8^jrx . dZddlZddlZddlZddlZddlZddlmZddl m Z ddl m Z ddl mZddlmZmZmZmZddlmZdd lmZdd lmZmZmZmZd Zej<gd j>Z ej<gd j>Z!ee jEZ#eddZ$ede$eddeddeddzeddeddzeddzeddeddzeddzgZ%e%Dcgc] }|e$k7s | c}Z&ejNjQde%dZ)dZ*ejNjQde&dZ+ejNjQde%dZ,ejNjQde%dZ-ejNjQde&dZ.ejNjQde&dZ/ejNjae1d ejNjQde&ejNjQd!ejdd"d#d$d%Z3ejNjQde%d&Z4ejNjQde%d'Z5ejNjQd(dd)gejNjQddegd*Z6ejNjQde%ejNjQd+e#ejne jpdejr,gd-Z:d.Z;d/Zd2Z?ejNjQde&d3Z@d4ZAejNjQde%d5ZBd6ZCd7ZDd8ZEejNjQde%d9ZFd:ZGejNjQd;d<ejd=ieId>feej ejf?d@dAeIdBfgdCZKdDZLdEZMejNjQdFdGdHgejNjQdIgdJdKZNejNjQdFdGdHgejNjQdIgdJdLZOejNjQdIgdMejNjQdNdOdPgdQZPejNjQdIgdMdRZQdSZRGdTdUeZSdVZTejNjQde%dWZUycc}w)Xz'Testing for Gaussian process regressionN) approx_fprime)clone)ConvergenceWarning)GaussianProcessRegressor)RBF DotProductExpSineSquared WhiteKernel)ConstantKernel) MiniSeqKernel)assert_allcloseassert_almost_equalassert_array_almost_equalassert_array_lessc2|tj|zS)N)npsin)xs r/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/sklearn/gaussian_process/tests/test_gpr.pyfr#s rvvay=)?g@g@g@g@g @)@g@g@g@g@rfixed length_scalelength_scale_boundsr)MbP?@@{Gz?Y@h㈵>)r$r#皙?kernelc.tjdkrtjdt |j t t}|jt d\}}t|tttj|dy)N#This test may fail on 32 bit Pythonr&T return_cov) sysmaxsizepytestxfailrfitXypredictrrdiag)r&gpry_predy_covs rtest_gpr_interpolationr:9sf {{e :; #& 1 5 5a ;CKKdK3MFE",rc td}gd}tjgd}t|j ||}|j |d\}}t ||dd jd tjt|z jt ||t tj|d y) Nr)baseline_similarity_bounds)ABC)r*Tr+ eval_gradientr@r-) r rarrayrr2r5rraveleyelenr6)r&r3r4r7r8r9s r!test_gpr_interpolation_structuredrIFs g >FA A "& 1 5 5a ;CKKdK3MFEq%a(..01rvvc!f~3E2L2L2N",rc"tjdkrtjdt |j t t}|j|jj|j|jkDsJy)Nr(r)r*) r.r/r0r1rr2r3r4log_marginal_likelihoodkernel_thetar&r7s rtest_lml_improvingrOUso {{e :; #& 1 5 5a ;C  & &s{{'8'8 9C  rc:t|jtt}t j |j jjtj}|j|dt|j j|dy)Nr*dtypeF) clone_kernel) rr2r3r4ronesrLrMshapefloat64rKr)r&r7 input_thetas rtest_lml_without_cloning_kernelr\jsh #& 1 5 5a ;C''#++++11DK %@ ));:rct|jtt}|j |j j d\}}tjtj|dk|j j |j jdddfk(z|j j |j jdddfk(zsJy)Nr*T-C6?rr@) rr2r3r4rKrLrMrallabsbounds)r&r7lml lml_gradients rtest_converged_to_local_maximumrdts #& 1 5 5a ;C33CKK4E4EtLC 66   $ ;;   2 21a4 8 8 : ;;   2 21a4 8 8 :  rct|jtt}|jj }t j|jjjj}d}||t j|dddfdf<t|dddf|jj|zt|jj|dddf|zy)Nr*g|=r@r) rr2r3r4rLrarfinforMrUmaxisfiniter)r&r7ramax_tinys rtest_solution_inside_boundsrks #& 1 5 5a ;C [[  F 88CKK%%++ , 0 0D D,0FBKKq!t % %q ()fQTlCKK$5$5$<=ckk''1)<=rz9https://github.com/scikit-learn/scikit-learn/issues/31366)raisesreasonrrB ct|}td|jD}|jd i||it |j t tj|jd\}}d}t|jjfd|}t||d|d z y) Nc3DK|]}|jds|yw)rN)endswith).0names r z$test_lml_gradient..s # n0M#s  r*TrC& .