rtjZ>ddl?m@Z@mAZAddlBmCZCmDZDmEZEmFZFmGZGmHZHgdZIdZJdZKe jLZMeMjNeMjOcZPZQejRePjSdZTejUVdZWeWXeTeTddZTePeTeQeTcZPZQe jYZZeZjNeZjOcZ[Z\dZ]dZ^ej_ddgdZ`ejabdeIejabddd gd!ZcejabdeIejabddd gd"ZdejabdeIejabddd gd#ZeejabdeIejabddd gd$ZfejabdeIejabddd gd%ZgejabdeIejabddd gd&ZhejabdeIejabddd gejabd'dgeFzejabd(d)d*gd+Zid,Zjd-Zkd.Zld/Zmd0Znd1Zoejabd2gd3ejabd4eFd5Zpejabd6gd7ejabd8dd gejabd4eFd9Zqejabd6gd7ejabd8dd gejabd4eFd:Zr ddAZsejabdBdCegdDdgeFzDejabdEgdFdGZtdHZuejabdIdJdKgejabdLejvejwgejabdMejxgeFzejabdNdOdPgdQdgRejabddd gejabdSgdTdUZydVZzejabd(dWdXgdYdZgRejabdgd[ejabddd gejabdNd\d]gdQdgRejabdMejxgeFzd^Z{ejabd(dWdXgdYdZgRejabdId_gejabddd gejabdNgd`gdaRejabdLejvejwgejabdMejxgeFzdbZ|ejabd(dWdXgdYdZgRejabdIdJdKgejabddd gejabdNgd`gdaRejabdLejvejwgejabdMejxgeFzdcZ}ddZ~ejabdIdJdKgejabdMejxgeFzejabdedfdggejabdhgdidjZejabd'dgeFzejabdNgdkgdaRejabdIgdldmZdnZdoZejabdped qefed qefgdrZejabdpeefeefgejabdsddtgduZdvZdwZdxZdyZejabdzdd{e]gejabdsde*d|gejabd'dgeFzd}Zejabdsde*d|gejabd'dgeFzd~ZdZ ddZejabde5ejabdeAege8RejabdedJedJedJedKege8RdZejabdedJegejabde5dZejabde5dZejabde6d dZejabdee2dZejabdeeeeeefejabd4eFdZdZejabdeefdZdZejabdzdde^gdZejabdzdd{e]gdZejabdeegdZejabdsddtgejabdeegdZdZdZejabdddtgdtdggejabd'eDeEzeFzeGzeHzdZdZejabdeegejabdddiedfddiedfddiedfgdZejabdeegdZdZejaddZejabdgdejabddd gejabd4eFdZejabdgdejabd4eFdZejabddd gejabd4eFdZejabdd dgejabddejdgejabdejgeFzejabdgddZejabdgddZdZejabdgdejabded>dZdZejabdeifedsdifedsdtifgdZejabdddgejabddd gejabd(gdâdĄZejabddd gejabd(gdâdńZejabdgdƢdDŽZejabd(gdâdȄZejabd(gdâdɄZdʄZejabdd dgejabd'dgeFzejabddQdgejabdeIdgzd̄ZejabdNddgdQdgRejabdd dgddgRejabdd dgddgRejabdIdJdKgejabdd>dgdԄZejabdIdJdKgejabdNddgdQdgRdՄZejabdNddgdQdgRdքZejabdeege dئdلZejabdeefeefge dئdۄZdS)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||kSN)npmeany_testy_predkwargss c/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/linear_model/tests/test_ridge.py_accuracy_callablerGXs 76V# $ $$c6||z dzS)N)rA)rCrDs rF_mean_squared_error_callablerK\s f_ " ( ( * **rHlongwide)paramsc|jdkrd\}}nd\}}t||}tj|}t ||||}d|dddf<t j|\}}} tj|dksJ|ddd|f|dd|df} } | d|ddf| |dddf} } |jdkr@| d d | }||z}|| | ||z d zzz }nB| d d | }| j tj d|z z| j z|z}d}|tj |z}d|d<t j|j |z|z|j |z}|||zz }|||zz }tj|tj|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 3 3C 1SV   AAaaaeHz!