B \"@shddlZddlZddlmZmZmZddlmZddl m Z m Z ddl m Z dgZGdddeeZdS) N) BaseEstimatorRegressorMixinclone)check_is_fitted) check_array safe_indexing)FunctionTransformerTransformedTargetRegressorc@s4eZdZdZd ddZddZd dd Zd d ZdS)r a Meta-estimator to regress on a transformed target. Useful for applying a non-linear transformation in regression problems. This transformation can be given as a Transformer such as the QuantileTransformer or as a function and its inverse such as ``log`` and ``exp``. The computation during ``fit`` is:: regressor.fit(X, func(y)) or:: regressor.fit(X, transformer.transform(y)) The computation during ``predict`` is:: inverse_func(regressor.predict(X)) or:: transformer.inverse_transform(regressor.predict(X)) Read more in the :ref:`User Guide `. Parameters ---------- regressor : object, default=LinearRegression() Regressor object such as derived from ``RegressorMixin``. This regressor will automatically be cloned each time prior to fitting. transformer : object, default=None Estimator object such as derived from ``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, optional Function to apply to ``y`` before passing to ``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, optional Function to apply to the prediction of the regressor. Cannot be set at the same time as ``transformer`` as well. 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 ``fit`` and ``predict``. 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) # doctest: +ELLIPSIS TransformedTargetRegressor(...) >>> tt.score(X, y) 1.0 >>> tt.regressor_.coef_ array([2.]) 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 `. NTcCs"||_||_||_||_||_dS)N) regressor transformerfunc inverse_func check_inverse)selfr r r rrr6lib/python3.7/site-packages/sklearn/compose/_target.py__init__ks 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)r rZvalidaterr 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 r r ValueErrorr transformer_r rfitslicemaxshaper transformnpZallcloseinverse_transformwarningswarn UserWarning)ryZ idx_selectedZy_selZy_sel_trrr_fit_transformerss&        z+TransformedTargetRegressor._fit_transformercCst|ddddd}|j|_|jdkr2|dd}n|}|||j|}|jdkrp|jddkrp|jdd}|j d krdd l m }||_ n t |j |_ |d kr|j ||n|j j|||d |S) aoFit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix}, 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, shape (n_samples,) Target values. sample_weight : array-like, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- self : object FTZnumeric)Z accept_sparseZforce_all_finiteZ ensure_2dZdtyperr)axisN)LinearRegression) sample_weight)rndim _training_dimreshaper#rrrsqueezer Z linear_modelr& regressor_rr)rXr"r'Zy_2dZy_transr&rrrrs$         zTransformedTargetRegressor.fitcCstt|d|j|}|jdkr6|j|dd}n |j|}|jdkrp|jdkrp|jddkrp|j dd}|S)aPredict 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}, shape = (n_samples, n_features) Samples. Returns ------- y_hat : array, shape = (n_samples,) Predicted values. r,rr$r)r%) rr,predictr(rrr*r)rr+)rr-ZpredZ pred_transrrrr.s      z"TransformedTargetRegressor.predict)NNNNT)N)__name__ __module__ __qualname____doc__rr#rr.rrrrr s Y $ 8)rZnumpyrbaserrrZutils.validationrZutilsrrZ preprocessingr __all__r rrrrs