8^jZ>ddl?m@Z@mAZAddlBmCZCmDZDmEZEmFZFmGZGmHZHgdZIdZJdZKe jZMeMjeMjcZPZQejePjdZTejjdZWeWjeTeTddZTePeTeQeTcZPZQe jZZeZjeZjcZ[Z\dZ]dZ^ejddgdZ`ejjdeIejjddd gd!ZcejjdeIejjddd gd"ZdejjdeIejjddd gd#ZeejjdeIejjddd gd$ZfejjdeIejjddd gd%ZgejjdeIejjddd gd&ZhejjdeIejjddd gejjd'dgeFzejjd(d)d*gd+Zid,Zjd-Zkd.Zld/Zmd0Znd1Zoejjd2gd3ejjd4eFd5Zpejjd6gd7ejjd8dd gejjd4eFd9Zqejjd6gd7ejjd8dd gejjd4eFd:Zr dd<Zsejjd=d>egd?dgeFzDejjd@gdAdBZtdCZuejjdDdEdFgejjdGejejgejjdHejgeFzejjdIdJdKgdLdgMejjddd gejjdNgdOdPZydQZzejjd(dRdSgdTdUgMejjdgdVejjddd gejjdIdWdXgdLdgMejjdHejgeFzdYZ{ejjd(dRdSgdTdUgMejjdDdZgejjddd gejjdIgd[gd\MejjdGejejgejjdHejgeFzd]Z|ejjd(dRdSgdTdUgMejjdDdEdFgejjddd gejjdIgd[gd\MejjdGejejgejjdHejgeFzd^Z}d_Z~ejjdDdEdFgejjdHejgeFzejjd`dadbgejjdcgdddeZejjd'dgeFzejjdIgdfgd\MejjdDgdgdhZdiZdjZejjdked lefed lefgdmZejjdkeefeefgejjdnddogdpZdqZdrZdsZdtZejjduddve]gejjdnde*dwgejjd'dgeFzdxZejjdnde*dwgejjd'dgeFzdyZdzZ dd{Zejjd|e5ejjd}eAege8Mejjd~edEedEedEedFege8MdZejjd~edEegejjd|e5dZejjd|e5dZejjde6d dZejjdee2dZejjdeeeeeefejjd4eFdZdZejjdeefdZdZejjdudde^gdZejjduddve]gdZejjdeegdZejjdnddogejjdeegdZdZdZejjdddogdodggejjd'eDeEzeFzeGzeHzdZdZejjdeegejjdddiedfddiedfddiedfgdZejjdeegdZdZejjIddZejjdgdejjddd gejjd4eFdZejjdgdejjd4eFdZejjddd gejjd4eFdZejjdd dgejjddejRdgejjdejTgeFzejjdgddZejjdgddZdZejjdgdejjded;dZdZejjdeifedndifedndoifgdZejjdddgejjddd gejjd(gddZejjddd gejjd(gddZejjdgdd„Zejjd(gddÄZejjd(gddĄZdńZejjdd dgejjd'dgeFzejjddLdgejjdeIdgzdDŽZejjdIddgdLdgMejjdd dgddgMejjdd dgddgMejjdDdEdFgejjdd;dgdτZejjdDdEdFgejjdIddgdLdgMdЄZejjdIddgdLdgMdфZejjdeege dӫdԄZejjdeefeefge dӫdքZy)N)product)linalg)config_contextdatasets)clone)make_classificationmake_low_rank_matrixmake_multilabel_classificationmake_regression)ConvergenceWarning)LinearRegressionRidgeRidgeClassifierRidgeClassifierCVRidgeCVridge_regression)_check_gcv_mode _RidgeGCV_solve_cholesky_solve_cholesky_kernel _solve_lbfgs _solve_svd_X_CenterStackOp) get_scorer make_scorermean_squared_error) GridSearchCV GroupKFoldKFold LeaveOneOutcross_val_predict) minmax_scale)check_random_state)_NUMPY_NAMESPACE_NAMES_atol_for_typemove_to)yield_namespace_device_dtype_combinationsyield_namespaces)_get_check_estimator_ids)assert_allcloseassert_almost_equalassert_array_almost_equalassert_array_equalignore_warnings)_array_api_for_tests check_array_api_input_and_values) _IS_32BITCOO_CONTAINERSCSC_CONTAINERSCSR_CONTAINERSDOK_CONTAINERSLIL_CONTAINERS)svd sparse_cgcholeskylsqrsagsaga)r8r;)r8r9r:r;r<c 2tj||k(SN)npmeany_testy_predkwargss p/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/sklearn/linear_model/tests/test_ridge.py_accuracy_callablerGXs 776V# $$c.||z dzjS)N)rA)rCrDs rF_mean_squared_error_callablerK\s f_ " ( ( **rHlongwide)paramsc|jdk(rd\}}nd\}}t||}tjj |}t ||||}d|dddf<t j|\}}} tj|dkDsJ|ddd|f|dd|df} } | d|ddf| |dddf} } |jdk(r8|jd d | }||z}|| |j||z d zzz }nI|jd d | }| jtjd|z z| jz|z}d}|tj|z}d|d<t j|j|z|z|j|z}|||zz }|||zz }tj j|tj j|ksJ||||fS)aDDataset with OLS and Ridge solutions, well conditioned X. The construction is based on the SVD decomposition of X = U S V'. Parameters ---------- type : {"long", "wide"} If "long", then n_samples > n_features. If "wide", then n_features > n_samples. For "wide", we return the minimum norm solution w = X' (XX')^-1 y: min ||w||_2 subject to X w = y Returns ------- X : ndarray Last column of 1, i.e. intercept. y : ndarray coef_ols : ndarray of shape Minimum norm OLS solutions, i.e. min ||X w - y||_2_2 (with minimum ||w||_2 in case of ambiguity) Last coefficient is intercept. coef_ridge : ndarray of shape (5,) Ridge solution with alpha=1, i.e. min ||X w - y||_2_2 + ||w||_2^2. Last coefficient is intercept. rL) )rQrP) n_samples n_featureseffective_rank random_stateNMbP? lowhighsizer^rJr)rWrW)paramminr@random RandomStater rr7alluniformnormalTdiagidentitysolvenorm)global_random_seedrequestrRrSkrngXUsVtU1U2Vt1_coef_olsyalphad coef_ridgeR_OLSR_Ridges rFols_ridge_datasetr`s>}} % : % : Iz"A ))   2 3C 1SV AAaeHzz!}HAq" 66!d(   q"1"uXqABxB AY12q5 C}};;3Rj;A L R#**)j"8*9Q> >> KKCbyK 9552771q5>)BDD014 E  J''AAfIaccAgk13373J H E!j. G 99>>% 299>>'#: :: : a: %%rHsolver fit_interceptTFc|\}}}}d}t|d||dvrdnd|} |tj|z } |||zz } dtj| dztj| dzz z } t di| } |d d d d f}|r|d }n*||jd z }||jz }d }| j |||d d }| j tj|k(sJt| j|| j||tj| k(sJt di| j ||tj|jd  } | j tj|k(sJt| j|| j||tj| k(sJ| j|k(sJy )zTest that Ridge converges for all solvers to correct solution. We work with a simple constructed data set with known solution. ?Tr;r<V瞯<绽|=rzrrtolrUrVrJNrWraxis sample_weight)dictr@rAsumrfit intercept_pytestapproxr*coef_scoreonesshapesolver_)rrrrlrpryrwcoefrzrNres_null res_RidgeR2_Ridgemodel intercepts rFtest_ridge_regressionrs&MAq!T E .EE' F2771:~HAH I266)Q,'"&&1*===H OFOE !SbS& AH A  L  IIaO 9D   v}}Y7 77 7EKK& ;;q!  h 7 77 7 OFO  1BGGAGGAJ4G  HE   v}}Y7 77 7EKK& ;;q!  h 7 77 7 ==F "" "rHcF|\}}}}|j\}} d} t| dz |||dvrdnd|} |ddddf}d tj||fd z}tjj |t || d z ksJ|r|d} n*||jd z }||jz }d } | j|||dd}| jtj| k(sJt| jtj||fd y)a Test that Ridge converges for all solvers to correct solution on hstacked data. We work with a simple constructed data set with known solution. Fit on [X] with alpha is the same as fit on [X, X]/2 with alpha/2. For long X, [X, X] is a singular matrix. rrJrrrrNrW?rVrr:0yE>atol)rrr@ concatenater matrix_rankrarArrrrr*rr_ rrrrlrpryrwrrRrSrzrrs rF test_ridge_regression_hstacked_Xrs&&MAq!TGGIz E ai#.EE'  E !SbS& A bnnaV!,,A 99  #s9j1n'E EE EH A  L  IIaO 9D   v}}Y7 77 7EKKtTz!2>rHc>|\}}}}|j\}} d} td| z|||dvrdnd|} |ddddf}tj||fd }tjj |t || ksJtj||f}|r|d} n*||jd z }||jz }d } | j|||dd}| jtj| k(sJt| j|d y) aJTest that Ridge converges for all solvers to correct solution on vstacked data. We work with a simple constructed data set with known solution. Fit on [X] with alpha is the same as fit on [X], [y] [X], [y] with 2 * alpha. For wide X, [X', X'] is a singular matrix. rrJrrrrNrWrrrr)rrr@rrrrarrArrrrr*rrs rF test_ridge_regression_vstacked_Xrs!