}}HAq" 6!d(    qqq"1"uXqABBxB AAAY122qqq5 C};;3Rj;AA L R#**)j"8*99Q> >> KKCbyK 9 95271q5>>)BD014 E  J'''AAfIacAgk13733J H E!j. G 9>>% 29>>'#:#: : : : : a: %%rHsolver fit_interceptTFc|\}}}}d}t|d||dvrdnd|} |tj|z } |||zz } dtj| dztj| dzz z } t di| } |d d d d f}|r |d }n2||d z }||z }d }| |||d d }| jtj|ksJt| j || ||tj| ksJt di| ||tj |j d  } | jtj|ksJt| j || ||tj| ksJ| j|ksJd S)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 ..EEE' F271::~HAH I26)Q,''"&1*=*===H OOFOOE !!!SbS& AH A  L  IIaOOO 9D  v}Y77 7 7 7 7EK&&& ;;q!   h 7 7 7 7 7 7 OOFOO  1BGAGAJ4G4G  H HE  v}Y77 7 7 7 7EK&&& ;;q!   h 7 7 7 7 7 7 =F " " " " " "rHcn|\}}}}|j\}} d} t| dz |||dvrdnd|} |ddddf}d tj||fd z}tj|t || d z ksJ|r |d} n2||d z }||z }d } | |||dd}| j tj | ksJt| j tj||fd dS)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!TGIz E ai#..EEE'    E !!!SbS& A bnaV!,,,,A 9  # #s9j1n'E'E E E E EH A  L  IIaOOO 9D  v}Y77 7 7 7 7EKtTz!2>>>>>>rHcf|\}}}}|j\}} d} td| z|||dvrdnd|} |ddddf}tj||fd }tj|t || ksJtj||f}|r |d} n2||d z }||z }d } | |||dd}| j tj | ksJt| j|d dS) 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_XrsZ&MAq!TGIz E %i#..EEE'    E !!!SbS& A 1vA&&&A 9  # #s9j'A'A A A A A ad AH A  L  IIaOOO 9D  v}Y77 7 7 7 7EKD111111rHcN|\}}}}|j\}} d} t| |||dvrdnd|} td i| } |r!|ddddf}|d} |dd}nd} | |||| ks|s6| jt j| ksJt| j|dSt| ||t||z| z|tj tj | j| jftj tj | |fksJt jd | jt j| ksJt| j|dS) 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$GIz E #..EEE' F OOFOOE aaa"fIH CRCy  IIaOOO :]6=#;#;;;;; T*****  a((!,,,D9,a000y~~beE$4ek$ABCCbinn E)T/ "G G       OPPPP6=#;#;;;;; T*****rHc"|\}}}}|j\}} d} t| |||dvrdnd|} |r!|ddddf}|d} |dd}nd} dtj||fd z}tj|t || ksJ| |||| ks|sa| jtj | ksJ|d krtj t| j tj||fdSt| ||tjtj| j| j ftjtj| ||fksJtjd | jtj | ksJt| j tj||fdS)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$GIz E #..EEE'    E aaa"fIH CRCy  bnaV!,,,,A 9  # #s9j'A'A A A A A IIaOOO:]6=#;#;;;;; Z   KMMM RU4:%677777  a((!,,,y~~beE$4ek$ABCCbinn E)T4' (G G       OPPPP6=#;#;;;;; RU4:%677777rHc|\}}}}|j\}} d} t| |||dvrdnd|} |r!