a ;Za%@sxddlZddlZddlmZddlmZmZddlm Z ddl m Z ddl m Z dd lmZmZGd d d e eeZdS) N)linprog) BaseEstimatorRegressorMixin) LinearModel)ConvergenceWarning)_check_sample_weight) sp_version parse_versionc@s0eZdZdZdddddddd Zd d d ZdS) QuantileRegressora6 Linear regression model that predicts conditional quantiles. The linear :class:`QuantileRegressor` optimizes the pinball loss for a desired `quantile` and is robust to outliers. This model uses an L1 regularization like :class:`~sklearn.linear_model.Lasso`. Read more in the :ref:`User Guide `. .. versionadded:: 1.0 Parameters ---------- quantile : float, default=0.5 The quantile that the model tries to predict. It must be strictly between 0 and 1. If 0.5 (default), the model predicts the 50% quantile, i.e. the median. alpha : float, default=1.0 Regularization constant that multiplies the L1 penalty term. fit_intercept : bool, default=True Whether or not to fit the intercept. solver : {'highs-ds', 'highs-ipm', 'highs', 'interior-point', 'revised simplex'}, default='interior-point' Method used by :func:`scipy.optimize.linprog` to solve the linear programming formulation. Note that the highs methods are recommended for usage with `scipy>=1.6.0` because they are the fastest ones. solver_options : dict, default=None Additional parameters passed to :func:`scipy.optimize.linprog` as options. If `None` and if `solver='interior-point'`, then `{"lstsq": True}` is passed to :func:`scipy.optimize.linprog` for the sake of stability. Attributes ---------- coef_ : array of shape (n_features,) Estimated coefficients for the features. intercept_ : float The intercept of the model, aka bias term. n_features_in_ : int Number of features seen during :term:`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 n_iter_ : int The actual number of iterations performed by the solver. See Also -------- Lasso : The Lasso is a linear model that estimates sparse coefficients with l1 regularization. HuberRegressor : Linear regression model that is robust to outliers. Examples -------- >>> from sklearn.linear_model import QuantileRegressor >>> import numpy as np >>> n_samples, n_features = 10, 2 >>> rng = np.random.RandomState(0) >>> y = rng.randn(n_samples) >>> X = rng.randn(n_samples, n_features) >>> reg = QuantileRegressor(quantile=0.8).fit(X, y) >>> np.mean(y <= reg.predict(X)) 0.8 g??Tinterior-pointNquantilealpha fit_interceptsolversolver_optionscCs"||_||_||_||_||_dS)Nr)selfrrrrrr=lib/python3.9/site-packages/sklearn/linear_model/_quantile.py__init___s zQuantileRegressor.__init__c Cs |j||dddd\}}t||}|jd}|}|jr>|d7}|jdkrZt||j}ntd|j|jdks~|jdkrtd |jt |jt std |j|j d vrtd |j nV|j d krt t dkrtdt n0|j dvrt t dkrtd|j dt |jdurFt |jtsFtd|j|jdurh|j dkrhddi}n|j}|dk}tt|} ttjd||d|||j||d|jg} |jrd| d<d| |<tjt| df||t| df || t| t|  gdd} n.tj|||| t| t|  gdd} ||} t| | | |j |d} | j}| jsddddd }td!| jd"|| jd#d$d%| jt|d|||d|}| j|_|jr|dd|_ |d|_!n ||_ d|_!|S)&aFit the model according to the given training data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- self : object Returns self. FT)Z accept_sparseZ y_numericZ multi_outputrrz1Penalty alpha must be a non-negative number, got r gz5Quantile should be strictly between 0.0 and 1.0, got z-The argument fit_intercept must be bool, got )highs-ds highs-ipmhighsrrevised simplexz'Invalid value for argument solver, got rz1.3.0zBSolver 'revised simplex' is only available with scipy>=1.3.0, got )rrrz1.6.0zSolver z* is only available with scipy>=1.6.0, got NzMInvalid value for argument solver_options, must be None or a dictionary, got rZlstsqr)Z fill_value)Zaxis)cA_eqb_eqmethodZoptionszIteration limit reached.z!Problem appears to be infeasible.z Problem appears to be unbounded.z#Numerical difficulties encountered.)rrzDLinear programming for QuantileRegressor did not succeed. Status is z: zunknown reason zResult message of linprog: )"Z_validate_datar shaperrnpsum ValueErrorr isinstanceboolrr r rdictintZ concatenateZfullZonesZeyerxZsuccesswarningswarnZstatus setdefaultmessagerZnitZn_iter_Zcoef_Z intercept_)rXyZ sample_weightZ n_featuresZn_paramsrrmaskZn_maskrrrresultZsolutionZfailureparamsrrrfitns                 $   zQuantileRegressor.fit)N)__name__ __module__ __qualname____doc__rr6rrrrr sQ r )r-Znumpyr%Zscipy.optimizerbaserrZ_baser exceptionsrZutils.validationr Z utils.fixesr r r rrrrs