&MAq!TGGIz E %i#.EE'  E !SbS& A 1vA&A 99  #s9j'A AA A ad AH A  L  IIaO 9D   v}}Y7 77 7EKKD1rHcB|\}}}}|j\}} d} t| |||dvrdnd|} td i| } |r|ddddf}|d} |dd}nd} | j|||| kDs|s;| jt j | k(sJt| j|yt| j||t||z| z|tjjtj| j| jftjjtj| |fkDsJt jd | jt j | k(sJt| j|y) aTest that unpenalized Ridge = OLS converges for all solvers to correct solution. We work with a simple constructed data set with known solution. Note: This checks the minimum norm solution for wide X, i.e. n_samples < n_features: min ||w||_2 subject to X w = y rrrrrNrW1Ridge does not provide the minimum norm solution.reasonr)rrrrrrrr*rpredictr@rrkrxfail)rrrrlrpryrrwrRrSrzrNrrs rF!test_ridge_regression_unpenalizedr's&MAq$GGIz E #.EE' F OFOE a"fIH CRy  IIaO :]6==#;;;; T*  a(!,D9,a0yy~~beeE$4$4ekk$ABCbiinn EE)T/ "G      OP6==#;;;; T*rHc|\}}}}|j\}} d} t| |||dvrdnd|} |r|ddddf}|d} |dd}nd} dtj||fd z}tjj |t || ksJ| j|||| kDs|sg| jtj| k(sJ|d k(rtjt| jtj||fyt| j||tjj!tj| j| jftjj!tj| ||fkDsJtj"d | jtj| k(sJt| jtj||fy)a^Test that unpenalized Ridge = OLS converges for all solvers to correct solution. We work with a simple constructed data set with known solution. OLS fit on [X] is the same as fit on [X, X]/2. For long X, [X, X] is a singular matrix and we check against the minimum norm solution: min ||w||_2 subject to min ||X w - y||_2 rrrrrNrWrrVrr9rr)rrr@rrrrarrrrskipr*rrrrkr rrrrlrpryrrwrRrSrzrrs rF,test_ridge_regression_unpenalized_hstacked_Xr^s&MAq$GGIz E #.EE'  E a"fIH CRy  bnnaV!,,A 99  #s9j'A AA A IIaO:]6==#;;;; Z  KKM RUU4:%67  a(!,yy~~beeE$4$4ekk$ABCbiinn EE)T4' (G      OP6==#;;;; RUU4:%67rHc|\}}}}|j\}} d} t| |||dvrdnd|} |r|ddddf}|d} |dd}nd} tj||fd}tjj |t || ksJtj||f}| j|||| kDs|s;| jtj| k(sJt| j|yt| j||tjjtj| j| jftjjtj| |fkDsJtj d | jtj| k(sJt| j|y) aTest that unpenalized Ridge = OLS converges for all solvers to correct solution. We work with a simple constructed data set with known solution. OLS fit on [X] is the same as fit on [X], [y] [X], [y]. For wide X, [X', X'] is a singular matrix and we check against the minimum norm solution: min ||w||_2 subject to X w = y rrrrrNrWrrr)rrr@rrrrarrrrrr*rrrkrrs rF,test_ridge_regression_unpenalized_vstacked_Xrs&MAq$GGIz E #.EE'  E a"fIH CRy  1vA&A 99  #s9j'A AA A ad A IIaO:]6==#;;;; T*  a(!,yy~~beeE$4$4ekk$ABCbiinn EE)T/ "G      OP6==#;;;; T*rHsparse_containerrzr{Gz?c|=|r|tvrtjn|s|tvrtj|\}}}} |j\} } t j dd| } t||||dvrdndd| } |dddd f}tj||fd }tj||f}tj| d| z f|z} |r| d }n*||jd z }||jz }d}|||}| j||| | dd } | jtj|k(sJt| j | y) zTest that Ridge with sample weights gives correct results. We use the following trick: ||y - Xw||_2 = (z - Aw)' W (z - Aw) for z=[y, y], A' = [X', X'] (vstacked), and W[:n/2] + W[n/2:] = 1, W=diag(W) NrrVr[rrr順)rzrrrmax_iterrUrWrr)SPARSE_SOLVERS_WITH_INTERCEPTrr SPARSE_SOLVERS_WITHOUT_INTERCEPTrrorerr@rrrArrrr*r)rrrrzrrlrpryrwrrRrSswrrs rF$test_ridge_regression_sample_weightsrse$# V+HH KKM61Q#Q KKM%MAq!TGGIz  3B #.EE'  E !SbS& A 1vA&A ad A r1r6z U "BH A  L # Q  IIa"I% 9D   v}}Y7 77 7EKK&rHc8tjdd}tt|dg}t j ttj }t||dg}t j tj |j }t||y)NrWrVrrz) y_diabetesreshaper X_diabetesr@dotrgrr,)ryrK dual_coefcoef2s rFtest_primal_dual_relationshiprsl2q!A :q 7D z:<<(A&q!D6:I FF:<< + - -EdE*rHc tjjd}|jd}|jdd}d}t j t |5t||ddddd dddy#1swYyxYw) NrrZz3sparse_cg did not converge after [0-9]+ iterations.matchrr8rV)rzrrrverbose)r@rbrcrandnrwarnsr r)roryrpwarning_messages rF&test_ridge_regression_convergence_failr st ))   "C ! 