Kiʟ SrSSKrSSKrSSKrSSKrSSKJrJr SSKr SSK J r SSK J r SSKJrJrJr SSKJr SSKJr SS KJr SS KJrJr SS KJr SS KJrJrJ r SS K!J"r"J#r#J$r$J%r%J&r& SSK'J(r(J)r) SSK*J+r+J,r,J-r-J.r. Sr/Sr0SSSSSS\ Rb"\ Rd5RfS.Sjr4Sr5\ "S/S/S/S.SS9SSSSSS\ Rb"\ Rd5RfSS.Sj5r6"SS \5r7"S!S"\75r8SSSSSSS\ Rb"\ Rd5Rf4S#jr9"S$S%\75r:g)&zUGraphicalLasso: sparse inverse covariance estimation with an l1-penalized estimator. N)IntegralReal)linalg) _fit_context)EmpiricalCovarianceempirical_covariancelog_likelihood)ConvergenceWarning)_cd_fast)lars_path_gram)check_cvcross_val_score)Bunch)Interval StrOptionsvalidate_params)MetadataRouter MethodMapping_raise_for_params_routing_enabledprocess_routing)Paralleldelayed)_is_arraylike_not_scalarcheck_random_state check_scalar validate_datacZURSnS[X5-U[R"S[R-5--nXB[R "U5R 5[R "[R"U55R 5- -- nU$)zEvaluation of the graphical-lasso objective function the objective function is made of a shifted scaled version of the normalized log-likelihood (i.e. its empirical mean over the samples) and a penalisation term to promote sparsity r)shaper nplogpiabssumdiag)mle precision_alphapcosts a/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/sklearn/covariance/_graph_lasso.py _objectiver.-s A .1 1Aq255y8I4I IDRVVJ'++-rwwz7J0K0O0O0QQ RRD Kc[R"X-5nX1RS-nX2[R"U5R5[R"[R"U55R5- -- nU$)zExpression of the dual gap convergence criterion The specific definition is given in Duchi "Projected Subgradient Methods for Learning Sparse Gaussians". r)r"r&r!r%r')emp_covr)r*gaps r- _dual_gapr3:sl &&% &C  A CBFF:&**,rvvbggj6I/J/N/N/PP QQC Jr/cd-C6?dF)cov_initmodetolenet_tolmax_iterverboseepscURupUS:Xaq[R"U5n S[X 5-n X[R "S[R -5-- n [R"X -5U - n X X4S4$UcUR5nOUR5nUS-nURSSU S-2nXRSSU S-2'[R"U5n [R"U 5nSn[5nUS:Xa [SSS 9nO [SS 9n[Rn [R"USS2SS24S S 9n[U5GH9n[U 5GHenUS:a*US- nUUUU:gUU'USS2U4UU:gUSS2U4'OUSS2SS24USS&UUUU:g4n[R "S0UD6 US:XaQU UU:gU4U UU4S U--- *n["R$"UUSUUU['SU5U[)S5SSSS9 un n O#[+UUUR,XS- - SUSSS9u n nSSS5 SUUU4[R."UUU:gU4W5- - U UU4'U UU4*U-U UU:gU4'U UU4*U-U UUU:g4'[R."UU5nUUUUU:g4'UUUU:gU4'GMh [R0"U R55(d [3S5e[5X U5n [7X U5n U(a[9SUX4-5 UR;X45 [R<"U 5U:a OT[R0"U 5(aGM(US:dGM1[3S5e [>R@"SXm4-[B5 XUUS-4$!,(df  GN=f![2anURDSS-4Ul"UeSnAff=f)Nrrr gffffff?r4raiseignore)overinvalid)rCC)orderiFT) wr*betaQqyr;r9rngrandompositive do_screeninglars)XyGram n_samples alpha_min copy_Gramr=method return_pathg?z1The system is too ill-conditioned for this solverz<[graphical_lasso] Iteration % 3i, cost % 3.2e, dual gap %.3ezANon SPD result: the system is too ill-conditioned for this solverzDgraphical_lasso: did not converge after %i iteration: dual gap: %.3ez3. The system is too ill-conditioned for this solver)#r!rinvr r"r#r$r&copyflatpinvharangelistdictinfrangeerrstatecd_fastenet_coordinate_descent_grammaxrr sizedotisfiniteFloatingPointErrorr3r.printappendr%warningswarnr args)r1r*r7r8r9r:r;r<r=_ n_featuresr)r,d_gap covariance_diagonalindicesicostserrorssub_covarianceidxdirowcoefses r-_graphical_lassor~GszMMMA zZZ( nW99 RVVAI...w+,z9TM144lln mmo 4K||-zA~-.H*2& Q&'k*Jii #G A FE t|7H5g&[QRV!4C@xAZ(7qB)4RC)HN2&,72,>w#~,NN1b5)(3ABF(;N1%c7c>12[[*6*t|'w#~s':;)#s(3dSj@B!*1)M)M#"'!",!! &)X%6 ( 24 8#(%*)-#*q!Q('5"!