>c(j|dS)NF)rK)rMr7s rz#test_lml_gradient..sc11%?r)epsilonr^drtolatol) rnext get_params set_paramsrr2r3r4rKrMrcopyr )r&rlength_scale_param_name_rcrylml_gradient_approxr7s @rtest_lml_gradientrs6]F"#**,#F@0,?@ #& 1 5 5a ;C11&,,d1SOA|G' ? L"5DwQT}Urcpt|}|jtd\}}t|ddt |j j dkDrAttj|tj|j ddyttj|ddy)Nr*Tr+rr@) rr5r3rrHr&rMrr6exp)r&r7y_meanr9s r test_priorrs #& 1CKKdK3MFE1% 3::  q BGGENBFF6<<?,CQGBGGENAq1rct|jtt}|j t d\}}|j t d}t|tj|ddttj|tj|jz tj|dtj|jz dy)Nr*Tr+ir@) rr2r3r4r5X2sample_yrrmeanr6rgvar)r&r7rr9sampless rtest_sample_statisticsrs #& 1 5 5a ;CKKtK4MFEll2v&G 3Q7 ++-- wRWWU^//11 r optimizer fmin_l_bfgs_bct||}|jttt |j j dk(j|duusJy)zBTest that kernel parameters are unmodified when optimizer is None.r&rr-N)rr2r3r4boolrLrMr_)rr&r7s rtest_no_optimizerrsM #&I FCGGAqM ""c)..0 1i46G HH HrtargetrTchtjdkrtjdt |j t t}|jtd\}}|jtd\}}ttjtj||y)Nr(r)r*Tr+ return_std)r.r/r0r1rr2r3r4r5rrrsqrtr6)r&rr7rr9y_stds rtest_predict_cov_vs_stdrs} {{e :; #& 1 5 5a ;CKKtK4MFEKKtK4MFE/7rctjjd}|jddd}|dddfd|dddfzz}t ddg}t |j ||}tj|jjdtj|jjddzkDsJy) Nrr@)2rAr%rr*r) rrandom RandomStateuniformrrr2rrLrM)rngr3r4r&r7s rtest_anisotropic_kernelrs ))   "C B7#A !Q$#!Q$-A #s_F "& 1 5 5a ;C 66#++##A& '"&&1B1B11E*F*J JJ Jrcd\}}tjjd}|j||dzdz }tj|j dtjd|zj dz|j d|z}td d td g|zd g|z ztd dz}tj }tdD]|}t||dj||}|j|jj } | |tj"tj$j&z kDsJ| }~y)N)rArrAr@axisrBr%)scalesizerr!)r^r#rr$)r$$@) noise_levelnoise_level_boundsr)r&n_restarts_optimizer random_state)rrrrandnrsumnormalr?rr infrangerr2rKrLrMrffloat32eps) n_samples n_featuresrr3r4r&last_lmlrgprbs rtest_random_startsrsV"Iz ))   "C )Z(1,q0A q 1  &&Q-    # $ **3Y* / 0 sK 3UZ'k]Z=W$EFFwH %a %!5  #a) (()9)9:X 4 8 88888rcTtjt}tjt}t|z |z }t |}|j t |t |d}|j t t|jtd\}}||z|z}||z}|jtd\}} t||t|| |jtd\} } | |dzz} |jtd\} } t| | y)a Test normalization of the target values in GP Fitting non-normalizing GP on normalized y and fitting normalizing GP on unnormalized y should yield identical results. Note that, here, 'normalized y' refers to y that has been made zero mean and unit variance. r*Tr& normalize_yrr+rAN) rrr4stdrr2r3r5rr) r&rry_normr7gpr_normr8 y_pred_std y_pred_normy_pred_std_normrr9 y_cov_norms rtest_y_normalizationr sWWQZF FF1IE&jE !F #& 1CGGAv(v4HH LLARD9FJ e^f $Fe#J#+#3#3B4#3#H K , O4{{2${/HAu E1H E$$RD$9MAzz*rcFdtz}ddi}tdi|}t|d}|jt||j t d\}}tjgd}tjgd}t||d d t||d d y )a Here we test that, when normalize_y=True, our GP can produce a sensible fit to training data whose variance is significantly larger than unity. This test was made in response to issue #15612. GP predictions are verified against predictions that were made using GPy which, here, is treated as the 'gold standard'. Note that we only investigate the RBF kernel here, as that is what was used in the GPy implementation. The