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' 'D   rHct|||\}}td\}}|j|}|j|} |j|| } | j |} | j j } td5|j|||j| |}} |j| |}|j | }|j| jcxk(r|jk(sJJtt|td| tt|j td| dddy#1swYyxYw)NrrUTrrrr)r/r r?rrclasses_r'rr%rr*r&r@r-)rrrrrrrpryX_npy_npridge_nppred_np classes_npX_xpy_xpridge_xppred_xps rF*test_ridge_classifier_multilabel_array_apirs)&o{JOJB )q 9DAq 88J D 88J D}}T4(Ht$G""'')J 4 0XZZVZ4bjjfj6Ud==t,""4(}} 888888Bu=wG78#4#4EJJW XXXs B:EEc t|||\}}tddd\}}|j|}|j|}tjgd|} t | dj ||} | j|} td 5|j || |j || } } | |j | | fD]}t |dj | | }|j| }|j| jcxk(r|jk(sJJtt|td | t| dddy#1swYyxYw)ztCheck that passing an array for alpha works with array API dispatch. Non-regression test for issue #34003. rrZr)rrSrU)rr5r)rAr7rTrrrrrN) r/r r?r@r%rrrrrr*r&r%)rrrrrrpryr rr<rrrrrzrrs rF%test_ridge_per_target_alpha_array_apirsN&o{JOJB Q2A FDAq 88J D 88J D ZZ( ;F6%044T4@Ht$G 4 0 ZZVZ4bjjfj6Udbjjj?@ E57;;D$GH&&t,G==GMMqAII#vuo5 0Vf.?@.r{{m<""( -  ]]:\ : 048 0 )Y/ 0 0 0 0 $ /E ++-;;-8. . z 6, t 4 , IIi + ,, GE 0 >D D k 6, t 4 , IIi + ,,,- 0 0 0 0 , ,,, , ,,,sl) F56F) F5 G G3G8 G&GG&)F2 .F55F? G GGG# G&&G/ct|d\}}|jtdd}|jtdd}t }d}t j 5t jd|ttd5|j||ddddddtd5t dd j||dddy#1swYDxYw#1swYHxYw#1swYyxYw) NrrzkResults might be different than when Array API dispatch is disabled, or when a numpy-like namespace is usederror)messagecategoryTrrr) r/r%rrrwarningscatch_warningsfilterwarningsr rr)rrrwrrrrs rF)test_array_api_numpy_namespace_no_warningr*2s d CEB 6"1:&I 6"1:&I GE ;  ", T t 4 , IIi + ,, 4 0F Vd+// 9EFF  , ,,,FFs0!)C, C C,9C8 C) %C,,C58D test_funccL|d}||}||t||dyyy)Nrr)r,)r+r ret_dense ret_sparses rFtest_dense_sparser/Js6$I=)J!7!)ZC"8rHctjddgddgddgddgddgg}gd}td}|j||t |j ddggtjd gtd d i}|j||t |j ddggtjd gtd }|j||t |j ddggtjd gtjddgddgddgddgg}gd }td}|j||td }|j||t |jdk(sJt|j|jt|j|jy)Nr皙rrrVrVrVrWrW class_weightr8rVrXrWbalanced)rVrVrWrWrJ) r@rrrr-rrr r,rr)rpryrregas rFtest_class_weightsr8`s 4,q D$<#sc3ZPQAA t ,CGGAqMs{{S$K=1288QC=A 5z 2CGGAqMs{{S$K=1288RD>B z 2CGGAqMs{{S$K=1288QC=A 4,q D$<#sDEAA t ,CGGAqM  3DHHQN t}}  "" "cii4cnndoo>rHrc|}|jtjtj|d}|jtjtjt |j |j t jtjj}|tjdk(xxdzcc<dddd}|}|jtjtj|||}|jtjtjt |j |j |}|jtjtj|dz||}|jtjtj|t |j |j y ) z5Check class_weights resemble sample_weights behavior.r6r4rVrqrgY@)rrVrJrJN) ririsdatarr+rr@rr)rreg1reg2rr5s rF"test_class_weight_vs_sample_weightr>s= 5DHHTYY $ J 'DHHTYY $ DJJ/GGDKK--.M$++"#s*#u-L 5DHHTYY ]3 L )DHHTYY $ DJJ/ 5DHHTYY ]A%56 L )DHHTYY ]3 DJJ/rHc@tjddgddgddgddgddgg}gd}tdgd}|j||td d igd }|j||t |j d d ggtjdgy)Nr1rr2rrr3)rr5rV)r5r<rVrX)rr5rVrZgɿrJrW)r@rrrr-r)rpryrs rFtest_class_weights_cvr@s 4,q D$<#sc3ZPQAA n ECGGAqM !Uc>tjddgddgddgddgddgg}tjgd}|jd}gd}t|}t |r t |n|}t |dd| }d }|j|||jj|||fk(sJtjgdgd gd gj}|jd }|j|||jj|||fk(sJy) Nr1rr2rrr3rETrBrV)rVrWrVrWrV)rWrWrVrWrW) r@rrrrrrrrK transpose) rHrryrRr<rCrrDrs rF)test_ridge_classifier_cv_store_cv_resultsrHs 4,q D$<#sc3ZPQA "#A I F6{H'/'8{7#gH$x A IEE!QK ==  9i"B BB B  -/BC ik IEE!QK ==  9i"B BB BrH Estimatorctjjd}d}d\}}|tur|j |}n|j dd|}|j ||}||}|j |usJd|jd|j||t|j tj|y)NrrErrJr;z`alphas` was mutated in `z .