/&)hh&+A~&>&* ##)(- ' 1e7+L(+S)ff[C)<=uEF( 38$4>c3h3G2G%2O 7c>3./3=c3h3G2G%2O 33./~u538 CC/038 GsNC/0s)t;;z~~/00(Gg59Eg59DR$&' LL$ 'vve}s";;t$$Q(WU!\ MMV#$"  E1q5 00W+*N &&)SSUsE+B*OA;O DO.O O,O O O P&PPc[R"U5nSURSSURSS-2'[R"[R "U55$)aFind the maximum alpha for which there are some non-zeros off-diagonal. Parameters ---------- emp_cov : ndarray of shape (n_features, n_features) The sample covariance matrix. Notes ----- This results from the bound for the all the Lasso that are solved in GraphicalLasso: each time, the row of cov corresponds to Xy. As the bound for alpha is given by `max(abs(Xy))`, the result follows. rNr?)r"rZr[r!rer%)r1As r- alpha_maxrsI A !AFF aggaj1n  66"&&) r/ array-likeboolean)r1 return_costs return_n_iterprefer_skip_nested_validation)r8r9r:r;r<rr=rc [UUSUUUUUSS9 RU5n U RU R/n U(aU R U R 5 U (aU R U R 5 [U 5$)a L1-penalized covariance estimator. Read more in the :ref:`User Guide `. .. versionchanged:: v0.20 graph_lasso has been renamed to graphical_lasso Parameters ---------- emp_cov : array-like of shape (n_features, n_features) Empirical covariance from which to compute the covariance estimate. alpha : float The regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance. Range is (0, inf]. mode : {'cd', 'lars'}, default='cd' The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where p > n. Elsewhere prefer cd which is more numerically stable. tol : float, default=1e-4 The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped. Range is (0, inf]. enet_tol : float, default=1e-4 The tolerance for the elastic net solver used to calculate the descent direction. This parameter controls the accuracy of the search direction for a given column update, not of the overall parameter estimate. Only used for mode='cd'. Range is (0, inf]. max_iter : int, default=100 The maximum number of iterations. verbose : bool, default=False If verbose is True, the objective function and dual gap are printed at each iteration. return_costs : bool, default=False If return_costs is True, the objective function and dual gap at each iteration are returned. eps : float, default=eps The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Default is `np.finfo(np.float64).eps`. return_n_iter : bool, default=False Whether or not to return the number of iterations. Returns ------- covariance : ndarray of shape (n_features, n_features) The estimated covariance matrix. precision : ndarray of shape (n_features, n_features) The estimated (sparse) precision matrix. costs : list of (objective, dual_gap) pairs The list of values of the objective function and the dual gap at each iteration. Returned only if return_costs is True. n_iter : int Number of iterations. Returned only if `return_n_iter` is set to True. See Also -------- GraphicalLasso : Sparse inverse covariance estimation with an l1-penalized estimator. GraphicalLassoCV : Sparse inverse covariance with cross-validated choice of the l1 penalty. Notes ----- The algorithm employed to solve this problem is the GLasso algorithm, from the Friedman 2008 Biostatistics paper. It is the same algorithm as in the R `glasso` package. One possible difference with the `glasso` R package is that the diagonal coefficients are