|ddddf}|d} |dd}nd} tj||fd}tj|t || ksJtj||f}| |||| ks|s6| j tj | ksJt| j |dSt| ||tjtj| j | j ftjtj| |fksJtjd | j tj | ksJt| j |dS) 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$GIz E #..EEE'    E aaa"fIH CRCy  1vA&&&A 9  # #s9j'A'A A A A A ad A IIaOOO:]6=#;#;;;;; T*****  a((!,,,y~~beE$4ek$ABCCbinn E)T/ "G G       OPPPP6=#;#;;;;; T*****rHsparse_containerrzr{Gz?c|=|r|tvrtjn|s|tvrtj|\}}}} |j\} } t dd| } t||||dvrdndd| } |dddd f}tj ||fd }tj ||f}tj | d| z f|z} |r | d }n2|| d z }|| z }d}| ||}| ||| | dd } | j tj|ksJt| j| dS) 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_weightsrs$#  V+HHH KMMMM 61Q#Q#Q KMMM%MAq!TGIz  3 3B #..EEE'    E !!!SbS& A 1vA&&&A ad A r1r6z U "BH A  L #  Q   IIa"I%%% 9D  v}Y77 7 7 7 7EK&&&&&rHcBtdd}tt|dg}t jttj}t||dg}t jtj|j}t||dS)NrWrVrrz) y_diabetesreshaper X_diabetesr@dotrgrr,)ryrK dual_coefcoef2s rFtest_primal_dual_relationshiprs2q!!A :q 7 7 7D z:<((A&q!D6:::I F:< + + -EdE*****rHc 2tjd}|d}|dd}d}t jt |5t||ddddd ddddS#1swxYwYdS) NrrZz3sparse_cg did not converge after [0-9]+ iterations.matchrr8rV)rzrrrverbose)r@rbrcrandnrwarnsr r)roryrpwarning_messages rF&test_ridge_regression_convergence_failr s )   " "C ! 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'D E  rHct|||\}}td\}}||}||} ||| } | |} | j} td5||||| |}} || |}|| }|j | j cxkr |j ksnJtt|td| tt|jtd| ddddS#1swxYwYdS)NrrUTrrrr)r/r rFrrclasses_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{JOOJB )q 9 9 9DAq 88J  D 88J  D}}T4((Ht$$G"''))J 4 0 0 0XXZZVZ44bjjfj6U6Ud==t,,""4((} 8888888888Bu===wGGG78#4EJJJJWWW XXXXXXXXXXXXXXXXXXs&C E<<FFc Tt|||\}}tddd\}}||}||}tjgd|} t | d||} | |} td 5||| ||| } } | || | fD]}t |d| | }|| }|j | j cxkr |j ksnJtt|td | t| ddddS#1swxYwYdS)ztCheck that passing an array for alpha works with array API dispatch. Non-regression test for issue #34003. rrZr)rrSrU)rr<r)rHr7rTrrrrrN) r/r rFr@r-rrrrrr*r&r%)rrrrrrpryrrrCrrrrrzrrs rF%test_ridge_per_target_alpha_array_apirs&o{JOOJB Q2A F F FDAq 88J  D 88J  D Z((( ; ; ;F6%00044T4@@Ht$$G 4 0 0 0  ZZVZ44bjjfj6U6Udbjjj??@  E5777;;D$GGH&&t,,G=GM<<<qAI#vuo5 0 0Vf.?@@@ -r{ - -"( - - -  ]:\ : : : 0 04888 0 0 )Y/// 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 $ / / /E .+-; . . . z 6 6 6,, t 4 4 4 , , IIi + + + , , , , , , , , , , , , , , ,,,,,,,,,,,,,,,, GGE D D D D k 6 6 6,, t 4 4 4 , , IIi + + + , , , , , , , , , , , , , , ,,,,,,,,,,,,,,,,,,,s=DC1% D1C5 5D8C5 9DD D FE?3 F?F FF FFFH!&H = H! 