__init__`) r@rbrcrrrJr<rrr-r%)rIror<rRrSryrp ridge_ests rFtest_ridgecv_alphas_conversionrLs ))   "C F IzG IIi  KK1i ( )Z(A(I   v % #I$6$6#7zB %MM!Qy''F); 0.0.r) r@rbrcrrrJrrrr) rarIror<rRrSryrprKs rFtest_ridgecv_alphas_zerorOs ))   "C F IzG IIi  KK1i ( )Z(AB/I z ]]:-R S MM!Q     a  s B<<Cctjjd}d}dD]\}}|j|}|j||}d|j |z}t d}t ||}|j|||d|i} tt| | } | j||||j| jjk(sJt|j| jjy) NrrE)rrrrrrNrrzr)r@rbrcrrandrrrrrrcbest_estimator_rzr,r) ror<rRrSryrprrar1 parametersgss rFtest_ridgecv_sample_weightrVs ))   "C F"3K : IIi  IIi ,chhy11 1XB/ Aq 6v& %':" 5 q!=1~~!3!3!9!9999!'--1C1C1I1IJKrHc ddg}ddg}tjjd}t||D]!\}}|j || |j ||j |dzdz}d}d}|ddtj f |tj ddft d j | j | j | fd} fd } d } tjt| 5|dddd } tjt| 5| ddd$y#1swY:xYw#1swY.fit_ridge_not_okGs IIa1 2rHc,jyr?rY)rprsample_weights_not_OK_2rysrFfit_ridge_not_ok_2zUtest_raises_value_error_if_sample_weights_greater_than_1d..fit_ridge_not_ok_2Js IIa3 4rHz)Sample weights must be 1D array or scalarr) r@rbrcrrrrrrrr) n_sampless n_featuressrorRrSsample_weights_OKsample_weights_OK_1sample_weights_OK_2r[r^rrprrZr]rys @@@@@rF9test_raises_value_error_if_sample_weights_greater_than_1drd/s\QJa&K ))   #C!$Z!=! : IIi , IIi IIi0A59!! 1!RZZ- @"3BJJM"BA  !Q)* !Q+, !Q+, 3 5> ]]:W 5    > ]]:W 5 !   ! !7!.   ! !sE E+E( +E5 zn_samples,n_featuresrJctjjd}tdd}tdd}|j ||}|j |}|j |dzdz}||} |j | |||j |||t |j|jd y) NrrFrrJrVrrr)r@rbrcrrrr,r) rRrSrro sparse_ridge dense_ridgerprysample_weightsrs rF&test_sparse_design_with_sample_weightsriVs ))   #Cs%8Lc7K )Z(A )AYYy)Q.2N"HXq?OOAqO7l00+2C2CQOrHctjddgddgddgddgddgg}gd}td}|j||y) Nr1rr2rrr3)rVrZrqr;)r@rrr)rpryrs rFtest_ridgecv_int_alphasrklsM 4,q D$<#sc3ZPQAA < (E IIaOrHzparams, err_type, err_msgr<)rVrWiz alphas\[1\] == -1, must be > 0.0)gr1g$z"alphas\[0\] == -0.1, must be > 0.0)rVr1z1alphas\[2\] must be an instance of float, not strcd\}}tj||}tjdd|}tj||5|di|j ||dddy#1swYyxYw)z?Check the `alphas` validation in RidgeCV and RidgeClassifierCV.rrrJrNr)rorrJrrr)rIrNerr_typerrRrSrprys rFtest_ridgecv_alphas_validationrousk(!Iz )Z(A Aq)$A xw /&F1%&&&s A,,A5cd\}}tj||}|turtj|}ntjdd|}|dj ||y)zCheck the case when `alphas` is a scalar. This case was supported in the past when `alphas` where converted into array in `__init__`. We add this test to ensure backward compatibility. rrrJrVr;N)rorrrJr)rIrRrSrprys rFtest_ridgecv_alphas_scalarrqs[!Iz )Z(AG IIi  KK1i ( QAq!rHctdd}|jtt|jj dtj dk(sJy)Nr8rV)rrr)rrrrrr)rs rFtest_sparse_cg_max_iterrssB {Q /CGGJ # 99??1 !1!1!!4 44 4rHz-ignore::sklearn.exceptions.ConvergenceWarningc d}tt}}tj||dfj}t ddD]S}dD]L}t ||d}|j||t|jtj||NUdD]0}t |dd}|j|||j0Jy) NrJrVrQ)r;r<r:r)rrr)r8r7r9r5) rrr@tilergrangerrr-n_iter_)rrpryy_nrrrs rF test_n_iterrysI zqA ''!i^ $ & &C!QKJ- JFveDC GGAsO s{{BGGHi,H I JJ 3#6A48 3{{"""#rH)r:r8rrwith_sample_weightc|dk(}td||\}}d}|rAtjj|}d|j |j dz}|dk(rd n|} t | d | } t |d | } | j||| | j|||| t| j| jt| j| jd y)aCheck that ridge finds the same coefs and intercept on dense and sparse input in the presence of sample weights. For now only sparse_cg and lbfgs can correctly fit an intercept with sparse X with default tol and max_iter. 