not penalized. Examples -------- >>> import numpy as np >>> from sklearn.datasets import make_sparse_spd_matrix >>> from sklearn.covariance import empirical_covariance, graphical_lasso >>> true_cov = make_sparse_spd_matrix(n_dim=3,random_state=42) >>> rng = np.random.RandomState(42) >>> X = rng.multivariate_normal(mean=np.zeros(3), cov=true_cov, size=3) >>> emp_cov = empirical_covariance(X, assume_centered=True) >>> emp_cov, _ = graphical_lasso(emp_cov, alpha=0.05) >>> emp_cov array([[ 1.687, 0.212, -0.209], [ 0.212, 0.221, -0.0817], [-0.209, -0.0817, 0.232]]) precomputedT) r*r8 covariancer9r:r;r<r=assume_centered)GraphicalLassofitrrr)rkcosts_n_iter_tuple) r1r*r8r9r:r;r<rr=rmodeloutputs r-graphical_lassorsl      c'l !1!1 2F ell# emm$ =r/c 2^\rSrSr%0\R E\"\SSSS9/\"\SSSS9/\"\SSSS9/\ "SS15/S /\"\SSS S9/S .Er\ \ S '\RS 5 SSSSS\ R"\ R5R S4U4SjjrSrU=r$)BaseGraphicalLassoiprNrightclosedleftr4rPr<both)r9r:r;r8r<r=_parameter_constraintsstore_precisionr5r6Fch>[TU]US9 XlX lX0lX@lXPlX`lg)Nr)super__init__r9r:r;r8r<r=) selfr9r:r;r8r<r=r __class__s r-rBaseGraphicalLasso.__init__|s3 9    r/)r:r=r;r8r9r<)__name__ __module__ __qualname____firstlineno__rrrrrrr___annotations__popr"finfofloat64r=r__static_attributes__ __classcell__rs@r-rrps$  4 4$q$w78dAtG<=h4?@T6N+,;q$v67$D01   HHRZZ $ $r/rc ^\rSrSr%Sr0\R E\"\SSSS9/\ "S15S/S.Er\ \ S 'SS SS S S S \ R"\ R5RS S.U4Sjjjr\"SS9SSj5rSrU=r$)riaSparse inverse covariance estimation with an l1-penalized estimator. For a usage example see :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py`. Read more in the :ref:`User Guide `. .. versionchanged:: v0.20 GraphLasso has been renamed to GraphicalLasso Parameters ---------- alpha : float, default=0.01 The regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance. Range is (0, inf]. mode : {'cd', 'lars'}, default='cd' The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where p > n. Elsewhere prefer cd which is more numerically stable. covariance : "precomputed", default=None If covariance is "precomputed", the input data in `fit` is assumed to be the covariance matrix. If `None`, the empirical covariance is estimated from the data `X`. .. versionadded:: 1.3 tol : float, default=1e-4 The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped. Range is (0, inf]. enet_tol : float, default=1e-4 The tolerance for the elastic net solver used to calculate the descent direction. This parameter controls the accuracy of the search direction for a given column update, not of the overall parameter estimate. Only used for mode='cd'. Range is (0, inf]. max_iter : int, default=100 The maximum number of iterations. verbose : bool, default=False If verbose is True, the objective function and dual gap are plotted at each iteration. eps : float, default=eps The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Default is `np.finfo(np.float64).eps`. .. versionadded:: 1.3 assume_centered : bool, default=False If True, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data are centered before computation. Attributes ---------- location_ : ndarray of shape (n_features,) Estimated location, i.e. the estimated mean. covariance_ : ndarray of shape (n_features, n_features) Estimated covariance matrix precision_ : ndarray of shape (n_features, n_features) Estimated pseudo inverse matrix. n_iter_ : int Number of iterations run. costs_ : list of (objective, dual_gap) pairs The list of values of the objective function and the dual gap at each iteration. Returned only if return_costs is True. .. versionadded:: 1.3 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 -------- graphical_lasso : L1-penalized covariance estimator. GraphicalLassoCV : Sparse inverse covariance with cross-validated choice of the l1 penalty. Examples -------- >>> import numpy as np >>> from sklearn.covariance import GraphicalLasso >>> true_cov = np.array([[0.8, 0.0, 0.2, 0.0], ... [0.0, 0.4, 0.0, 0.0], ... [0.2, 0.0, 0.3, 0.1], ... [0.0, 0.0, 0.1, 0.7]]) >>> np.random.seed(0) >>> X = np.random.multivariate_normal(mean=[0, 0, 0, 0], ... cov=true_cov, ... size=200) >>> cov = GraphicalLasso().fit(X) >>> np.around(cov.covariance_, decimals=3) array([[0.816, 0.049, 0.218, 0.019], [0.049, 0.364, 0.017, 0.034], [0.218, 0.017, 0.322, 0.093], [0.019, 0.034, 0.093, 0.69 ]]) >>> np.around(cov.location_, decimals=3) array([0.073, 0.04 , 0.038, 0.143]) rNrrr)r*rrr4r5r6F)r8rr9r:r;r<r=rc D>[T U]UUUUUUU S9 XlX0lgN)r9r:r;r8r<r=r)rrr*r) rr*r8rr9r:r;r<r=rrs r-rGraphicalLasso.__init__ s8 +   $r/Trc T[XSSS9nURS:Xa9UR5n[R"UR S5UlOc[XRS9nUR(a)[R"UR S5UlOURS5Ul[UURSURURURURUR UR"S9 uUlUlUlUlU$) a(Fit the GraphicalLasso model to X. Parameters ---------- X : array-like of shape (n_samples, n_features) Data from which to compute the covariance estimate. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object Returns the instance itself. r )ensure_min_featuresensure_min_samplesrr?rrNr*r7r8r9r:r;r<r=)rrrZr"zerosr! location_rrmeanr~r*r8r9r:r;r<r=rrr)rr)rXrKr1s r-rGraphicalLasso.fit%s$ $qQ O ??m +ffhGXXaggaj1DN*1>R>RSG##!#!''!*!5!"GW **]]]]LL H D$/4;  r/)r*rrrrrrr)){Gz?N)rrrr__doc__rrrrrr_rr"rrr=rrrrrrs@r-rrstl$  3 3$4D89!=/2D9$D%  HHRZZ $ $%%25(6(r/rc R[SUS- 5n [U5n UcU R5n OUn [5n [5n[5nUb [U5nUHn[ U UU UUUUU U S9 un n nU R U 5 UR U5 Ub [ WU5nUb=[R"W5(d[R*nUR U5 US:Xa![RRS5 MUS:dMUb[SUW4-5 M[SU-5 M UbXU4$X4$![aR [R*nU R [R5 UR [R5 Nf=f)al1-penalized covariance estimator along a path of decreasing alphas Read more in the :ref:`User Guide `. Parameters ---------- X : ndarray of shape (n_samples, n_features) Data from which to compute the covariance estimate. alphas : array-like of shape (n_alphas,) The list of regularization parameters, decreasing order. cov_init : array of shape (n_features, n_features), default=None The initial guess for the covariance. X_test : array of shape (n_test_samples, n_features), default=None Optional test matrix to measure generalisation error. mode : {'cd', 'lars'}, default='cd' The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where p > n. Elsewhere prefer cd which is more numerically stable. tol : float, default=1e-4 The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped. The tolerance must be a positive number. enet_tol : float, default=1e-4 The tolerance for the elastic net solver used to calculate the descent direction. This parameter controls the accuracy of the search