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This case was supported in the past when `alphas` where converted into array in `__init__`. We add this test to ensure backward compatibility. rrrJrVrBN)rorrrQr)rPrRrSrprys rFtest_ridgecv_alphas_scalarrxs}!Iz )Z((AG IIi  KK1i ( ( IQAq!!!!!rHctdd}|tt|jjdtjdksJdS)Nr8rV)rrr)rrrrrr)rs rFtest_sparse_cg_max_iterrzsP {Q / / /CGGJ ### 9?1 !1!!4 4 4 4 4 4 4rHz-ignore::sklearn.exceptions.ConvergenceWarningc d}tt}}tj||dfj}t ddD]W}dD]R}t ||d}|||t|j tj||SXdD]3}t |dd}||||j J4dS) NrJrVrQ)r;r<r:r)rrr)r8r7r9r<) rrr@tilergrangerrr-n_iter_)rrpryy_nrrrs rF test_n_iterrsI zqA '!i^ $ $ &C!QKKJJ- J JFveDDDC GGAsOOO s{BGHi,H,H I I I I J 3##6A4888 3{""""##rH)r:r8r#rwith_sample_weightc|dk}td||\}}d}|rCtj|}d||jdz}|dkrd n|} t | d | } t |d | } | ||| | |||| t| j | j t| j | j d dS)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 r#r)rSrUr3Nrrr_rr8r)rrr3rgƠ>r) r7r@rbrcrerrrr*rr) rrrlrr3rpryrro dense_solverrnrms rFtest_ridge_fit_intercept_sparsers"  H )$6   DAqM;i##$677ckkqwqzk::: #)F"2"2;;L|JJJKEHEEELOOAq O666]]1%%q FFFK*L,CDDDK%|'9EEEEEErH)r<r7r9c"tdd\}}||}t|}d|}tjt |5|||ddddS#1swxYwYdS)Nrr)rSrUr zsolver='{}' does not supportr)r7rformatrrrr)rrrpryX_csrrmrs rF%test_ridge_fit_intercept_sparse_errorrs *Ra H H HDAq M!  E'''L,33F;;G z 1 1 1##"""##################s BB Bc@tdd|d\}}|rDtj|}d||jdz}nd}||}t ddd d d }tdi|} tdi|} | ||| tj 5tj dt| ||| dddn #1swxYwYt| j| jdt| j| jdt!jtd5tdd dd||ddddS#1swxYwYdS)Nrrg@)rSrRrUr2rrr_r;Trr)rzrrrrrr+-C6?rz"sag" solver requires.*rrX)rrrrr)r7r@rbrcrerrrrr.r/ simplefilterr'r*rrrr) rrlrrpryrorrrNrnrms rF#test_ridge_fit_intercept_sparse_sagrsb *1Cc   DAqi##$677ckkqwqzk:::  M!  E %tF//&//K??6??LOOAq O666  " "@@g{333???@@@@@@@@@@@@@@@K*L,C$OOOOK%|'9EEEE k)B C C CWW U$D4HHHLLUTUVVVWWWWWWWWWWWWWWWWWWs$3DDD(FFFreturn_interceptrr container)rr8r9r:r;r<r#c 4td}|dd}gd}tj||}d}|rd}||z }||} d\} } trdnd } |d k} |d vrO|rMt jtd 5t| || |||| | | dddn #1swxYwYdSt| || ||| || | }|r-|\}}t||d| t||d| dSt||d| dS)z=check if all combinations of arguments give valid estimationsrrr)rVrJr<rg@)rXư>rXrr#)r;rzIn Ridge, only 'sag' solverr)rzrrrr3rrUN)rzrrr3rrrUrr) r#rYr@rr1rrrrr*)rrrrrorp true_coefsrytrue_intercept X_testingrzrrr3outrrs rF.test_ridge_regression_check_arguments_validityrs R C qAJ q*AN! A ! IJE3 &44$D H _$$)9$ ]:-J K K K   +!1!                       #)    C<ijqt<<<< >EEEEEEZad;;;;;;s B11B58B5)r7r8r9r:r;r<r#cVtjd}d}|dk}d\}}|||}||}|tj}|tj} dtjtjjz} t||d| |} | || | j } t||d| |} | ||| j }| j |j ksJ|j |j ksJ| |j |j ksJ| |j |j ksJt| j | j dd dS) Nrrr#rXrJ)rzrrrr3rgMb@?r)r@rbrcrrFr;finfo resolutionrrrrHrr*)rrorzr3rRrSX_64y_64X_32y_32rridge_32coef_32ridge_64coef_64s rFtest_dtype_matchrMs )   " "C E H Iz 99Y + +D 99Y  D ;;rz " "D ;;rz " "D bhrz""- -CFScHH LLtnGFScHH LLtnG =DJ & & & & =DJ & & & &   D ! ! '4: 5 5 5 5   D ! ! '4: 5 5 5 5HNHNDIIIIIIrHctjd}tjddg}d\}}}|||}|||}|tj}|tj}t|d} | ||| j } t|d} | ||| j } | j |j ksJ| j |j ksJ| |j |j ksJ| |j |j ksJt| j | j ddS) Nrrr)rrrJr9rrr) r@rbrcrrrFr;rrrrHrr+) rorzrRrSn_targetrrrrrrrrs rFtest_dtype_match_choleskyrrsu )   " "C Hc3Z E&-#Iz8 99Y + +D 99Y ) )D ;;rz " "D ;;rz " "D5444H LLtnG5444H LLtnG =DJ & & & & =DJ & & & &   D ! ! '4: 5 5 5 5   D ! ! '4: 5 5 5 5BBBBBBrH)r7r9r:r8r;r<r#seedctj|}d\}}|||}||}tj||d||zz}d}|dk} t } |dkrdnd} tjtjfD]E} t| | | | |||d| dd d d | | <F| tjj tjksJ| tjj tjksJt| tj| tj| dS) NrXrrr#r8rXrhrrF) rzrrUrr3rr return_n_iterrr) r@rbrcrrrr;r>rrFrHr*) rrrUrRrSrprryrzr3resultsr current_dtypes rF%test_ridge_regression_dtype_stabilityrsq 9((..L Iz9j11A   j ) )D q$$!3!3I!>!>>>A E HffG[((44dD*bj1   !1 HH] # # HH] # #%" " " "   2:  $ 2 2 2 2 2:  $ 2 2 2 2GBJ')<4HHHHHHrHctd\}}tj|}|dddddf}|ddd}td||dS)NrrrJr;r )r r@asfortranarrayrr)rprys rFtest_ridge_sag_with_X_fortranrss  + + +DAq !A ##A#qqq& A ##A#A Aq!!!!!rHzClassifier, paramsctdd\}}|dd}tj||gd}|d i|||}||}|j|jksJt|dddf|dddftd||dS) zRCheck that multilabel classification is supported and give meaningful results.rVr)rrUrWrNr;r r) r rr@rrrrr-r) ClassifierrNrpryrrrs rFtest_ridgeclassifier_multilabelrs *AA F F FDAq "aA 1vA&&&A *  v   " "1a ( (C [[^^F <17 " " " "vaaad|VAAAqD\222 Aq!!!!!rHrr#)rXrr<rcftjddgddgddgddgg}tjdd g}|rd }|||z}n||}t|d || }|||tj|jd ksJdS)z:Test that positive Ridge finds true positive coefficients.rVrJrrQrrrrrYrTrzr3rrrN)r@rrrrrdr)rrrzrprrryrs rF#test_ridge_positive_regression_testrs 1a&1a&1a&1a&122A 8QH  D EE$KK) # EE$KK d6   E IIaOOO 6%+" # ### ###rHctjd}|dd}|dd|jd}|r d}||z|z}n||z}|||jdd zz }g}d D]C}t|||d } || ||j Dt|d dddS)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,r%r<rrVr_rr)TFr)rzr3rrrrDN) r@rbrcrrerrfrrrrr*) rrzrorprrryrr3rs rF%test_ridge_ground_truth_positive_testrs )   # #C #sA ;;sCagaj; 1 1D Hy  H $ $t ++AG!..