'sag' is tested separately in test_ridge_fit_intercept_sparse_sag because it requires more iterations and should raise a warning if default max_iter is used. Other solvers raise an exception, as checked in test_ridge_fit_intercept_sparse_error rr)rSrUr+Nrrr_rr8r)rrr+rgƠ>r) r/r@rbrcrerrrr*rr) rrzrlrr+rpryrro dense_solverrgrfs rFtest_ridge_fit_intercept_sparser}s  H )$6 DAqMii##$67ckkqwwqzk:: #)F"2;L|JKEHELOOAq O6]1%q FK**L,C,CDK%%|'9'9ErH)r<r7r9ctdd\}}||}t|}dj|}tjt |5|j ||dddy#1swYyxYw)Nrr)rSrUrzsolver='{}' does not supportr)r/rformatrrrr)rrrpryX_csrrfrs rF%test_ridge_fit_intercept_sparse_errorrsk *Ra HDAq ! E'L,33F;G z 1#"###s A--A6c tdd|d\}}|rBtjj|}d|j |j dz}nd}||}t ddd d d }tdi|} tdi|} | j||| tj5tjdt| j||| dddt| j| jdt| j| jdt!j"td5tdd ddj||dddy#1swYxYw#1swYyxYw)Nrrg@)rSrRrUr*rrr_r;Trr)rzrrrrrr$-C6?rz"sag" solver requires.*rrX)rrrrr)r/r@rbrcrerrrrr'r( simplefilterr r*rrrr) rzrlrrpryrorrrNrgrfs rF#test_ridge_fit_intercept_sparse_sagrsg *1Cc DAqii##$67ckkqwwqzk::  ! E %tF/&/K?6?LOOAq O6  "@g{3?@K**L,C,C$OK%%|'9'9E k)B CW U$D4HLLUTUVWW @@ WWs//E. E:.E7:Freturn_interceptrr container)rr8r9r:r;r<rc td}|jdd}gd}tj||}d}|rd}||z }||} d\} } trdnd } |d k(} |d vr;|r9t j td 5t| || |||| | | dddyt| || ||| || | }|r$|\}}t||d| t||d| yt||d| y#1swYyxYw)z=check if all combinations of arguments give valid estimationsrrr)rVrJr5rg@)rXư>rXrr)r;rzIn Ridge, only 'sag' solverr)rzrrrr+rrUN)rzrrr+rrrUrr) r#rRr@rr1rrrrr*)rrrrrorp true_coefsrytrue_intercept X_testingrzrrr+outrrs rF.test_ridge_regression_check_arguments_validityrs# R C qAJ q*AN A! IJE34$D H _$)9 ]]:-J K  +!1!      #)  Cijqt< >EZad;?  s C((C1)r7r8r9r:r;r<rctjjd}d}|dk(}d\}}|j||}|j|}|j tj }|j tj } dtj tj jz} t||d| |} | j|| | j} t||d| |} | j||| j}| j|jk(sJ|j|jk(sJ| j|j|jk(sJ| j|j|jk(sJt| j| jdd y) NrrrrQrJ)rzrrrr+rgMb@?r)r@rbrcrr?r4finfo resolutionrrrrArr*)rrorzr+rRrSX_64y_64X_32y_32rridge_32coef_32ridge_64coef_64s rFtest_dtype_matchrMsx ))   "C E H Iz 99Y +D 99Y D ;;rzz "D ;;rzz "D bhhrzz"-- -CFScHH LLtnnGFScHH LLtnnG ==DJJ && & ==DJJ && &   D ! ' '4:: 55 5   D ! ' '4:: 55 5HNNHNNDIrHcDtjjd}tjddg}d\}}}|j ||}|j ||}|j tj }|j tj }t|d} | j||| j} t|d} | j||| j} | j|jk(sJ| j|jk(sJ| j|j|jk(sJ| j|j|jk(sJt| j| jdy) Nrrr)rr rJr9rrr) r@rbrcrrr?r4rrrrArr+) rorzrRrSn_targetrrrrrrrrs rFtest_dtype_match_choleskyrrsL ))   "C HHc3Z E&-#Iz8 99Y +D 99Y )D ;;rzz "D ;;rzz "D54H LLtnnG54H LLtnnG ==DJJ && & ==DJJ && &   D ! ' '4:: 55 5   D ! ' '4:: 55 5BrH)r7r9r:r8r;r<rseedctjj|}d\}}|j||}|j|}tj||d|j|zz}d}|dk(} t } |dk(rdnd} tj tjfD]9} t|j| |j| |||d| dd d d | | <;| tj jtj k(sJ| tjjtjk(sJt| tj | tj| y) NrQrrrr8rXrbrrF) rzrrUrr+rr return_n_iterrr) r@rbrcrrrr4r7rr?rAr*) rrrUrRrSrprryrzr+resultsr current_dtypes rF%test_ridge_regression_dtype_stabilityrsN 99((.L Iz9j1A   j )D q$$!3!3I!>>>A E HfG[(4dD**bjj1  !1 HH] # HH] #%" "     2::  $ $ 22 2 2::  $ $ 22 2GBJJ')<4HrHctd\}}tj|}|dddddf}|ddd}tdj ||y)Nrr rJr;r)r r@asfortranarrayrr)rprys rFtest_ridge_sag_with_X_fortranrsS  +DAq !A #A#q& A #A#A Aq!rHzClassifier, paramscntdd\}}|jdd}tj||gd}|d i|j ||}|j |}|j |j k(sJt|dddf|dddftdj ||y) zRCheck that multilabel classification is supported and give meaningful results.rVr)rrUrWrNr;rr) r rr@rrrrr-r) ClassifierrNrpryrrrs rFtest_ridgeclassifier_multilabelrs *AA FDAq "aA 1vA&A  