qtjrx dZddlZddlZddlZddlZddlZddlmZddl m Z ddl m Z ddl mZddlmZmZmZmZddlmZdd lmZdd lmZmZmZmZd Zejgd jZ ejgd jZ!ee "Z#eddZ$ede$eddeddeddzeddeddzeddzeddeddzeddzgZ%de%DZ&ej'(de%dZ)dZ*ej'(de&dZ+ej'(de%dZ,ej'(de%dZ-ej'(de&dZ.ej'(de&dZ/ej'0e1d !ej'(de&ej'(d"ej2d#d$d%d&Z3ej'(de%d'Z4ej'(de%d(Z5ej'(d)dd*gej'(ddegd+Z6ej'(de%ej'(d,e#ej7e j8dej9-gd.Z:d/Z;d0Zd3Z?ej'(de&d4Z@d5ZAej'(de%d6ZBd7ZCd8ZDd9ZEej'(de%d:ZFd;ZGej'(dieId?feejJ ejJf@dAdBeIdCfgdDZKdEZLdFZMej'(dGdHdIgej'(dJgdKdLZNej'(dGdHdIgej'(dJgdKdMZOej'(dJgdNej'(dOdPdQgdRZPej'(dJgdNdSZQdTZRGdUdVeZSdWZTej'(de%dXZUdS)Yz'Testing for Gaussian process regressionN) approx_fprime)clone)ConvergenceWarning)GaussianProcessRegressor)RBF DotProductExpSineSquared WhiteKernel)ConstantKernel) MiniSeqKernel)assert_allcloseassert_almost_equalassert_array_almost_equalassert_array_lessc0|tj|zS)N)npsin)xs e/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/gaussian_process/tests/test_gpr.pyfr#s rvayy=)?g@g@g@g@g @)@g@g@g@g@rfixed length_scalelength_scale_boundsr)MbP?@@{Gz?Y@h㈵>)r$r#皙?c(g|]}|tk |S) fixed_kernel).0kernels r r+6s"LLLV|5K5KV5K5K5Krr*cVtjdkrtjdt |t t}|t d\}}t|tttj |ddS)N#This test may fail on 32 bit Pythonr*T return_cov) sysmaxsizepytestxfailrfitXypredictrrdiag)r*gpry_predy_covs rtest_gpr_interpolationr?9s {e :;;; #& 1 1 1 5 5a ; ;CKKdK33MFE""",,,,,rc td}gd}tjgd}t|||}||d\}}t ||dd d tjt|z t ||t tj |d dS) Nr)baseline_similarity_bounds)ABC)r/Tr0 eval_gradientrEr2) r rarrayrr7r:rraveleyelenr;)r*r8r9r<r=r>s r!test_gpr_interpolation_structuredrNFs g > > >FA A "& 1 1 1 5 5a ; ;CKKdK33MFEq%%%a(..001rvc!ff~~3E2L2L2N2N""",,,,,rc$tjdkrtjdt |t t}||j j ||j ksJdS)Nr-r.r/) r3r4r5r6rr7r8r9log_marginal_likelihoodkernel_thetar*r<s rtest_lml_improvingrTUs {e :;;; #& 1 1 1 5 5a ; ;C  & &s{'8 9 9C>      rc$t|tt}t j|jjjtj }| |dt|jj|ddS)Nr/dtypeF) clone_kernel) rr7r8r9ronesrQrRshapefloat64rPr)r*r< input_thetas rtest_lml_without_cloning_kernelrajsx #& 1 1 1 5 5a ; ;C'#++1DDDK %@@@ );:::::rct|tt}||jjd\}}tjtj |dk|jj|jj dddfkz|jj|jj dddfkzsJdS)Nr/T-C6?rrE) rr7r8r9rPrQrRrallabsbounds)r*r<lml lml_gradients rtest_converged_to_local_maximumrits #& 1 1 1 5 5a ; ;C33CK4EtLLC 6    $ ;  2111a4 8 8 : ;  2111a4 8 8 :   rct|tt}|jj}t j|jjj j }d}||t j |dddfdf<t|dddf|jj|zt|jj|dddf|zdS)Nr/g|=rEr) rr7r8r9rQrfrfinforRrZmaxisfiniter)r*r<rfmax_tinys rtest_solution_inside_boundsrps #& 1 1 1 5 5a ; ;C [ F 8CK%+ , , 0D D,0FBKqqq!t % % %q ()fQQQTlCK$5$<===ck'1)<=====rz9https://github.com/scikit-learn/scikit-learn/issues/31366)raisesreasonrrG ct|}td|D}|jd i||it |t t|j d\}}d}t|j fd|}t||d|d z dS) Nc3DK|]}|d|VdS)rN)endswith)r)names r z$test_lml_gradient..sG## n0M0M# ######rr/TrH& .