(-U    uyyA,----W4a000000rH)r7r9r:r8r;r<c d}tjddgddgg}tjddg}||z}t|d|d }tjt d 5|||d d d n #1swxYwYtjt d 5t|||d|d\}}d d d d S#1swxYwYd S)z5Test input validation for positive argument in Ridge.r<rVrJrrQrWTFrzdoes not support positiverNzonly 'lbfgs' solver can be used)r3rr)r@rrrrrrr)rrzrprryrrws rFtest_ridge_positive_error_testr st E 1a&1a&!""A 8QG  D DA V5 Q Q QE z)D E E E !Q z)J K K K   q%$v   1                  s$&B  B B /CCCcj tddd\ dd}d fd }t  }td  }||}||}||ksJt|D]}||| }||ksJdS)z?Check ridge loss consistency when positive argument is enabled.rrrr<r%Nrc:|j}|Jtj|}|j|d||jjz}n|j}dtj|zz |z dzzdztj|dzzzS)Nrr_rrJ)rr@rbrcrrerr) rrU noise_scalerrorrprzrys rF ridge_lossz,test_positive_ridge_loss..ridge_loss s$  #)'' 55C;Q %+BS!T!TTDD;DRVQT\I5!;<<>  D#N LOOMGGAq G...CIt$////3 222 KKDqqwqzKBBMM"##bccFFFdNFFFGGAq G... 9>>  D#N GGAcrc111fIq"v]3B3-?G@@@CIt$////3 222 ??6?? % %BEF7O,C % D DDHHQ!6H777     TvTTTUUUDJ400004333# IIKK A. Q./0q 9 9 9B A. Q./0 1 1B#((**O$i1n$%%%*%%%n  &6 Q&678qO#  Q    b ! ! ??6??  q!?  C CD ??6??  r2_  E EDDJ +++:99999::rH)rwrZ)rZrwno_swwith_sw no_interceptwith_interceptrcD|\}}t|||d\}}|r*tj|jdf} d| ddd<nd} tjddd } t || d |d } | ||| tjg|jt| R} t} t| D]\}}t| ||D]\}\}}t||d}|r+|||||| | n"|||||| ||| |d|f<t| j| ddS)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)rrNrJrrrrNT)rXrCrOrrPrr7)rzrr.rr)r r@rrrrremptyrr enumeraterrrr*rR)rXr[rrrrRrSrpryrrCrrrg alpha_idxrzidx train_idxtest_idxrs rF!test_ridge_cv_results_predictionsr s.$Iz  iVW   DAqqwqzm444  ccc [Q " "F(# H LLA]L333(!817!8CKK!8!8999K B%f-- J J 5*3BHHQNN*C*C J J &C&)X]5QQQE! 6 iL!I,mI>V !I,) 555/4}}Qx[/I/IKS)+ , , JH(+DAAAAAArHc |\}}t||d|\ tj| t|dd}| t }t |j tj fd| D}t|j t | d S) 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. rJrrNT)rXrOrPrrcg|]H\}}||||IS)rrr)rtraintestrprrrys rFrz;test_ridge_cv_multioutput_sample_weight.. se   t IIah% e8LI M M U U$     rHN) r r@rrrr rrisqueezerr*rr) rXr[rlrRrSrrg y_pred_loorprrrys @@@@rF'test_ridge_cv_multioutput_sample_weightr s$Iz '    DAq GI&&M#;dH LLA]L333 B  ( ( (E        "xx{{    JH(+=a+L+L*LMMMMMrHc |\}}t||dd\ dfd}t|}| t}t |j t j fd|D}t|j  | d S) zECheck that `RidgeCV` works properly with a custom multioutput scorer.rJrrc||z dz}tj|d}|jdkrtj|ddg S| S)NrJrrrV)r)r@rAndimr)y_truerDerrors mean_errorss rF custom_errorz=test_ridge_cv_custom_multioutput_scorer..custom_error0 sX6/a'gf1---  q J{QF;;;; ;|rHcD||| 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: s& Q 1 1! 