-iRSSKrSSKrSSKrSSKrSSKJrJrJr SSK J r J r SSK J r SSKJr SSKJr SSKJr SSKJr SS KJr SS KJrJrJr SS KJr SS KJr S r Sr!Sr"Sr#\RHRKS\5S5r&\RHRKS/SQ5\RHRKSSS/5S55r'Sr(Sr)Sr*Sr+\RHRKS/SQ5S5r,Sr-Sr.S r/S!r0\RHRKS/SQ5S"5r1\RHRKS#/S$Q5S%5r2S&r3S'r4S(r5S)r6S*r7S+r8g),N) load_iris make_blobs make_circles)PCA KernelPCA)NotFittedError) Perceptron) rbf_kernel) GridSearchCV)Pipeline)StandardScaler)assert_allcloseassert_array_almost_equalassert_array_equal)CSR_CONTAINERS)_check_psd_eigenvaluesc [RRU5nURS5nURS5nSnSGH nSSSU4Hn[ U5(+n[ SXeUS 9nUR U5n URU5RU5n [[R"U 5[R"U 55 U RS :wdeURU5n U RS U RS :XdeU(dMURU 5n U RUR:XaMe GM g ) zNominal test for all solvers and all known kernels + a custom one It tests - that fit_transform is equivalent to fit+transform - that the shapes of transforms and inverse transforms are correct rcZU0:Xde[R"X5R5$)N)npminimumsum)xykwargss ^/var/www/html/venv/lib/python3.13/site-packages/sklearn/decomposition/tests/test_kernel_pca.py histogram"test_kernel_pca..histogram#s&||zz!##%%autodensearpack randomizedlinearrbfpolyr)kernel eigen_solverfit_inverse_transformrN)rrandom RandomState random_samplecallabler fit_transformfit transformrabssizeshapeinverse_transform) global_random_seedrngX_fitX_predr!r-r,invkpcaX_fit_transformedX_fit_transformed2X_pred_transformedX_pred2s r test_kernel_pcarEsI ))   2 3C   f %E   v &F& B  :Fv&&C&SVD!% 2 25 9 !%%!:!:5!A  %()2662D+E  %))Q. .."&!7 %++A.2C2I2I!2LL LLs001CD}} 4445;Br#c[SSSS9nSn[R"[US9 UR [ R RSS55 SSS5 g!,(df  g=f)zCheck that kPCA raises an error if the parameters are invalid Tests fitting inverse transform with a precomputed kernel raises a ValueError. T precomputed) n_componentsr.r,z6Cannot fit_inverse_transform with a precomputed kernelmatchN)rpytestraises ValueErrorr5rr0randn) estimatorerr_mss r "test_kernel_pca_invalid_parametersrRFsR tMIFF z 0 biioob"-. 1 0 0s 0A  A.c"[RRU5nURSS5n[ US9R U5nUR U5nUR5nSUSS2S4'UR U5n[XF5 g)zCheck robustness to mutations in the original training array Test that after fitting a kPCA model, it stays independent of any mutation of the values of the original data object by relying on an internal copy. rG) random_stateiNr) rr0r1randrr5r6copyr)r;stateXr@ transformed1X_copy transformed2s r $test_kernel_pca_consistent_transformr\Tsz II ! !"4 5E 2rA % ( , ,Q /D>>!$L VVXFAadG>>&)Ll9r#c [RRU5nURSS5nSnUHn[R"S5n[ S5H'n[ SXAS9nURU5SXVSS24'M) [U[R"USSS24S5RSS55 M g) zyTest that Kernel PCA produces deterministic output Tests that the same inputs and random state produce the same output. rG)r'r&)rr^r)rIr-rTrN) rr0r1rUzerosrangerr4rtilereshape)r;r<rXr-solver transformed_Xir@s r $test_kernel_pca_deterministic_outputrfgs ))   2 3C RA&L) rA!&SD"&"4"4Q"7":MQ$   rww}QT/BB'G'O'OPRTU'VW r# csr_containerc [RRU5nU"URS55nU"URS55nSHnSHn[ SUUSSS9nUR U5nUR U5RU5n [[R"U5[R"U 55 URU5n U RS URS :Xde[R"[5 URU 5 S S S 5 M M g !,(df  M=f) zTest that kPCA works on a sparse data input. Same test as ``test_kernel_pca except inverse_transform`` since it's not implemented for sparse matrices. rr)r%r'r()r)r*r+rFr)r,r-r.rTr/N)rr0r1r2rr4r5r6rr7r9rLrMrr:) rgr;r<r=r>r-r,r@rArBrCs r test_kernel_pca_sparserixs! ))   2 3C #++F3 4E 3,,V4 5F8 /F)&+ D!% 2 25 9 !%%!:!:5!A  %()2662D+E  "&!7 %++A.2C2I2I!2LL LL ~.&&'9:/.