>c0|dS)NF)rP)rRr<s rz#test_lml_gradient..sc11%??r)epsilonrcdrtolatolr') rnext get_params set_paramsrr7r8r9rPrRrcopyr )r*rlength_scale_param_name_rhr}lml_gradient_approxr<s @rtest_lml_gradientrs6]]F"##**,,###F@@0,?@@@ #& 1 1 1 5 5a ; ;C11&,d1SSOA|G' ???? L"5DwQT}UUUUUUrct|}|td\}}t|ddt |jjdkrBttj|tj |jdddSttj|dddS)Nr/Tr0rrE) rr:r8rrMr*rRrr;exp)r*r<y_meanr>s r test_priorrs #& 1 1 1CKKdK33MFE1%%% 3: q  BGENNBF6<?,C,CQGGGGGBGENNAq11111rc&t|tt}|t d\}}|t d}t|tj |ddttj |tj | z tj |dtj | z ddS)Nr/Tr0irE) rr7r8r9r:X2sample_yrrmeanr;rlvar)r*r<rr>sampless rtest_sample_statisticsrs #& 1 1 1 5 5a ; ;CKKtK44MFEll2v&&G 3 3Q777 ++--- wRWU^^//111 r optimizer fmin_l_bfgs_bct||}|ttt |jjdk|duusJdS)zBTest that kernel parameters are unmodified when optimizer is None.r*rr2N)rr7r8r9boolrQrRrd)rr*r<s rtest_no_optimizerrsa #&I F F FCGGAqMMM "c)..00 1 1i46G H H H H H HrtargetrYctjdkrtjdt |t t}|td\}}|td\}}ttj tj ||dS)Nr-r.r/Tr0 return_std)r3r4r5r6rr7r8r9r:rrrsqrtr;)r*rr<rr>y_stds rtest_predict_cov_vs_stdrs {e :;;; #& 1 1 1 5 5a ; ;CKKtK44MFEKKtK44MFE//77777rctjd}|ddd}|dddfd|dddfzz}t ddg}t |||}tj|jj dtj|jj ddzksJdS) NrrE)2rFr%rr/r) rrandom RandomStateuniformrrr7rrQrR)rngr8r9r*r<s rtest_anisotropic_kernelrs )   " "C B7##A !!!Q$#!!!Q$-A #s__F "& 1 1 1 5 5a ; ;C 6#+#A& ' '"&1B11E*F*F*J J J J J J Jrcd\}}tjd}|||dzdz }tj|dtjd|zdz|d|z}td d td g|zd g|z ztd dz}tj }tdD]u}t||d ||}||jj} | |tjtjjz ksJ| }vdS)N)rFrrFrEaxisrGr%)scalesizerr!)rcr#rr$)r$$@) noise_levelnoise_level_boundsr)r*n_restarts_optimizer random_state)rrrrandnrsumnormalrDrr infrangerr7rPrQrRrkfloat32eps) n_samples n_featuresrr8r9r*last_lmlrgprgs rtest_random_startsrs"Iz )   " "C )Z((1,q0A q 1  &Q--    # # $ **3Y* / / 0 sK 3UZ'k]Z=W$$$EEEFFwH %a %!5    #a)) (()9::X 4 4 888888rctjt}tjt}t|z |z }t |}|t |t |d}|t t|td\}}||z|z}||z}|td\}} t||t|| |td\} } | |dzz} |td\} } t| | dS)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_yrr0rFN) rrr9stdrr7r8r:rr) r*rry_normr<gpr_normr= y_pred_std y_pred_normy_pred_std_normrr> y_cov_norms rtest_y_normalizationr sDWQZZF F1IIE&jE !F #& 1 1 1CGGAv(v4HHHH LLARD99FJ e^f $Fe#J#+#3#3B4#3#H#H K ,,, O444{{2${//HAu E1H E$$RD$99MAzz*****rctdtz}ddi}tdi|}t|d}|t||t d\}}tjgd}tjgd}t||d d t||d d d S)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?rrg333333?Nr') r9rrr7r8r:rrrJr )y_large RBF_paramsr*r<r=r y_pred_gpyy_pred_std_gpys rtest_large_variance_yr3s21fG!