h+ddlZddlmZddlZddlmZddlmZddl m Z m Z ddl m Z dd lmZdd lmZdd lmZdd lmZmZdd lmZmZmZGdde e e ZdS)N)Real)sparse)linprog) BaseEstimatorRegressorMixin) LinearModel)ConvergenceWarning)_safe_indexing)_check_sample_weight) sp_version parse_version)HiddenInterval StrOptionsc eZdZUdZeedddgeedddgdgehd eed hgedgd Z ee d <d ddd dd dZ ddZ dS)QuantileRegressoraI 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. 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` will change 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)) 0.8 rr neither)closedNleftboolean>revised simplexhighshighs-ds highs-ipminterior-pointwarnquantilealpha fit_interceptsolversolver_options_parameter_constraintsg?g?TcL||_||_||_||_||_dSNr)selfr r!r"r#r$s Z/var/www/html/Sam_Eipo/venv/lib/python3.11/site-packages/sklearn/linear_model/_quantile.py__init__zQuantileRegressor.__init__s/!  * ,c||||gddd\}}t||}|jd}|}|jr|dz }t j||jz}|jdkrtj dtd}nG|jd vr7ttd krtd |jd t|j}|dkr+ttd krtd |dtj|r|dvrtd |jd|j |dkrddi}n|j}t j|d} t'| } | t'|kr(|| }t)|| }t)|| }t jt jd|z|||jz|d|jz zg} |jr d| d<d| |<|dvrtj| |jd} |jrOtjt j| df|j} tj| || | | | gd}ntj|| | | gd}nmt j| } |jr6t j| df} t j| || | | | gd}nt j|| | | gd}|}t;| ||||}|j}|jsTddddd }tj d!|j d"|!|j d#zd$zd%z|j"ztF|d|||d|zz }|j$|_%|jr|dd|_&|d|_'n||_&d&|_'|S)'aFit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- self : object Returns self. )csccsrcooTF) accept_sparse y_numeric multi_outputr rzThe default solver will change from 'interior-point' to 'highs' in version 1.4. Set `solver='highs'` or to the desired solver to silence this warning.r)rrrz1.6.0zSolver z* is only available with scipy>=1.6.0, got z1.11.0z- is not anymore available in SciPy >= 1.11.0.)rrrz; does not support sparse X. Use solver 'highs' for example.Nlstsqrr) fill_valuer-)dtypeformat)shaper5)r6)axis)cA_eqb_eqmethodoptionszIteration limit reached.z!Problem appears to be infeasible.z Problem appears to be unbounded.z#Numerical difficulties encountered.)r rzDLinear programming for QuantileRegressor did not succeed. Status is z: zunknown reason zResult message of linprog: g)(_validate_params_validate_datar r7r"npsumr!r#warningsr FutureWarningrr ValueErrorrissparser$nonzerolenr concatenatefullr eyer5 csc_matrixoneshstackrxsuccessstatus setdefaultmessager nitn_iter_coef_ intercept_)r(Xy sample_weight n_featuresn_paramsr!r#r$indices n_indicesr9rMrOr:r;resultsolutionfailureparamss r)fitzQuantileRegressor.fits& "" /// #  1-]A>> WQZ     MH }%% 2 ;& M      &FF [   =11117$+77*477  [F % % %* h8O8O*O*OO&OOO  ?1   &0R"R"R2$+222    &65E+E+E%t_NN!0N,*]++A.LL s=)) ) ))'2Mq'**Aq'**A NH 777 -T]!23      AaDAhK 7 7 7 *YageDDDC! G( 1~QW)U)U)UVV}dAuqb#t%DUSSS}a!S3$%7FFF&##C! Bw 1~..~tQr3&EANNN~q1"cC4&8qAAA"    8~ -658 G M/#]///$$V]4DEEF1 1 . ! #   )8)$x1x<0G'HHz   "DJ$QiDOODJ!DO r+r') __name__ __module__ __qualname____doc__rrrrdictr%__annotations__r*rer+r)rrsWWtXdAq;;;<(4D8889# J   F::vh'' ( (   ,!$$D, - - - - -mmmmmmr+r)rEnumbersrnumpyrCscipyrscipy.optimizerbaserr_baser exceptionsr utilsr utils.validationr utils.fixesrrutils._param_validationrrrrrlr+r)rxs*""""""00000000++++++""""""33333333333333BBBBBBBBBBiiiii ^]iiiiir+