direction for a given column update, not of the overall parameter estimate. Only used for mode='cd'. The tolerance must be a positive number. max_iter : int, default=100 The maximum number of iterations. This parameter should be a strictly positive integer. verbose : int or bool, default=False The higher the verbosity flag, the more information is printed during the fitting. eps : float, default=eps The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Default is `np.finfo(np.float64).eps`. .. versionadded:: 1.3 Returns ------- covariances_ : list of shape (n_alphas,) of ndarray of shape (n_features, n_features) The estimated covariance matrices. precisions_ : list of shape (n_alphas,) of ndarray of shape (n_features, n_features) The estimated (sparse) precision matrices. scores_ : list of shape (n_alphas,), dtype=float The generalisation error (log-likelihood) on the test data. Returned only if test data is passed. rr?r.z/[graphical_lasso_path] alpha: %.2e, score: %.2ez"[graphical_lasso_path] alpha: %.2e)rerrZr^r~rkr rir"r`nanrhsysstderrwriterj)ralphasr7X_testr8r9r:r;r<r= inner_verboser1rr covariances_ precisions_scores_ test_emp_covr*r)ro this_scores r-graphical_lasso_pathrRsV7Q;'M"1%Glln  6L&KfG +F3  ',<$!!% - )KQ    ,   z *!+L*E  ;;z** ffW NN: & a< JJ  S ! q[!Ej)* :UBCGH'11  $$)" '&&J    '   rvv & 's#AE  AF&%F&c ^\rSrSr%Sr0\R E\"\SSSS9S/\"\SSSS9/S /\S/S .Er\ \ S 'S S SS S SSSS\ R"\ R5RSS. U4Sjjr\"SS9SSj5rSrSrU=r$)GraphicalLassoCViaSparse inverse covariance w/ cross-validated choice of the l1 penalty. See glossary entry for :term:`cross-validation estimator`. Read more in the :ref:`User Guide `. .. versionchanged:: v0.20 GraphLassoCV has been renamed to GraphicalLassoCV Parameters ---------- alphas : int or array-like of shape (n_alphas,), dtype=float, default=4 If an integer is given, it fixes the number of points on the grids of alpha to be used. If a list is given, it gives the grid to be used. See the notes in the class docstring for more details. Range is [1, inf) for an integer. Range is (0, inf] for an array-like of floats. n_refinements : int, default=4 The number of times the grid is refined. Not used if explicit values of alphas are passed. Range is [1, inf). cv : int, cross-validation generator or iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross-validation, - integer, to specify the number of folds. - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.20 ``cv`` default value if None changed from 3-fold to 5-fold. tol : float, default=1e-4 The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped. Range is (0, inf]. enet_tol : float, default=1e-4 The tolerance for the elastic net solver used to calculate the descent direction. This parameter controls the accuracy of the search direction for a given column update, not of the overall parameter estimate. Only used for mode='cd'. Range is (0, inf]. max_iter : int, default=100 Maximum number of iterations. mode : {'cd', 'lars'}, default='cd' The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where number of features is greater than number of samples. Elsewhere prefer cd which is more numerically stable. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None verbose : bool, default=False If verbose is True, the objective function and duality gap are printed at each iteration. eps : float, default=eps The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Default is `np.finfo(np.float64).eps`. .. versionadded:: 1.3 assume_centered : bool, default=False If True, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data are centered before computation. Attributes ---------- location_ : ndarray of shape (n_features,) Estimated location, i.e. the estimated mean. covariance_ : ndarray of shape (n_features, n_features) Estimated covariance matrix. precision_ : ndarray of shape (n_features, n_features) Estimated precision matrix (inverse covariance). costs_ : list of (objective, dual_gap) pairs The list of values of the objective function and the dual gap at each iteration. Returned only if return_costs is True. .. versionadded:: 1.3 alpha_ : float Penalization parameter selected. cv_results_ : dict of ndarrays A dict with keys: alphas : ndarray of shape (n_alphas,) All penalization parameters explored. split(k)_test_score : ndarray of shape (n_alphas,) Log-likelihood score on left-out data across (k)th fold. .. versionadded:: 1.0 mean_test_score : ndarray of shape (n_alphas,) Mean of scores over the folds. .. versionadded:: 1.0 std_test_score : ndarray of shape (n_alphas,) Standard deviation of scores over the folds. .. versionadded:: 1.0 n_iter_ : int Number of iterations run for the optimal alpha. 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 -------- graphical_lasso : L1-penalized covariance estimator. GraphicalLasso : Sparse inverse covariance estimation with an l1-penalized estimator. Notes ----- The search for the optimal penalization parameter (`alpha`) is done on an iteratively refined grid: first the cross-validated scores on a grid are computed, then a new refined grid is centered around the maximum, and so on. One of the challenges which is faced here is that the solvers can fail to converge to a well-conditioned estimate. The corresponding values of `alpha` then come out as missing values, but the optimum may be close to these missing values. In `fit`, once the best parameter `alpha` is found through cross-validation, the model is fit again using the entire training set. Examples -------- >>> import numpy as np >>> from sklearn.covariance import GraphicalLassoCV >>> true_cov = np.array([[0.8, 0.0, 0.2, 0.0], ... [0.0, 0.4, 0.0, 0.0], ... [0.2, 0.0, 0.3, 0.1], ... [0.0, 0.0, 0.1, 0.7]]) >>> np.random.seed(0) >>> X = np.random.multivariate_normal(mean=[0, 0, 0, 0], ... cov=true_cov, ... size=200) >>> cov = GraphicalLassoCV().fit(X) >>> np.around(cov.covariance_, decimals=3) array([[0.816, 0.051, 0.22 , 0.017], [0.051, 0.364, 0.018, 0.036], [0.22 , 0.018, 0.322, 0.094], [0.017, 0.036, 0.094, 0.69 ]]) >>> np.around(cov.location_, decimals=3) array([0.073, 0.04 , 0.038, 0.143]) For an example comparing :class:`sklearn.covariance.GraphicalLassoCV`, :func:`sklearn.covariance.ledoit_wolf` shrinkage and the empirical covariance on high-dimensional gaussian data, see :ref:`sphx_glr_auto_examples_covariance_plot_sparse_cov.py`. rNrrrr? cv_object)r n_refinementscvn_jobsrr5r6r4F) rrrr9r:r;r8rr<r=rc \>[T U]UUUUU U U S9 XlX lX0lXlgr)rrrrrr) rrrrr9r:r;r8rr<r=rrs r-rGraphicalLassoCV.