#&J   :  F "&d C C CCGGAwRD99FJKKKJ XDDDNFJT:::: JTBBBBBBrcptjttdzfj}t d}t |dd}|ttt |dd}|t||td\}}|td\}}|td\}} |td\}} t||ddd ft||ddd fdz t|j d D]2} t||d | ft| | d | f3| td } | td } | j dksJ| j dksJt| | ddd ddftD]}t |d}|ttt |d}|ttjttfjt|jj|jjddS)NrFrrF)r*rrTrr0rrE.rr)rr)rrFrr)rvstackr9Trrr7r8r:rrrr^rkernelsrQrR)y_2dr*r<gpr_2d y_pred_1dy_std_1d y_pred_2dy_std_2dry_cov_1dy_cov_2dr y_sample_1d y_sample_2ds rtest_y_multioutputrjs 9aQZ "Dc " " "F "&De T T TCGGAqMMM %VtQV W W WF JJq$++bT+::Ix ...==Ix++bT+22KAx...55KAx 9QQQT?333 9QQQT?Q#6777 1 &&==HhsF{&;<<<HhsF{&;<<<<,,rR,00K//"/33K   ' ' ' '   * * * * [Aqqq%9:::HH&f$GGG 1 )TJJJ 1biA'')***CK-v~/CQGGGGHHrcd}t||}|tt||jj||jjksJdS)Nc |tjd}|||d}}tdD]{}tj|tjd|dddftjd|dddf}||d}||kr||}}|||fS)NrFrHrrE)rrrr atleast_1drmaximumminimum) obj_func initial_thetarfr theta_optfunc_minrrRrs rrz(test_custom_optimizer..optimizersi##A&&  H]% 8 8 8 r / /AM BJr6!!!Q$<88"*QqqqRSt :U:UVVEe444A8||&+Q8 (""rr)rr7r8r9rPrQrRr*)r*rr<s rtest_custom_optimizerrs # # # #&I F F FCGGAqMMM  & &s{'8 9 9C$>vRV$>$W$W!NM n555 ]33333rctddtddz}t}|td\}}|td\}}t|}|td\}}|td\}}t ||t ||dS) Nrr)constant_value_boundsrTrr0r/)rDrrr:r8r)default_kernelgpr1ry_std1y_cov1gpr2y_std2y_cov2s rtest_no_fit_default_predictrss':::S >>>N $ % %D Q4 00IAv Q4 00IAv #> : : :D Q4 00IAv Q4 00IAvff---ff-----rctddg}t|}d}tjt|5|t tdddn #1swxYwYtddgtdd gz}t|}tj d 5}tj d |t tt|d ksJt|djtsJ|djjddksJt|djtsJ|djjddksJ dddn #1swxYwYt#jt d }tddgddg}t|}tj d 5}tj d ||tt|d ksJt|djtsJ|djjddksJt|djtsJ|djjddksJ ddddS#1swxYwYdS)Nr$rrr/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)recordalwaysrFrzThe 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.rEzThe 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.)rrr5warnsrr7r8r9r warningscatch_warnings simplefilterrM issubclasscategoryrargsrtile) r*r<warning_message kernel_sumgpr_sumrX_tile kernel_dimsgpr_dimss rtest_warning_boundsr)s dD\ 2 2 2F "& 1 1 1C  ( @ @ @ 1 t === #JAAAJ'j999G   - - - h''' Aq6{{a&),.@AAAAA 1I  "1 %*1 1 1 1 1&),.@AAAAA 1I  "1 %*1 1 1 1 1 1%               4WQ]]FC:C:NNNK'{;;;H   - - - h''' VQ6{{a&),.@AAAAA 1I  "1 %*1 1 1 1 1&),.@AAAAA 1I  "1 %*1 1 1 1 1 1%                  s8!A--A14A1C FF F8CK  KKcdtdz}tddd}||z}t|tt dS)Ng@gI@rrr)r periodicityperiodicity_boundsr/)rr rr7r8r9)k1k2r*s r%test_bound_check_fixed_hyperparameterr/2sf 3D))) )B cg   B"WFF+++//155555rctjtjdtj}t |d}|t||jtj dksJ| td\}}t||ttj |dd tjdd }}tj d}tj||d f tj|d fd gd }|t|| td\} } t| ddd fd ttj | ddd | j||fksJ| j|||fksJdS)aCheck that the std. dev. is affected to 1 when normalizing a constant feature. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/18318 NaN where affected to the target when scaling due to null std. dev. with constant target. rrYTrrr0r2rz)rrFrE)r)r^ fill_valuerN).rE)rr]r8r^r_rr7 _y_train_stdr5rVr:r r;rr concatenaterfull) r* y_constantr<r=r>r n_targetsrr9Y_predY_covs rtest_constant_targetr9>s2:666J "&d C C CCGGAz  v}S11 1 1 1 1KKdK33MFEFJ'''BGENNCd333371:qyI )   " "C  JJYNJ + + G9a.Q 7 7 7     AGGAqMMMKKdK33MFEF111a4L!$$$BGE&M**Cd;;;; >>>>>rzparams, TypeError, err_msgrr~zCalpha must be a scalar or an array with same number of entries as yrrF)r*rz#requires that all bounds are finitectdi|}tj||5|tt ddddS#1swxYwYdS)z0Check that expected error are raised during fit.rNr')rr5rqr7r8r9)params TypeErrorerr_msgr<s rtest_gpr_fit_errorrFs( # , ,V , ,C y 0 0 0 1 s!AAActttt}d}t jt|5|dddddS#1swxYwYdS)z7Check that we raise the proper error in the LML method.r/z.Gradient can only be evaluated for theta!=NonerTrHN) rrr7r8r9r5rq ValueErrorrPr<rEs rtest_gpr_lml_errorrJs "#%% 0 0 0 4 4Q : :C>G z 1 1 188 ##$#777888888888888888888sA<<BBc tttt}d}t jt|5|tddddddS#1swxYwYdS)z4Check that we raise the proper error during predict.r/z9At most one of return_std or return_cov can be requested.rTr1rN) rrr7r8r9r5rq RuntimeErrorr:rIs rtest_gpr_predict_errorrNs "#%% 0 0 0 4 4Q : :CIG |7 3 3 399 A$4 888999999999999999999sBB BrTFr6)NrErctjd}d\}}}|f}|||fz}|f}| |dkr||fz}|||}|||} |j|} t |} | || | | d\} } | | d\}}| j|ksJ| j|ksJ|j|f|zksJdS) aCheck the shapes of y_mean, y_std, and y_cov in single-output (n_targets=None) and multi-output settings, including the edge case when n_targets=1, where the sklearn convention is to squeeze the predictions. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/17394 https://github.com/scikit-learn/scikit-learn/issues/18065 https://github.com/scikit-learn/scikit-learn/issues/22174 )r r\NrErTrr0)rrrrrr7r:r^)rr6rrn_samples_trainn_samples_test y_train_shape y_test_shaper<rr=modelr=rrr>s rtest_predict_shapesrXs4 )   % %C29/J$&M% 4 #$LQ#yl2 ii44G YY~z 2 2Fci'G $ = = =E IIgwMM&TM::MFE}}V}55HAu << ' ' ' ' ;, & & & & ;>+l: : : : : : :rctjd}d\}}d}d}|f}|||fz}| |dkr|||f}n||f}|||} |||} |j|} t |} | | | | | |} | j|ksJdS) 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 rP)rrQr\rNrErRr)rrrrrr7rr^)rr6rrrSn_samples_X_testn_samples_y_testrUrVr<rr=rW