ó ‡ˆ\c@sddlZddlZddlmZmZmZddlmZddl m Z m Z ddl m Z dgZdeefd„ƒYZdS( iÿÿÿÿNi(t BaseEstimatortRegressorMixintclone(tcheck_is_fitted(t check_arrayt safe_indexing(tFunctionTransformertTransformedTargetRegressorcBsDeZdZdddded„Zd„Zdd„Zd„ZRS(sµ 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 `. cCs1||_||_||_||_||_dS(N(t regressort transformertfunct inverse_funct check_inverse(tselfRR R R R ((s6lib/python2.7/site-packages/sklearn/compose/_target.pyt__init__ks     c CsR|jd k r<|jd k s-|jd k r<tdƒ‚n~|jd k r`t|jƒ|_nZ|jd k r|jd krtdƒ‚ntd|jd|jdtd|j ƒ|_|jj |ƒ|j rNt d d t d|j dd ƒƒ}t||ƒ}|jj|ƒ}tj||jj|ƒƒsNtjd tƒqNnd S( s¢Check transformer and fit transformer. Create the default transformer, fit it and make additional inverse check on a subset (optional). sE'transformer' and functions 'func'/'inverse_func' cannot both be set.s=When 'func' is provided, 'inverse_func' must also be providedR R tvalidateR iii s—The provided functions or transformer are not strictly inverse of each other. If you are sure you want to proceed regardless, set 'check_inverse=False'N(R tNoneR R t ValueErrorRt transformer_RtTrueR tfittslicetmaxtshapeRt transformtnptallclosetinverse_transformtwarningstwarnt UserWarning(R tyt idx_selectedty_selty_sel_t((s6lib/python2.7/site-packages/sklearn/compose/_target.pyt_fit_transformerss& &  c Cs/t|dtdtdtddƒ}|j|_|jdkrT|jddƒ}n|}|j|ƒ|jj|ƒ}|jdkr°|j ddkr°|j d dƒ}n|j d krÞdd l m}|ƒ|_nt|j ƒ|_|d kr|jj||ƒn|jj||d |ƒ|S( soFit 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 t accept_sparsetforce_all_finitet ensure_2dtdtypetnumericiiÿÿÿÿitaxis(tLinearRegressiont sample_weightN(RtFalseRtndimt _training_dimtreshapeR#RRRtsqueezeRRt linear_modelR*t regressor_RR(R tXRR+ty_2dty_transR*((s6lib/python2.7/site-packages/sklearn/compose/_target.pyR—s$  " cCs«t|dƒ|jj|ƒ}|jdkrO|jj|jddƒƒ}n|jj|ƒ}|jdkr§|jdkr§|jddkr§|j ddƒ}n|S(s½Predict 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. R2iiÿÿÿÿiR)( RR2tpredictR-RRR/R.RR0(R R3tpredt pred_trans((s6lib/python2.7/site-packages/sklearn/compose/_target.pyR6Ïs  "N( t__name__t __module__t__doc__RRRR#RR6(((s6lib/python2.7/site-packages/sklearn/compose/_target.pyRs Y $ 8(RtnumpyRtbaseRRRtutils.validationRtutilsRRt preprocessingRt__all__R(((s6lib/python2.7/site-packages/sklearn/compose/_target.pyts