__init__sC +   * r/Trc 8 ^^^^[UTS5 [TTSS9mTR(a)[R"TR S5TlOTRS5Tl[TTRS9n[TRUSS9n[5nTRn[STRS- 5m[U5(aBTRH#n[!US ["S[R$S S 9 M% TRmSn ObTR&n [)U5n S U -n [R*"[R,"U 5[R,"U 5U5S S S2m[/5(a[1TS40UD6n O[3[30S9S9n [4R4"5n [7U 5GHn[8R:"5 [8R<"S[>5 [ATRBTRS9"UUUU4SjURD"TU40U RFRDD655nS S S 5 [IW6unnn[IU6n[IU6nURK[ITUU55 [MU[NRP"S5SS9n[R$*nSn[SU5Hununnn[R"U5nUS[RT"[RV5RX- :a[RZn[R\"U5(aUnUU:dMUnUnM WS:XaUSSn USSn OhUU:Xa&U[_U5S- :XdUUSn UUS-Sn OTl?Tl@T$!,(df  GN=f) aFit the GraphicalLasso covariance model to X. Parameters ---------- X : array-like of shape (n_samples, n_features) Data from which to compute the covariance estimate. y : Ignored Not used, present for API consistency by convention. **params : dict, default=None Parameters to be passed to the CV splitter and the cross_val_score function. .. versionadded:: 1.5 Only available if `enable_metadata_routing=True`, which can be set by using ``sklearn.set_config(enable_metadata_routing=True)``. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- self : object Returns the instance itself. rr )rr?rrF) classifierr*r)min_valmax_valinclude_boundariesrN)split)splitterrA)rr<c 3># UHfup[[5"TUTTUTRTRTR[ STR -5TTRS9 v Mh g7f)皙?)rrr8r9r:r;r<r=N)rrr8r9r:intr;r=).0traintestrrrrs r- 'GraphicalLassoCV.fit..sm O(V 01%% w!YY HH!%!$S4==%8!9 - HH (VsA.A1T)keyreverserz8[GraphicalLassoCV] Done refinement % 2i out of %i: % 3is)rrr<paramsrr _test_score)axismean_test_scorestd_test_score)r*r8r9r:r;r<r=)Arrrr"rr!rrrr rr^rrer<rrrr`rrlogspacelog10rrrtimerarlcatch_warnings simplefilterr rrrrzipextendsortedoperator itemgetter enumeraterrr=rrhlenrjrkrrarray cv_results_stdalpha_r~r8r9r:r;rrr)rr)rrrKrr1rpathn_alphasr*ralpha_1alpha_0 routed_paramst0ru this_pathcovsroscores best_scorelast_finite_idxindexr best_index grid_scores best_alpharrs`` @@r-rGraphicalLassoCV.fitsZ: &$. $q 9   XXaggaj1DNVVAYDN&q$:N:NO dggqU 3v;;At||a/0 #H - -FF'. %[[FM ..M(GWnG[['!2BHHW4ExPQUSUQUVF   +D%B6BM!5r?;M YY[}%A((*%%h0BC %DKKN O(*xx1'U 8N8N8T8T'U O  +4"9oOD!V:D&\F KKFFD1 2$H$7$7$:DID &&JO-6t_))vqWWV_ rxx ';'?'?!??!#J;;z**&+O+!+J!&J.=Qq'!*q'!*.zSYQR]7Rz*1-zA~.q1s4y1},z*1-j!1!!44zA~.q1zA~.q1+H55RXXg%68I8VW<X"|||  1N1umTYY[2-=>?Q&ZCJ47m d1g a #%{{%   hh{+ $bhhv&67{((+,A7B1a47HD  uQC{3 4-/1ggk.J*+-/VVKa-H)*J'   HX ]]]]! H D$/4;  g+*s A3X  X c[US9R[UR5[ 5RSSS9S9nU$)a"Get metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.5 Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. )ownerrr)calleecaller)rmethod_mapping)raddr rr)rrouters r-get_metadata_routing%GraphicalLassoCV.get_metadata_routingnsF d+//dgg&(?..ge.L0  r/) rrrrrrrrrrrr)r)rrrrrrrrrr_rr"rrr=rrrrrrrs@r-rrsyv$  3 3$Haf=|L"8QVDEmT" $D    HHRZZ $ $:5x6xtr/r);rrrrrlnumbersrrnumpyr"scipyr sklearn.basersklearn.covariancerrr sklearn.exceptionsr sklearn.linear_modelr rcr sklearn.model_selectionr r sklearn.utilsrsklearn.utils._param_validationrrrsklearn.utils.metadata_routingrrrrrsklearn.utils.parallelrrsklearn.utils.validationrrrrr.r3rrr=r~rrrrrrrXr/r-r$s\ "%XX15/=QQ5  "       I1X& >" # #(        D,>'L        }%@n)nr/