o tBh)@sddlZddlZddlmZddlmZddlmZm Z ddl m Z ddl m Z dd lmZdd lmZdd lmZmZGd d d e e eZdS)N)sparse)linprog) BaseEstimatorRegressorMixin) LinearModel)ConvergenceWarning)_safe_indexing)_check_sample_weight) sp_version parse_versionc@s0eZdZdZdddddddd Zd d d ZdS) 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='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. Solvers "highs-ds", "highs-ipm" and "highs" support sparse input data and, in fact, always convert to sparse csc. 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"||_||_||_||_||_dSNr)selfrrrrrru/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/linear_model/_quantile.py__init__cs   zQuantileRegressor.__init__cCs~|j||gdddd\}}t||}|jd}|}|jr!|d7}|jdkr/t||j}ntd|j|jdksA|jd krItd |jt |jt sWtd |j|j d vrdtd |j |j dvrzt t dkrztd|j dt t|r|j dvrtd|j d|jdurt |jtstd|j|jdur|j dkrddi}n|j}t|d}t|} | t|kr||}t||}t||}ttjd||d||j|d|jg} |jrd| d<d| |<|j dvr2tj| |jdd} |jr$ttj| df|jd} tj| || | | | gdd} n=1.6.0, got )r$r"r#z; does not support sparse X. Use solver 'highs' for example.NzMInvalid value for argument solver_options, must be None or a dictionary, got rlstsqr) fill_valuer)dtypeformat)shaper')r()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: ))_validate_datar r)rrnpsum ValueErrorr isinstanceboolrr r rissparserdictnonzerolenr concatenatefulleyer' csc_matrixoneshstackrxsuccesswarningswarnstatus setdefaultmessager nitn_iter_coef_ intercept_)rXy sample_weight n_featuresn_paramsrrindices n_indicesr+r?rAr,r-resultsolutionfailureparamsrrrfitrs                   " "   zQuantileRegressor.fitr)__name__ __module__ __qualname____doc__rrYrrrrrsS r)rEnumpyr4scipyrscipy.optimizerbaserr_baser exceptionsr utilsr utils.validationr utils.fixesr r rrrrrs