaper rnpallcloseinverse_transformwarningswarn UserWarning)ry idx_selectedy_sely_sel_ts r_fit_transformerz+TransformedTargetRegressor._fit_transformersu   ' I !T%6%BW   ) %d&6 7 7D  y$):)B S!4Y!."0 !!!D  a      tSAGAJ"4D-E-EFFL"1l33E'11%88G;ud&7&I&I'&R&RSS  6      r!F)prefer_skip_nested_validationc r|td|jjdt|dddddd}|j|_|jd kr|d d }n|}|||j |}|jd kr'|j d d kr| d }|j d d l m}||_nt!|j |_|jj||fi|t%|jdr|jj|_|S)aBFit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) Target values. **fit_params : dict Parameters passed to the `fit` method of the underlying regressor. Returns ------- self : object Fitted estimator. NzThis z= estimator requires y to be passed, but the target y is None.r1FTnumeric) input_name accept_sparseforce_all_finite ensure_2ddtypeallow_ndr$raxisLinearRegressionfeature_names_in_)r& __class____name__r ndim _training_dimreshaper5r'rr*squeezer linear_modelrC regressor_rrhasattrrD)rXr1 fit_paramsy_2dy_transrCs rrzTransformedTargetRegressor.fits{2 9E/EEE   !   V 6Q;;99R##DDD d####--d33 <1  q!1Q!6!6oo1o--G > ! 7 7 7 7 7 7..00DOO#DN33DOAw55*555 4?$7 8 8 G%)_%FD " r!c lt||jj|fi|}|jdkr/|j|dd}n|j|}|jdkr2|jdkr'|jddkr| d}|S)aKPredict using the base regressor, applying inverse. The regressor is used to predict and the `inverse_func` or `inverse_transform` is applied before returning the prediction. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Samples. **predict_params : dict of str -> object Parameters passed to the `predict` method of the underlying regressor. Returns ------- y_hat : ndarray of shape (n_samples,) Predicted values. r$r?rr@) rrLrrGr'r-rIrHr*rJ)rrNpredict_paramspred pred_transs rrz"TransformedTargetRegressor.predicts( &t&q;;N;; 9>>*<rqs EEEEEEEEEEEE''''''//////////////000000$$$$$$...... ' ({.{.{.{.{.{.{.{.{.{.r!