o ?Hh(@sddlZddlmZddlZddlmZddlmZddl m Z m Z m Z ddl mZddlmZdd lmZmZdd lmZmZdd lmZmZd d lmZGdddee e ZdS)N)Real)sparse)linprog) BaseEstimatorRegressorMixin _fit_context)ConvergenceWarning)_safe_indexing)Interval StrOptions) parse_version sp_version)_check_sample_weight validate_data) LinearModelcseZdZUdZeeddddgeeddddgdgehd gedgd Zee d <d d dddd ddZ e dddddZ fddZ ZS)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>revised simplexhighshighs-ds highs-ipminterior-pointquantilealpha fit_interceptsolversolver_options_parameter_constraintsg?g?TrcCs"||_||_||_||_||_dSNr)selfrrr r!r"r&^/home/air/sanwanet/gpt-api/venv/lib/python3.10/site-packages/sklearn/linear_model/_quantile.py__init__s   zQuantileRegressor.__init__)prefer_skip_nested_validationcCst|||gdddd\}}t||}|jd}|}|jr!|d7}t||j}|jdkr=tt dkr=t d|jd t |rP|jd vrPt d|jd |j d ur_|jdkr_d di}n|j }t|d}t|} | t|kr||}t||}t||}ttjd||d||j|d|jg} |jrd| d<d| |<|jd vrt j| |jdd} |jrt tj| df|jd} t j| || | | | gdd} n= 1.11.0.)rrrz; does not support sparse X. Use solver 'highs' for example.Nlstsqrr) fill_valuer*)dtypeformat)shaper2)r3)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)&rrr4r npsumrr!rr ValueErrorrissparser"nonzerolenr concatenatefullreyer2 csc_matrixoneshstackrxsuccesswarningswarnstatus setdefaultmessager nitn_iter_coef_ intercept_)r%Xy sample_weight n_featuresn_paramsrr"indices n_indicesr6rFrHr7r8resultsolutionfailureparamsr&r&r'fits          " "   zQuantileRegressor.fitcst}d|j_|S)NT)super__sklearn_tags__ input_tagsr)r%tags __class__r&r'rb*s z"QuantileRegressor.__sklearn_tags__r$)__name__ __module__ __qualname____doc__r rr dictr#__annotations__r(rr`rb __classcell__r&r&rer'rs* Y   r)rLnumbersrnumpyr>scipyrscipy.optimizerbaserrr exceptionsr utilsr utils._param_validationr r utils.fixesr rutils.validationrr_baserrr&r&r&r's