v  " "1a (C [[^F <<177 "" "vad|VAqD\2 Aq!rHrr)rXrr5rcRtjddgddgddgddgg}tjdd g}|rd }|j||z}n|j|}t|d || }|j ||tj |j d k\sJy)z:Test that positive Ridge finds true positive coefficients.rVrJrrQrrr rrYrTrzr+rrrN)r@rrrrrdr)rrrzrprrryrs rF#test_ridge_positive_regression_testrs 1a&1a&1a&1a&12A 88QH D EE$K) # EE$K d6 E IIaO 66%++" ## #rHctjjd}|jdd}|j dd|j d}|r d}||z|z}n||z}||j |j dd zz }g}d D]<}t|||d } |j| j||j>t|d ddy)zTest that Ridge w/wo positive converges to the same solution. Ridge with positive=True and positive=False must give the same when the ground truth coefs are all positive. r,rqr5rrVr_rr)TFr)rzr+rrrr=N) r@rbrcrrerrfrrrrr*) rrzrorprrryrr+rs rF%test_ridge_ground_truth_positive_testrs ))   #C #sA ;;sCaggaj; 1D Hy  H $t ++AG!.(-U  uyyA,,- . W4a0rH)r7r9r:r8r;r<c d}tjddgddgg}tjddg}||z}t|d|d }tjt d 5|j ||d d d tjt d 5t|||d|d\}}d d d y #1swYBxYw#1swYy xYw)z5Test input validation for positive argument in Ridge.r5rVrJrrQrWTFrzdoes not support positiverNzonly 'lbfgs' solver can be used)r+rr)r@rrrrrrr)rrzrprryrrws rFtest_ridge_positive_error_testr s E 1a&1a&!"A 88QG D DA V5 QE z)D E !Q z)J K  q%$v 1    s$B8C8CC c& tddd\ dd}d fd }tj }td j }||}||}||ksJt|D]}||| }||krJy) z?Check ridge loss consistency when positive argument is enabled.rrr}r5rqNch|j}|Utjj|}|j|j d||jj z}n |j}dtj|zz |z dzzdztj|dzzzS)Nrr_rrJ)rr@rbrcrrerr) rrU noise_scalerrorrprzrys rF ridge_lossz,test_positive_ridge_loss..ridge_loss s$$  #))'' 5C;;Q %++BSBS!TTD;;DRVVQT\I5!;<> DNN LLOMGGAq G.CIIt$/ 2 KKDqqwwqzKBMM"#bcFdNFGGAq G. 99>> DNN GGAcrc1fIq"v]3B-?G@CIIt$/ 2 ?6? % %BEEF7O,C % DDHHQ!6H7   wvh&STUDJJ403# IIK A. Q/0q 9B A. Q/0 1B#((*O$i1n%*%nn  &6 Q78qO# Q  b ! ?6?  q!?  CD ?6?  r2_  EDDJJ +9rH)rrrZ)rZrrno_swwith_sw no_interceptwith_interceptrc|\}}t|||d\}}|r-tj|jdf} d| ddd<nd} tjddd } t || d |d } | j ||| tjg|jt| } t} t| D]\}}t| j||D]h\}\}}t||d}|r|j ||||| | n|j |||||j||| |d|f<jt| j| dy)arCheck that the predictions stored in `cv_results_` are on the original scale. The GCV approach works on scaled data: centered by an offset and scaled by the square root of the sample weights. Thus, prior to computing scores, the predictions need to be scaled back to the original scale. These predictions are the ones stored in `cv_results_` in `RidgeCV`. In this test, we check that the internal predictions stored in `cv_results_` are equivalent to a naive LOO-CV grid search with a `Ridge` estimator. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/13998 rrRrSrrU)rrNrJrr rrGT)rQr<rHrrIrr7)rzrr.rr)r r@rrrrremptyrr enumeraterrrr*rK)rQrTrzrrrRrSrpryrr<rrra alpha_idxrzidx train_idxtest_idxrs rF!test_ridge_cv_results_predictionsr s.$Iz  iVW DAqqwwqzm4  cc [[Q "F(# H LLA]L3((!8177!8CK!89K B%f- J 5*3BHHQN*C J &C&)X]5QE! iL!I,mI>V !I,) 5/4}}Qx[/IKS)+ , J JH((+DArHc |\}}t||d|\}}tj|}t|dd}|j |||t } t |j} tj| j|D cgc]4\} } | j || || || j|| 6c} } } t|jt||  ycc} } w) zCheck that `RidgeCV` works properly with multioutput and sample_weight when `scoring != None`. We check the error reported by the RidgeCV is close to a naive LOO-CV using a Ridge estimator. rJrrGT)rQrHrIrrN)r r@rrrr rrcsqueezerrr*rr)rQrTrlrRrSrpryrrrartraintest