4 45555rH)rOrcg|]@\}}|||ASrr)rrrrprrys rFrz;test_ridge_cv_custom_multioutput_scorer..D sEWWWKE41U8QuX & & . .qw 7 7WWWrHN) r rrr rrir@rrr*r) r[rRrSrrrgrrprrrys @@@@rF'test_ridge_cv_custom_multioutput_scorerr( s$Iz  aa   DAq666668999H LLA B  ( ( (EWWWWWW288TU;;WWWJH(<<:+F+F*FGGGGGrH metaestimator)enable_metadata_routingc>|dS)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" MOO((*****rHzmetaestimator, make_datasetc|ddd\}}t|}|||tj|jddS)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.r%rrrrrN)rrr@rr)rrrprys rF+test_set_score_request_with_default_scoringrW s\ <#!" E E EDAq-((Ma"'!'!**=*=>>>>>rH) r%r%rrZrVr&r'r'TFFNr?)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_diabetesdiabetesrBrrrrrindrbrcror, load_irisrArrrGrKfixturermark parametrizerrrrrrrrrrrrrrrr$r7rJrWr;r>r-rmrrr~rrrrrrrrrrrrrrrrr rrrr)sortedr1r6r?rErGrLrOrSrVr]rkrprrr TypeErrorrvrxrzr0rrrrrrrrrr}rrrrrrrrrrrrrrrrrHrFrs ,,,,,,,, 211111HGGGGGGGGG/.....,,,,,,SRRRRR B A A 4#S !8 ! # #! Jbi  #$$iA C $3$i#C*S/ JxDK%%%+++'(((C&C&)(C&L7++4-88)#)#98,+)#X7++4-88#?#?98,+#?L7++4-88%2%298,+%2P7++4-882+2+98,+2+j7++4-88282898,+28j7++4-882+2+98,+2+j7++4-88+dVn-DEE3+../'/'/.FE98,+/'d+++   444<>>>$000*@"2"2"233.99 D D:943 D"N"N"NOO*T5M::.99..:9;:PO.""N"N"NOO*T5M::.9933:9;:PO3$     ****Z*1' G G G F^ #+ +   (FFF+R+R   +R\555,eW%5662:rz":;; ~(EFFWh$7ff=MNN4-88)K)K98ONGF<;76)KX J J J5$-lK5PQQ#K#K#KLL4-88Y $:@PQQ ~(EFFFFGFRQ98MLRQ F25$-lK5PQQi[114-88 $$$""" 2:rz":;; ~(EFF*I*IGF<; 9821RQ*IZ5$-lK5PQQeW%5664-88 $$$""" 2:rz":;; ~(EFFIIGF<; 9876RQI<KKK@eW%566 ~(EFF2w//#=G=G0/GF76=G@+dVn-DEE """ %=%=%=>>::?> FE:(666r333$ % ( ( (/:  E 2 2 24GH--- gii!$5$5$7$79L#MNay))00*)  0DDDN333 P P P,,,$T:7I$JKKeeAhh/00+dVn-DEE""FE10LK"eeAhh/00+dVn-DEE 2 2FE10 2   EI$ $ $ $ N.--// %'AB   Uu%%%!!!  !        ( /////1B1B1D1DE.--//XX X&.--// :''GGG',',',T*FF3I,J,JKKFFLKF."   .99DD:9  D ? ? ?F2C DEE00FE08 A A A .0LM:T:7I$JKKCCLKC8w0A&BCC==DC=(ay))w0A&BCCDC*)(KKK0$!$!$!N/1a&1a&1ABB^#n4~EVPP CB P"w0A&BCC M "J0ST * +  1 } %  @   &&!DC"&w0A&BCC""DC""555 KLL##ML#$#I#I#IJJ-e}==.99#F#F:9>=KJ#FL#>#>#>??.99##:9@?#-e}==.99WW:9>=W8+eT];;4*?@@rxj>&ABB OOO6<6<CBA@<; 6NOO-t}7IBVWWeT]9I(JeW%566q!f--8B8B.-76XWPO8BveW%566Xx$8vv>NOONNPO76NDXx$8vv>NOOHHPOHJ74E*FGG---++.-HG+! O$    12---??.-???rH