y_sampless rtest_sample_y_shapesr]s )   % %C"&J$&M% 4 Q()5EF (*:; ii44G YY' 4 4Fci'G $ = = =E IIgwv1ABBI ?l * * * * * *r)NrErFrGrrErcptjd}|dd}|d||nd}t |}|||j}||||||j}||ksJdS)zJCheck the output shape of `sample_y` is consistent before and after `fit`.rrGNrEr6r)rrrrrrr^r7)r6rrr8r9rWshape_before_fitshape_after_fits rtest_sample_y_shape_with_priorrcs )   % %C "aA "9#8iia@@A $y 9 9 9E~~a9~==C IIaOOOnnQ)n<<BO  . . . . . .rc0tjd}d}||d}||||nd}t |}||d\}}||d \}} |||||d\} } ||d \}} |j| jksJ|j| jksJ| j| jksJdS) zIIAFFA $y 9 9 9E!MM!M==J ==t=44LAy IIaOOO--d-;;Ix--d-33KAx  y . . . . ?hn , , , , ?hn , , , , , ,rcPtjd}|dd}|dd}t d}t jtd5|||d d d d S#1swxYwYd S) zmCheck that an error is raised when the number of targets seen at fit is inconsistent with n_targets. rrrGrFrEr`z!The number of targets seen in `y`rN) rrrrrr5rqrHr7)rr8r9rWs rtest_n_targets_errorrn=s )   " "C "aA "aA $q 1 1 1E z)L M M M !Qs7BB"BceZdZdZdZdS) CustomKernelz A custom kernel that has a diag method that returns the first column of the input matrix X. This is a helper for the test to check that the input matrix X is not mutated. c|dddfS)Nrr')selfr8s rr;zCustomKernel.diagQsAwrN)__name__ __module__ __qualname____doc__r;r'rrrprpJs- rrpctttt}t jt}|td\}}tt|dS)z Check that the input X is not modified by the predict method of the GaussianProcessRegressor when setting return_std=True. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/24340 r/TrN) rrpr7r8r9rrrr:r )r<X2_copyrs r#test_gpr_predict_input_not_modifiedryUsd #,.. 9 9 9 = =a C CCgbkkG ;;rd; + +DAqB     rct|tt}|tdd}t |tdS)z\ Check that only y_mean is returned when return_cov=False and return_std=False. r/FrLN)rr7r8r9r:r )r*r<r=s r%test_gpr_predict_no_cov_no_std_returnr{esQ #& 1 1 1 5 5a ; ;C [[u[ ? ?FFAr)Vrvrr3rnumpyrr5scipy.optimizer sklearn.basersklearn.exceptionsrsklearn.gaussian_processr sklearn.gaussian_process.kernelsrrr r r rD4sklearn.gaussian_process.tests._mini_sequence_kernelr sklearn.utils._testingr rrrr atleast_2drr8rrKr9r(rnon_fixed_kernelsmark parametrizer?rNrTrWrarirpr6AssertionErrorlogspacerrrrr]r^r_rrrrrrrrr rr)r/r9rAzerosrHrrFrJrNrXr]rcrlrnrpryr{r'rrrs--   ((((((111111====== ONNNNNBM000113R],,,--/AaDDJJLLsAAA CSCSk:::Ac;##3KPPPPAc;##3KPPPPakAc;##3KPPPPak ML'LLL7++ - -,+ - - - -#455657++,+7++;;,+;#455  65 #455 > >65 >  F#455RB)?)?@@VVA@65  V*7++ 2 2,+ 27++,+"t_&=>>D##%%=11II21?>I7++Awrwqwqz'L'L'L#MNN88ON,+8 K K K67++$+$+,+$+N4C4C4Cn+H+H+H\#455654   7++44,+4*..."F F F R 6 6 67++%<%<,+%