y_pred_loos rF'test_ridge_cv_multioutput_sample_weightr s$Iz '  DAq GGI&M#;dH LLA]L3 B  (E "xx{  t IIah% e8LI M U U$  JH((+=a+L*LM s9C5 c  |\}}t||dd\}}d fd}t|}|j||t}t |j }t j|j|D cgc]/\} } |j|| || j|| 1c} } } t|j ||  ycc} } w) zECheck that `RidgeCV` works properly with a custom multioutput scorer.rJrrc||z dz}tj|d}|jdk(rtj|ddg S| S)NrJrrrV)r)r@rAndimr)y_truerDerrors mean_errorss rF custom_errorz=test_ridge_cv_custom_multioutput_scorer..custom_error0 sP6/a'ggf1-   q JJ{QF;; ;|rHc6||j| S)zGMultioutput score that give twice more importance to the second target.)r)rrpryrs rFcustom_multioutput_scorerzJtest_ridge_cv_custom_multioutput_scorer..custom_multioutput_scorer: sQ 1 1! 4555rH)rHrN) r rrr rrcr@rrrr*r) rTrRrSrpryrrrarrrrrs @rF'test_ridge_cv_custom_multioutput_scorerr( s$Iz  aa DAq689H LLA B  (EKM88TU;WKE41U8QuX & . .qw 7WJH((<:+F*FG Xs4C metaestimator)enable_metadata_routingc.|jy)zTest that `RidgeCV` or `RidgeClassifierCV` with default `scoring` argument (`None`), don't enter into `RecursionError` when metadata is routed. N)get_metadata_routing)rs rF*test_metadata_routing_with_default_scoringrN s O((*rHzmetaestimator, make_datasetc|ddd\}}t|}|j||tj|jdy)zTest that `set_score_request` is set within `RidgeCV.fit()` and `RidgeClassifierCV.fit()` when using the default scoring and no UnsetMetadataPassedError is raised. Regression test for the fix in PR #29634.rqrrr}rrN)rrr@rr)rrrprys rF+test_set_score_request_with_default_scoringrW sF #!" EDAq-(Ma"''!''!**=>rH) rqrqrrZrVg*@rYrYTFFNr?)r' itertoolsrnumpyr@rscipyrsklearnrr sklearn.basersklearn.datasetsrr r r sklearn.exceptionsr sklearn.linear_modelr rrrrrsklearn.linear_model._ridgerrrrrrrsklearn.metricsrrrsklearn.model_selectionrrrr r!sklearn.preprocessingr" sklearn.utilsr#sklearn.utils._array_apir$r%r&r'r(-sklearn.utils._test_common.instance_generatorr)sklearn.utils._testingr*r+r,r-r.sklearn.utils.estimator_checksr/r0sklearn.utils.fixesr1r2r3r4r5r6SOLVERSrr load_diabetesdiabetesr;rrrrrindrbrcror$ load_irisr:rrrGrKfixturermark parametrizerrrrrrrrrrrrrr rrr/rCrPr4r7r%rgrlryrrrrrrrrrrrrrrrrrr rrr"sortedr*r/r8r>r@rErHrLrOrVrdrirkr TypeErrorrorqrsr)ryr}rrrrrrrrvrrrrrrrrrrrrrrrrrHrFrsU , 2HG/,S B 4#S !8 ! ! #! Jbii   #$iiA C $3i#C*S/ JxDKK%+'(C&)C&L7+4-8)#9,)#X7+4-8#?9,#?L7+4-8%29,%2P7+4-82+9,2+j7+4-8289,28j7+4-82+9,2+j7+4-8+dVn-DE3+./'/F9,/'d+ 4<>$0*@"23.9 D:4 D"NO*T5M:.9.:;P.""NO*T5M:.93:;P3$     *Z*1 G F^ #+  (F+R  +R\5,eW%562::rzz":; ~(EFWh$7ff=MN4-8)K9OG<7)KX J5$-lK5PQ#KL4-8Y $:@PQ ~(EFFGR9MR F25$-lK5PQi[14-8 $" 2::rzz":; ~(EF*IG< 92R*IZ5$-lK5PQeW%564-8 $" 2::rzz":; ~(EFIG< 97RI<K@eW%56 ~(EF2w/#=G0G7=G@+dVn-DE " %=>:? F:(6r3$ % (/: E 24GH-- i!$5$79L#MNay)0*  0DN3 P,$T:7I$JKeAh/0+dVn-DE"F1L"eAh/0+dVn-DE 2F1 2 EI$ N.-/ %'AB   Uu%!  !     ( //1B1DE.-/X X&.-/ :'G',',T*F3I,JKFLF."  .9D: D ?F2C DE0F08 A .0LM:T:7I$JKCLC8w0A&BC=D=(ay)w0A&BCD*(K0$!N/1a&1a&1AB^#n4~EVP C P"w0A&BC M "J0ST * +  1 } %  @   &!D"&w0A&BC"D""5 KL#M#$#IJ-e}=.9#F:>K#FL#>?.9#:@#-e}=.9W:>W8+eT];4*?@rxxj>&AB O6<CA< 6NO-t}7IBVWeT]9I(JeW%56q!f-8B.7XP8BveW%56Xx$8vv>NONP7NDXx$8vv>NOHPHJ74E*FG-+.H+! O$  12-?.?rH