{Kg2*ddlZddlmZddlZddlmZddlmZddl m Z m Z m Z ddl mZddlmZdd lmZmZdd lmZmZdd lmZd d lmZGddee e Zy)N)Real)sparse)linprog) BaseEstimatorRegressorMixin _fit_context)ConvergenceWarning)_safe_indexing)Interval StrOptions) parse_version sp_version)_check_sample_weight) LinearModelceZdZUdZeedddgeedddgdgehd gedgd Zee d <d d dddd dZ e dddZ y)QuantileRegressoraH 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='highs' Method used by :func:`scipy.optimize.linprog` to solve the linear programming formulation. From `scipy>=1.6.0`, it is recommended to use the highs methods because they are the fastest ones. Solvers "highs-ds", "highs-ipm" and "highs" support sparse input data and, in fact, always convert to sparse csc. From `scipy>=1.11.0`, "interior-point" is not available anymore. .. versionchanged:: 1.4 The default of `solver` changed to `"highs"` in version 1.4. 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) >>> # the two following lines are optional in practice >>> from sklearn.utils.fixes import sp_version, parse_version >>> solver = "highs" if sp_version >= parse_version("1.6.0") else "interior-point" >>> reg = QuantileRegressor(quantile=0.8, solver=solver).fit(X, y) >>> np.mean(y <= reg.predict(X)) np.float64(0.8) rrneither)closedNleftboolean>revised simplexhighshighs-ds highs-ipminterior-pointquantilealpha fit_interceptsolversolver_options_parameter_constraintsg?g?TrcJ||_||_||_||_||_yNr)selfrr r!r"r#s b/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/sklearn/linear_model/_quantile.py__init__zQuantileRegressor.__init__s)!  * ,)prefer_skip_nested_validationc|j||gddd\}}t||}|jd}|}|jr|dz }t j ||j z}|jdvr1ttdkrtd|jd t|j}|d k(r!ttd k\rtd|d tj|r|d vrtd|jd|j |d k(rddi}n |j}t j|d} t| } | t|kr|| }t!|| }t!|| }t j"t j$d|z|||j&z|d|j&z zg} |jr d| d<d| |<|d vrtj(| |j*d} |jrWtj,t j.| df|j*} tj0| || | | | gd}ntj0|| | | gd}nvt j(| } |jr8t j.| df} t j"| || | | | gd}nt j"|| | | gd}|}t3| ||||}|j4}|j6s_ddddd}t9j:d|j<d |j?|j<d!zd"zd#z|j@ztB|d|||d|zz }|jD|_#|jr|dd|_$|d|_%|S||_$d$|_%|S)%aFit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix} 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. )csccsrcooTF) accept_sparse y_numeric multi_outputr)rrrz1.6.0zSolver z* is only available with scipy>=1.6.0, got rz1.11.0z- is not anymore available in SciPy >= 1.11.0.)rrrz; does not support sparse X. Use solver 'highs' for example.Nlstsqrr) fill_valuer-)dtypeformat)shaper5)r6)axis)cA_eqb_eqmethodoptionszIteration 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: g)&_validate_datarr7r!npsumr r"rr ValueErrorrissparser#nonzerolenr concatenatefullreyer5 csc_matrixoneshstackrxsuccesswarningswarnstatus setdefaultmessager nitn_iter_coef_ intercept_)r'Xy sample_weight n_featuresn_paramsr r"r#indices n_indicesr9rJrLr:r;resultsolutionfailureparamss r(fitzQuantileRegressor.fits ("" / # 1-]A> WWQZ     MH }% 2 ;;  =11$++'**47  [[F % %* h8O*O&!NO  ??1 &0R"R$++'22     &65E+E%t_N!00N,**]+A.L s=) ))'2Mq'*Aq'*A NNH 7 -T]]!23     AaDAhK 7 7 **YaggeDC!!(( 1~QWW)UV}}dAuqb#t%DUS}}a!S3$%7F&&#C!!ww 1~.~~tQr3&EAN~~q1"cC4&8qA"  88~~-658 G MM#]]O2/$$V]]4DEF1 1 .. ! # )8$x1x<'HHzz   DJ$QiDO  DJ!DO r*r&) __name__ __module__ __qualname____doc__r rr dictr$__annotations__r)r rdr*r(rrsWtdAq;<4D89#     ,$D* -5d6dr*r)rPnumbersrnumpyrBscipyrscipy.optimizerbaserrr exceptionsr utilsr utils._param_validationr r utils.fixesrrutils.validationr_baserrrkr*r(rws@">>+":33` ^]`r*