-09./.s D:: E rcr$ n_featuresrrGc [RRU5nURSU45nURSU45nUS:XaSOSn[ [R "[ X`S9RU5RU55[R "[X`S:waUOSS 9RU5RU555 g ) zTest that kPCA with linear kernel is equivalent to PCA for all solvers. KernelPCA with linear kernel should produce the same output as PCA. rrr'r)r-r&full) svd_solverN) rr0r1r2rr7rr5r6r)rcrjr;r<r=r>n_compss r test_kernel_pca_linear_kernelrps ))   2 3C   q*o .E   : /F X%a1G y6::5AKKFST  .?FV L SZ Yv  r#c$[RRS5nURS5nURS5nSHInSH@n[ XCS9nUR U5R U5RnUSU4:XaM@e MK g) zTest that `n_components` is correctly taken into account for projections For all solvers this tests that the output has the correct shape depending on the selected number of components. rrrr&r'r()r/rrrIr-rN)rr0r1r2rr5r6r9)r<r=r>r-cr@r9s r test_kernel_pca_n_componentsrus ))   "C   f %E   v &F9 A!GDHHUO--f5;;EQF? "? :r#cN[R"SS/SS/SS//5n[5nURU5nURS:Xde[SS9nURU5nURS:Xde[SSS9nURU5nURS:Xdeg ) zCheck that the ``remove_zero_eig`` parameter works correctly. Tests that the null-space (Zero) eigenvalues are removed when remove_zero_eig=True, whereas they are not by default. ?r/)rlrrrI)rlrT)rIremove_zero_eigN)rarrayrr4r9)rXr@Xts r test_remove_zero_eigr|s 9a.1a&1i.9:A ;D  A B 88v   ! $D  A B 88v   !T :D  A B 88v  r#c [R"SS/SS//5n[R"5 [R"S[ 5 [R "SS9 [SSSS 9nURU5RU5nURU5n[[R"U5[R"U55 S S S 5 S S S 5 g !,(df  N=f!,(df  g =f) zNon-regression test for issue #12141 (PR #12143) This test checks that fit().transform() returns the same result as fit_transform() in case of non-removed zero eigenvalue. r/rerrorwarn)allrFr&)rIryr-N) rrzwarningscatch_warnings simplefilterRuntimeWarningerrstaterr5r6r4rr7)r=kABs r test_leave_zero_eigrs HHq!fq!f% &E  " g~6 [[V $q%gVAe &&u-A&A %bffQi ;% # " % $ # "s$0C5"A1C$C5$ C2 .C55 Dc[RRU5nURS5nURS5nSGHn[ SUSS9R U5R U5n[ SUSSS9R [R"X"R55R [R"X2R55n[ SUSSS9R[R"X"R55n[ SUSSS9R [R"X"R55R [R"X"R55n[[R"U5[R"U55 [[R"U5[R"U55 GM g ) z?Test that kPCA works with a precomputed kernel, for all solversrrrrrrr-rTrH)r-r,rTN) rr0r1r2rr5r6dotTr4rr7) r;r<r=r>r-X_kpcaX_kpca2 X_kpca_train X_kpca_train2s r test_kernel_pca_precomputedrs^ ))   2 3C   f %E   v &F9 al C SZ Yv    ]QR Sww' ( Yrvvfgg. / ! LQ -ugg. /   ]QR Sww' ( YrvveWW- .  ""&&."&&/B!"&&"6}8MN;:r#cSS/SS//n[SUSSS9nURU5 SS /S S//n[SUSSS9nURU5 [URUR5 [URUR5 g ) zCheck that the kernel centerer works. Tests that a non symmetric precomputed kernel is actually accepted because the kernel centerer does its job correctly. r/rrl(rHr)r,r-rIrT iN)rr5r eigenvectors_ eigenvalues_)rcKr@Kckpca_cs r )test_kernel_pca_precomputed_non_symmetricrs Q!RA 6PQ D HHQK b'B7 B 6PQF JJrNt))6+?+?@t((&*=*=>r#c[SSSSS9up[SSS9n[S U4S [S S 94/5n[ S [ R "SS5-S9n[USUS9nURX5 URS:Xdeg)zCheck that kPCA works as expected in a grid search pipeline Test if we can do a grid-search to find parameters to separate circles with a perceptron model. 333333?皙?r n_samplesfactornoiserTr*rr,rI kernel_pcar rmax_iter@)kernel_pca__gammarlcv param_gridr/N) rrr r dictraranger r5 best_score_)rXrr@pipeliner grid_searchs r test_gridsearch_pipeliner:s #cA NDA E 2D,- jRS>T/UVWHryyQ/?(?