following code can be used to recreate the GPy data: -------------------------------------------------------------------------- import GPy kernel_gpy = GPy.kern.RBF(input_dim=1, lengthscale=1.) gpy = GPy.models.GPRegression(X, np.vstack(y_large), kernel_gpy) gpy.optimize() y_pred_gpy, y_var_gpy = gpy.predict(X2) y_pred_std_gpy = np.sqrt(y_var_gpy) -------------------------------------------------------------------------- rrTrr)gy(V.@gV],;g6}~CgHW -@gƠKQ@)g ؼG'@gsr@g{u>5?g ?g.[ ?gQ?rr{g333333?Nr~) r4rrr2r3r5rrrEr )y_large RBF_paramsr&r7r8r y_pred_gpyy_pred_std_gpys rtest_large_variance_yr3s21fG!#&J  : F "&d CCGGAwRD9FJKJ XXDNFJT: JTBrctjttdzfj}t d}t |dd}|j ttt |dd}|j t||jtd\}}|jtd\}}|jtd\}} |jtd\}} t||ddd ft||ddd fdz t|jd D]$} t||d | ft| | d | f&|jtd } |jtd } | jdk(sJ| jdk(sJt| | ddd ddftD]}t |d}|j ttt |d}|j ttjttfjt|jj |jj dy)NrArrF)r&rrTrr+rr@.rr)rr)rrArr)rvstackr4Trrr2r3r5rrrrYrkernelsrLrM)y_2dr&r7gpr_2d y_pred_1dy_std_1d y_pred_2dy_std_2dry_cov_1dy_cov_2dr y_sample_1d y_sample_2ds rtest_y_multioutputrjs 99aQZ " "Dc "F "&De TCGGAqM %VtQV WF JJq$++bT+:Ix ...=Ix++bT+2KAx...5KAx 9QT?3 9QT?Q#67 1 &=HhsF{&;<HhsF{&;<=,,rR,0K//"/3K    '' '    ** * [Aq%9:H&f$G 1 )TJ 1biiA'))*CKK--v~~/C/CQGHrcd}t||}|jtt|j |j j |j |jj kDsJy)Nc ^tjjd}|||d}}tdD]q}tj|j tj d|dddftjd|dddf}||d}||ksn||}}s||fS)NrFrCrr@)rrrr atleast_1drmaximumminimum) obj_func initial_thetarar theta_optfunc_minrrMrs rrz(test_custom_optimizer..optimizersii##A&  ]% 8 r /AMM BJJr6!Q$<8"**QqRSt :UVEe4A8|&+Q8  /(""rr)rr2r3r4rKrLrMr&)r&rr7s rtest_custom_optimizerrsc # #&I FCGGAqM  & &s{{'8'8 9Ccft|d}t|d}tjttdf}tjt t ddzf}|j ||tjttddzf}tjt t ddzf}|j ||tjddddddf}|j|d \}}|j|d \}} t||t|| y) Nr"rrr@gV瞯$>vRV$>$W!NM n5 ]3rcftddtddz}t}|jtd\}}|jtd\}}t|}|jtd\}}|jtd\}}t ||t ||y) Nrr)constant_value_boundsrTrr+r*)r?rrr5r3r)default_kernelgpr1ry_std1y_cov1gpr2y_std2y_cov2s rtest_no_fit_default_predictrss':S >N $ %D Q4 0IAv Q4 0IAv #> :D Q4 0IAv Q4 0IAvff-ff-rc8tddg}t|}d}tjt|5|j t tdddtddgtdd gz}t|}tjd 5}tjd |j t tt|d k(sJt|djtsJ|djj ddk(sJt|djtsJ|djj ddk(sJ dddt#j$t d }tddgddg}t|}tjd 5}tjd |j |tt|d k(sJt|djtsJ|djj ddk(sJt|djtsJ|djj ddk(sJ dddy#1swY%xYw#1swY*xYw#1swYyxYw)Nr$rr r*zThe optimal value found for dimension 0 of parameter length_scale is close to the specified upper bound 0.001. Increasing the bound and calling fit again may find a better value.rrr gj@T)recordalwaysrArzThe optimal value found for dimension 0 of parameter k1__noise_level is close to the specified upper bound 0.001. Increasing the bound and calling fit again may find a better value.r@zThe optimal value found for dimension 0 of parameter k2__length_scale is close to the specified lower bound 