-i(SSKrSSKJr SSKrSSKJr SSKJr SSK J r J r J r SSK Jr SSKJr SS KJrJr SS KJrJr SS KJrJr S S KJr "SS\\ \ 5rg)N)Real)sparse)linprog) BaseEstimatorRegressorMixin _fit_context)ConvergenceWarning)_safe_indexing)Interval StrOptions) parse_version sp_version)_check_sample_weight validate_data) LinearModelc^\rSrSr%Sr\"\SSSS9/\"\SSSS9/S /\"1S k5/\S/S .r \\ S 'S SSSSS .Sjr \ "SS9SSj5r U4SjrSrU=r$)QuantileRegressora 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. 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 >>> reg = QuantileRegressor(quantile=0.8).fit(X, y) >>> np.mean(y <= reg.predict(X)) np.float64(0.8) rrneither)closedNleftboolean>highshighs-ds highs-ipminterior-pointrevised simplexquantilealpha fit_interceptsolversolver_options_parameter_constraintsg?g?Trc@XlX lX0lX@lXPlgNr )selfr!r"r#r$r%s Q/var/www/html/venv/lib/python3.13/site-packages/sklearn/linear_model/_quantile.py__init__QuantileRegressor.__init__s!  * ,)prefer_skip_nested_validationc [UUU/SQSSS9up[X15nURSnUnUR(aUS- n[R "U5UR -nURS:Xa,[[S5:a[SURS 35e[R"U5(a)URS ;a[SURS 35eURcURS:XaS S0nO URn[R"U5Sn[U5n U [U5:aX8n[!X5n[!X(5n[R""[R$"SU-US9X0R&-USUR&- -/5n UR(a SU S'SX'URS ;a[R("XR*SS9n UR(aR[R,"[R."U S4UR*S95n [R0"XU *U*X*/SS9n O[R0"X*X*/SS9n Ou[R("U 5n UR(a5[R."U S45n [R""XU *U*X*/SS9n O[R""X*X*/SS9n Un[3U U UURUS9nUR4nUR6(d`SSSSS.n[8R:"SUR<S3UR?UR<S5-S-S -UR@-[B5 US UUUSU-- nURDUl#UR(aUSS Ul$USUl%U$UUl$S!Ul%U$)"a]Fit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- self : object Returns self. )csccsrcooTF) accept_sparse y_numeric multi_outputrrz1.11.0zSolver z- is not anymore available in SciPy >= 1.11.0.)rrrz; does not support sparse X. Use solver 'highs' for example.Nlstsqrr) fill_valuer0)dtypeformat)shaper8)r9)axis)cA_eqb_eqmethodoptionszIteration limit reached.z!Problem appears to be infeasible.z Problem appears to be unbounded.z#Numerical difficulties encountered.)rrzDLinear programming for QuantileRegressor did not succeed. Status is z: zunknown reason zResult message of linprog: g)&rrr:r#npsumr"r$rr ValueErrorrissparser%nonzerolenr concatenatefullr!eyer8 csc_matrixoneshstackrxsuccesswarningswarnstatus setdefaultmessager nitn_iter_coef_ intercept_)r)Xy sample_weight n_featuresn_paramsr"r%indices n_indicesr<rLrNr=r>resultsolutionfailureparamss r*fitQuantileRegressor.fits(  /  -]> WWQZ     MH }% 2 ;;* *z]8=T/T$++&ST  ??1  $++5W"W$++'22     &4;;:J+J%t_N!00N,**]+A.L s=) ))2Mq*Aq*A NNH 7 -T]]!23     AaDAK ;;< < **YggeDC!!(( 1~QWW)UV}}duqb#t%DUS}}aS$%7F&&#C!!ww 1~.~~tr3&EAN~~q"c4&8qA;;"  88~~-658 G MM#]]O2/$$V]]4DEF1 1 .. ! # )8$x1x<'HHzz   DJ$QiDO  DJ!DO r-cF>[TU]5nSURlU$)NT)super__sklearn_tags__ input_tagsr)r)tags __class__s r*rj"QuantileRegressor.__sklearn_tags__*s!w')!% r-)r"rYr#rZrXr!r$r%r()__name__ __module__ __qualname____firstlineno____doc__r rr dictr&__annotations__r+r rfrj__static_attributes__ __classcell__)rms@r*rrsVrdAq;<4D89#     ,$D* -5Y6Yvr-r)rRnumbersrnumpyrDscipyrscipy.optimizerbaserrr exceptionsr utilsr utils._param_validationr r utils.fixesrrutils.validationrr_baserrr-r*rs@">>+":3BY ^]Yr-