@JxA*EKOOA  " "a '' 'r#c[SSSSS9up[SSS9n[S U4S [S S 94/5n[ [ R "S S 5S9n[USUS9n[USS9nURXa5 URS :Xdeg)zCheck that kPCA works as expected in a grid search pipeline (2) Test if we can do a grid-search to find parameters to separate circles with a perceptron model. This test uses a precomputed kernel. rrrrrrHrrrr rrr/)Perceptron__max_iterrlrr)gammaN) rrr r rrrr r r5r)rXrr@rrrX_kernels r $test_gridsearch_pipeline_precomputedrIs #cA NDA M :D,- jRS>T/UVWH299Q?;JxA*EK!3'HOOH  " "a '' 'r#c[SSSSS9up[SS9RX5RX5nUS:de[ S S S S S 9nUR U5n[SS9RXA5RXA5nUS:Xdeg)aCheck that kPCA projects in a space where nested circles are separable Tests that 2D nested circles become separable with a perceptron when projected in the first 2 kPCA using an RBF kernel, while raw samples are not directly separable in the original space. rrrrrrrg?r*rTr)r,rIr.rrwN)rr r5scorerr4)rXr train_scorer@rs r test_nested_circlesrYs #cA NDAa(,,Q288>K    1D D   "Fa(,,V7==fHK #  r#c SS/SS/SS//n[SSSS 9nURU5 URR5S:Xde[R "UR[ UR5:H5(deg ) zuCheck that ``_check_psd_eigenvalues`` is correctly called in kPCA Non-regression test for issue #12140 (PR #12145). rr/gw̫@g:0yE>rr)rT)r,rIr.N)rr5rminrrrrXr@s r test_kernel_conditioningrus Q(D!Ha=1A H1D QDHHQK    "a '' ' 66$##'=d>O>O'PP Q QQ Qr#c /SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/n[S US S 9n[R"[S S 9 UR U5 SSS5 [S USS 9nUS:Xa4[R"[S S 9 UR U5 SSS5 gUR U5 g!,(df  Ne=f!,(df  g=f)aCheck how KernelPCA works with non-PSD kernels depending on n_components Tests for all methods what happens with a non PSD gram matrix (this can happen in an isomap scenario, or with custom kernel functions, or maybe with ill-posed datasets). When ``n_component`` is large enough to capture a negative eigenvalue, an error should be raised. Otherwise, KernelPCA should run without error since the negative eigenvalues are not selected. )gQ@p= # @p= ף@rr ףp= (\))rgQ@ףp= rrQ(\@)rrg(\.@Q@rr333333&{G:7)rrr@333333 rRQ?)rrrrrrrr)rrrrrrrr)rrrrrrgq= ףp@#@)rrrrrrrg(\u5@rH)r,r-rIz*There are significant negative eigenvaluesrJNrr()rrLrMrNr5)rcrr@s r test_precomputed_kernel_not_psdrs <<;:::;= A MQ OD z)U V   W MQ OD ]] J  HHQK   % W V  sB0C0 B> CrI)rrGr^cSup[X-SSSS9up4USU2SS24X1S2SS24pe[USSS9RU5RU5n[US SS9RU5RU5n[ [ R "U5[ R "U55 [US SS9RU5RU5n [ [ R "U 5[ R "U55 g) zGCheck that 'dense' 'arpack' & 'randomized' solvers give similar results)idrrrrNr&rr'r()rrr5r6rrr7) rIn_trainn_testrX_r=r>ref_preda_predr_preds r #test_kernel_pca_solvers_equivalencers !OG #S1 DAhwhkNAhkN6 ,W1E U 6   ,XAF U 6  bffVnbffX.>? ,\J U 6  bffVnbffX.>?r#c[SSSS9tp[SSSSS 9nURU5nURU5n[R R X- 5[R R U5- S :deg ) zTest if the reconstruction is a good approximation. Note that in general it is not possible to get an arbitrarily good reconstruction because of kernel centering that does not preserve all the information of the original data. rrrrrjrTr^r*TgMbP?)rIr,r.alpha皙?N)rrr4r:rlinalgnorm)rXrr@X_trans X_reconsts r 0test_kernel_pca_inverse_transform_reconstructionrsx  CEA T D  #G&&w/I 99>>!- (299>>!+< )) ) <<> )) )&&uq'9:H&&uq'9:HH'r#c[R"S5 [SSS9up[R"SS9 [ SSS9R U5 SSS5 g!,(df  g=f) zCheck that KernelPCA works with pandas output when the solver is arpack. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/27579 pandasT)as_frame return_X_y)transform_outputrr'rsN)rL importorskiprsklearnconfig_contextrr4)rXrs r test_kernel_pca_pandas_outputr-sO  ! dt 4DA    :qx8FFqI ; : :s A A()9rnumpyrrLrsklearn.datasetsrrrsklearn.decompositionrrsklearn.exceptionsrsklearn.linear_modelr sklearn.metrics.pairwiser sklearn.model_selectionr sklearn.pipeliner sklearn.preprocessingr sklearn.utils._testingrrrsklearn.utils.fixesrsklearn.utils.validationrrErRr\rfmark parametrizerirprur|rrrrrrrrrrrrrrrrr#r rs @@0-+/0%0 /;+5\ /:&X".9";:";J#LM2w/0N0#$,<2#OL#LM?N?4 ( ( 8 R #LM0N0f5!@6!@HD""Y"C(8 Jr#