1000.0. Decreasing the bound and calling fit again may find a better value.rrrr#rzThe optimal value found for dimension 0 of parameter length_scale is close to the specified lower bound 10.0. Decreasing the bound and calling fit again may find a better value.zThe optimal value found for dimension 1 of parameter length_scale is close to the specified lower bound 10.0. Decreasing the bound and calling fit again may find a better value.)rrr0warnsrr2r3r4r warningscatch_warnings simplefilterrH issubclasscategoryrargsrtile) r&r7warning_message kernel_sumgpr_sumrX_tile kernel_dimsgpr_dimss rtest_warning_boundsr&s dD\ 2F "& 1C  ( @ 1 t = #JAJ'j9G   - h' Aq6{a&),,.@AAA 1I   " "1 %*1 1  1&),,.@AAA 1I   " "1 %*1 1  1% 4WWQ]FC:C:NK'{;H   - h' VQ6{a&),,.@AAA 1I   " "1 %*1 1  1&),,.@AAA 1I   " "1 %*1 1  1%  K  <  s%I6CJ0Bsn2::6J "&d CCGGAz   v}}S1 11 1KKdK3MFEFJ'BGGENCd3771:qyI ))   "C  JJYNJ + GG9a.Q 7   AGGAqMKKdK3MFEF1a4L!$BGGE&M*Cd; <gsK}m ,s  & 0%&F #&T JChh #J  % #  +   $ * %   Ghh     O     O   GGGGW XX + &  % * %  $  FV5JE3V5JE3CS!12>rzparams, TypeError, err_msgrrzzCalpha must be a scalar or an array with same number of entries as yrrA)r&rz#requires that all bounds are finitectdi|}tj||5|jtt dddy#1swYyxYw)z0Check that expected error are raised during fit.rNr~)rr0rlr2r3r4)params TypeErrorerr_msgr7s rtest_gpr_fit_errorrCsA( # ,V ,C y 0 1 s AActtjtt}d}t j t|5|jddddy#1swYyxYw)z7Check that we raise the proper error in the LML method.r*z.Gradient can only be evaluated for theta!=NonerTrCN) rrr2r3r4r0rl ValueErrorrKr7rBs rtest_gpr_lml_errorrGsT "#% 0 4 4Q :C>G z 18 ##$#7888s A&&A/cttjtt}d}t j t|5|jtdddddy#1swYyxYw)z4Check that we raise the proper error during predict.r*z9At most one of return_std or return_cov can be requested.rTr,rN) rrr2r3r4r0rl RuntimeErrorr5rFs rtest_gpr_predict_errorrKsU "#% 0 4 4Q :CIG |7 39 A$4 8999s A,,A5rTFr3)Nr@rctjjd}d\}}}|f}|||fz}|f}| |dkDr||fz}|j||}|j||} |j|} t |} | j || | j | d\} } | j | d\}}| j|k(sJ| j|k(sJ|j|f|zk(sJy) aCheck the shapes of y_mean, y_std, and y_cov in single-output (n_targets=None) and multi-output settings, including the edge case when n_targets=1, where the sklearn convention is to squeeze the predictions. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/17394 https://github.com/scikit-learn/scikit-learn/issues/18065 https://github.com/scikit-learn/scikit-learn/issues/22174 )r rWNr@rTrr+)rrrrrr2r5rY)rr3rrn_samples_trainn_samples_test y_train_shape y_test_shaper9rr:modelr8rrr9s rtest_predict_shapesrUs ))   %C29/J$&M% 4 #$LQ#yl2 ii4G YY~z 2Fcii'G $ =E IIgwMM&TM:MFE}}V}5HAu <<< '' ' ;;, && & ;;>+l: :: :rcvtjjd}d\}}d}d}|f}|||fz}| |dkDr|||f}n||f}|j||} |j||} |j|} t |} | j | | | j | |} | j|k(sJy) a&Check the shapes of y_samples in single-output (n_targets=0) and multi-output settings, including the edge case when n_targets=1, where the sklearn convention is to squeeze the predictions. