o \i@@s dZddlZddlmZddlmZmZddlZddl m Z ddl m Z ddl mZddlmZd d lmZmZd d lmZd d lmZd d lmZmZmZd dlmZmZd dlm Z ddl!m"Z"e#ej$j%Z&ddZ'dddZ(ddZ)ddZ*Gdddee"Z+dS)z< A Theil-Sen Estimator for Multiple Linear Regression Model N) combinations)IntegralReal)effective_n_jobs)linalg)get_lapack_funcs)binom)RegressorMixin _fit_context)ConvergenceWarning)check_random_state)HiddenInterval StrOptions)Paralleldelayed) validate_data) LinearModelcCs||}ttj|ddd}|tk}t||jdk}||}||ddtjf}ttj||dd}|tkrWtj||ddf|ddtjd|dd}nd}d}t dd|||t d|||S)u Modified Weiszfeld step. This function defines one iteration step in order to approximate the spatial median (L1 median). It is a form of an iteratively re-weighted least squares method. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. x_old : ndarray of shape = (n_features,) Current start vector. Returns ------- x_new : ndarray of shape (n_features,) New iteration step. References ---------- - On Computation of Spatial Median for Robust Data Mining, 2005 T. Kärkkäinen and S. Äyrämö http://users.jyu.fi/~samiayr/pdf/ayramo_eurogen05.pdf r raxisrNg?) npsqrtsum_EPSILONintshapenewaxisrnormmaxmin)Xx_olddiff diff_normmask is_x_old_in_X quotient_norm new_directionr+/var/www/www-root/data/www/176.119.141.140/sports-predictor/venv/lib/python3.10/site-packages/sklearn/linear_model/_theil_sen.py_modified_weiszfeld_steps"  r-,MbP?cCs|jddkrdtj|ddfS|dC}tj|dd}t|D]}t||}t||d|kr8||fS|}q!t dj |dt ||fS) u Spatial median (L1 median). The spatial median is member of a class of so-called M-estimators which are defined by an optimization problem. Given a number of p points in an n-dimensional space, the point x minimizing the sum of all distances to the p other points is called spatial median. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. max_iter : int, default=300 Maximum number of iterations. tol : float, default=1.e-3 Stop the algorithm if spatial_median has converged. Returns ------- spatial_median : ndarray of shape = (n_features,) Spatial median. n_iter : int Number of iterations needed. References ---------- - On Computation of Spatial Median for Robust Data Mining, 2005 T. Kärkkäinen and S. Äyrämö http://users.jyu.fi/~samiayr/pdf/ayramo_eurogen05.pdf rT)keepdimsr rrzYMaximum number of iterations {max_iter} reached in spatial median for TheilSen regressor.)max_iter) rrmedianravelmeanranger-rwarningswarnformatr )r#r1tolspatial_median_oldn_iterspatial_medianr+r+r,_spatial_medianPs""   r=cCs(ddd|||d|d|S)aApproximation of the breakdown point. Parameters ---------- n_samples : int Number of samples. n_subsamples : int Number of subsamples to consider. Returns ------- breakdown_point : float Approximation of breakdown point. rg?r+) n_samples n_subsamplesr+r+r,_breakdown_pointsr@c Cst|}|jd|}|jd}t|jd|f}t||f}tt||}td||f\} t|D])\} } || ddf|dd|df<|| |d|<| ||dd||| <q5|S)aLeast Squares Estimator for TheilSenRegressor class. This function calculates the least squares method on a subset of rows of X and y defined by the indices array. Optionally, an intercept column is added if intercept is set to true. Parameters ---------- X : array-like of shape (n_samples, n_features) Design matrix, where `n_samples` is the number of samples and `n_features` is the number of features. y : ndarray of shape (n_samples,) Target vector, where `n_samples` is the number of samples. indices : ndarray of shape (n_subpopulation, n_subsamples) Indices of all subsamples with respect to the chosen subpopulation. fit_intercept : bool Fit intercept or not. Returns ------- weights : ndarray of shape (n_subpopulation, n_features + intercept) Solution matrix of n_subpopulation solved least square problems. rr)gelssN) rrremptyoneszerosr!r enumerate) r#yindices fit_intercept n_featuresr?weightsX_subpopulationy_subpopulationlstsqindexsubsetr+r+r,_lstsqs  rPc @seZdZUdZdgdeedhgeeddddgdegeeddddgeed dddgd gdegd gd Z e e d <dddddddddd ddZ ddZ eddddZdS)TheilSenRegressoraTheil-Sen Estimator: robust multivariate regression model. The algorithm calculates least square solutions on subsets with size n_subsamples of the samples in X. Any value of n_subsamples between the number of features and samples leads to an estimator with a compromise between robustness and efficiency. Since the number of least square solutions is "n_samples choose n_subsamples", it can be extremely large and can therefore be limited with max_subpopulation. If this limit is reached, the subsets are chosen randomly. In a final step, the spatial median (or L1 median) is calculated of all least square solutions. Read more in the :ref:`User Guide `. Parameters ---------- fit_intercept : bool, default=True Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations. copy_X : bool, default=True If True, X will be copied; else, it may be overwritten. .. deprecated:: 1.6 `copy_X` was deprecated in 1.6 and will be removed in 1.8. It has no effect as a copy is always made. max_subpopulation : int, default=1e4 Instead of computing with a set of cardinality 'n choose k', where n is the number of samples and k is the number of subsamples (at least number of features), consider only a stochastic subpopulation of a given maximal size if 'n choose k' is larger than max_subpopulation. For other than small problem sizes this parameter will determine memory usage and runtime if n_subsamples is not changed. Note that the data type should be int but floats such as 1e4 can be accepted too. n_subsamples : int, default=None Number of samples to calculate the parameters. This is at least the number of features (plus 1 if fit_intercept=True) and the number of samples as a maximum. A lower number leads to a higher breakdown point and a low efficiency while a high number leads to a low breakdown point and a high efficiency. If None, take the minimum number of subsamples leading to maximal robustness. If n_subsamples is set to n_samples, Theil-Sen is identical to least squares. max_iter : int, default=300 Maximum number of iterations for the calculation of spatial median. tol : float, default=1e-3 Tolerance when calculating spatial median. random_state : int, RandomState instance or None, default=None A random number generator instance to define the state of the random permutations generator. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. n_jobs : int, default=None Number of CPUs to use during the cross validation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. verbose : bool, default=False Verbose mode when fitting the model. Attributes ---------- coef_ : ndarray of shape (n_features,) Coefficients of the regression model (median of distribution). intercept_ : float Estimated intercept of regression model. breakdown_ : float Approximated breakdown point. n_iter_ : int Number of iterations needed for the spatial median. n_subpopulation_ : int Number of combinations taken into account from 'n choose k', where n is the number of samples and k is the number of subsamples. 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 See Also -------- HuberRegressor : Linear regression model that is robust to outliers. RANSACRegressor : RANSAC (RANdom SAmple Consensus) algorithm. SGDRegressor : Fitted by minimizing a regularized empirical loss with SGD. References ---------- - Theil-Sen Estimators in a Multiple Linear Regression Model, 2009 Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang http://home.olemiss.edu/~xdang/papers/MTSE.pdf Examples -------- >>> from sklearn.linear_model import TheilSenRegressor >>> from sklearn.datasets import make_regression >>> X, y = make_regression( ... n_samples=200, n_features=2, noise=4.0, random_state=0) >>> reg = TheilSenRegressor(random_state=0).fit(X, y) >>> reg.score(X, y) 0.9884 >>> reg.predict(X[:1,]) array([-31.5871]) boolean deprecatedrNleft)closedrr random_stateverbose rHcopy_Xmax_subpopulationr?r1r9rVn_jobsrW_parameter_constraintsTg@r.r/Fc Cs:||_||_||_||_||_||_||_||_| |_dSNrX) selfrHrYrZr?r1r9rVr[rWr+r+r,__init__Us  zTheilSenRegressor.__init__cCs|j}|jr |d}n|}|durC||krtd||||kr6||kr5|jr*dnd}td|||n||krBtd||nt||}tdtt||}t t|j |}||fS)Nrz=Invalid parameter since n_subsamples > n_samples ({0} > {1}).z+1zAInvalid parameter since n_features{0} > n_subsamples ({1} > {2}).z\Invalid parameter since n_subsamples != n_samples ({0} != {1}) while n_samples < n_features.) r?rH ValueErrorr8r"r!rrintrrrZ)r^r>rIr?n_dimplus_1all_combinationsn_subpopulationr+r+r,_check_subparamsls8   z"TheilSenRegressor._check_subparams)prefer_skip_nested_validationc sjdkr tdttjtdd\j\}|\_ t _ j rVt dj t dtj }t d|t dj ttjkrkttt}nfd d tj D}tj}t||t|j d fd d t|D}t|}t|jjd\_}j r|d_!|dd_"Sd_!|_"S)aUFit linear model. Parameters ---------- X : ndarray of shape (n_samples, n_features) Training data. y : ndarray of shape (n_samples,) Target values. Returns ------- self : returns an instance of self. Fitted `TheilSenRegressor` estimator. rSz`copy_X` was deprecated in 1.6 and will be removed in 1.8 since it has no effect internally. Simply leave this parameter to its default value to avoid this warning.T) y_numericzBreakdown point: {0}zNumber of samples: {0}zTolerable outliers: {0}zNumber of subpopulations: {0}csg|] }jddqS)F)sizereplace)choice).0_)r>r?rVr+r, sz)TheilSenRegressor.fit..)r[rWc3s(|]}tt|jVqdSr])rrPrH)rmjob)r# index_listr^rFr+r, s  z(TheilSenRegressor.fit..)r1r9rrNr)#rYr6r7 FutureWarningr rVrrrgn_subpopulation_r@ breakdown_rWprintr8rrrbrrZlistrr5rr[ array_splitrvstackr=r1r9n_iter_rH intercept_coef_) r^r#rFrI tol_outliersrGr[rJcoefsr+)r#rqr>r?rVr^rFr,fitsN          zTheilSenRegressor.fit)__name__ __module__ __qualname____doc__rrrrrr\dict__annotations__r_rgr rr+r+r+r,rQs2 y %rQ)r.r/),rr6 itertoolsrnumbersrrnumpyrjoblibrscipyrscipy.linalg.lapackr scipy.specialrbaser r exceptionsr utilsr utils._param_validationrrrutils.parallelrrutils.validationr_baserfinfodoubleepsrr-r=r@rPrQr+r+r+r,s,          38,