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/22175 rM)rrNrWrNr@rOr)rrrrrr2rrY)rr3rrrPn_samples_X_testn_samples_y_testrRrSr9rr:rT y_sampless rtest_sample_y_shapesrZs ))   %C"&J$&M% 4 Q()5EF (*:; ii4G YY' 4Fcii'G $ =E IIgwv1ABI ??l ** *r)Nr@rArBrr@rcPtjjd}|jdd}|jd||nd}t |}|j ||j }|j|||j ||j }||k(sJy)zJCheck the output shape of `sample_y` is consistent before and after `fit`.rrBNr@r3r)rrrrrrrYr2)r3rrr3r4rTshape_before_fitshape_after_fits rtest_sample_y_shape_with_priorr`s ))   %C "aA "9#8ia@A $y 9E~~a9~=CC IIaOnnQ)n<BBO  .. .rc$tjjd}d}|j|d}|j|||nd}t |}|j |d\}}|j |d \}} |j |||j |d\} } |j |d \}} |j| jk(sJ|j| jk(sJ| j| jk(sJy) zIAFA $y 9E!MM!M=J ==t=4LAy IIaO--d-;Ix--d-3KAx   y .. . ??hnn ,, , ??hnn ,, ,rc&tjjd}|jdd}|jdd}t d}t j td5|j||d d d y #1swYy xYw) zmCheck that an error is raised when the number of targets seen at fit is inconsistent with n_targets. rrrBrAr@r]z!The number of targets seen in `y`rN) rrrrrr0rlrEr2)rr3r4rTs rtest_n_targets_errorrk=ss ))   "C "aA "aA $q 1E z)L M !Qs +BBceZdZdZdZy) CustomKernelz A custom kernel that has a diag method that returns the first column of the input matrix X. This is a helper for the test to check that the input matrix X is not mutated. c|dddfS)Nrr~)selfr3s rr6zCustomKernel.diagQsAwrN)__name__ __module__ __qualname____doc__r6r~rrrmrmJs  rrmcttjtt}t j t}|jtd\}}tt|y)z Check that the input X is not modified by the predict method of the GaussianProcessRegressor when setting return_std=True. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/24340 r*TrN) rrmr2r3r4rrrr5r )r7X2_copyrs r#test_gpr_predict_input_not_modifiedrvUsJ #,. 9 = =a CCggbkG ;;rd; +DAqB rct|jtt}|j tdd}t |ty)z\ Check that only y_mean is returned when return_cov=False and return_std=False. r*FrIN)rr2r3r4r5r )r&r7r8s r%test_gpr_predict_no_cov_no_std_returnrxes9 #& 1 5 5a ;C [[u[ ?FFAr)Vrsrr.rnumpyrr0scipy.optimizer sklearn.basersklearn.exceptionsrsklearn.gaussian_processr sklearn.gaussian_process.kernelsrrr r r r?4sklearn.gaussian_process.tests._mini_sequence_kernelr sklearn.utils._testingr rrrr atleast_2drr3rrFr4 fixed_kernelrnon_fixed_kernelsmark parametrizer:rIrOrRr\rdrkr1AssertionErrorlogspacerrrrrXrYrZrrrrrrrrrrr&r,r6r>zerosrErrCrGrKrUrZr`rirkrmrvrxr*s0rrs-   (1= OBMM0133R]],-//aDJJLA SSk:c;#3KPPc;#3KPPkc;#3KPPk +2LV|5KVL7+ -, - -#4567+,7+;,;#45 6 #45 >6 >  F#45RB)?@VA6  V*7+ 2, 27+,"t_&=>D#%=1I2?I7+Awrwwqwwqz'L#MN8O,8 K67+$+,$+N4Cn+H\#4564 7+4,4*."F R 67+%<,%