{KgddlZddlZddlZddlmZmZddlmZddlm Z m Z m Z m Z ddl mZddlmZmZddlmZmZmZmZmZmZmZmZmZmZmZmZddlm Z dd l!m"Z"m#Z#dd l$m%Z%e&ejNZ(e(Dcgc] }| c}Z)e)ed ed edededededededededg z Z)dZ* ddZ+dZ,ejZj]de)e*dZ/egdej` ej`gfegdej` ej`gfegdej` ej`gfegdej` ej`gfeddgej` ddej`gfeddgej` dddej`gfedddgej` ej`gfedddgej` ej`gfedddgej` ddej`gfedddgej` dddej`gfedddgej` dddej`gfedddgej` ej`gfedgd ej` ej`gfedddgej` ddej`gfedddgej` dddej`gfedddgej` dddej`gfegd!ej` d"dej`gfegej` d"d#ej`gfgZ1edggfedggfedgd$gfedd%dggfeddggfedgd$gfedd%dggfeddggfeddggfegd&gfg Z2egdgfedggd'fedggd'fedgdgfedggd'fedgd'gfedgdgfegddgfedd(gddgfg Z3ejZj]d)e1e2zd*Z4ejZj]d+e1e3zd,Z5ejZj]d-ged.d/d0d1dfed.d/d2d.dfed( d.d/dd(dfed d.d/dd dfed d/d.d.d3dfed(d4d.d/d5ddfed(d4d.dd6ddfed6ejld1d1dejld1zz dd1fed6ejld1ejld1d(zd(d(fedd6ejld1d7ddfedd6d2ddejldzz ddfedd6d2ejldd(z ddfedd6d2d8ddfed ejld1ejnd1d ejld1zz ddfed9d:dddfed.d:d;d"dfed9dd?d"dfed.d@dAdBdCfed9d@gdCgdzed.dDdEdBdFfed9dDgdFgdzed9dGdAdHdCfed.dGgdIgdzed9d?d?ddJfed9dKdKddLfed.d;dddfed9d;d;ddfed.dKddMdLfed.dNdddfed.d=dddfed9d=d=ddfedOd9gdPegdPdQz ddfedOd.gdPegdPd(z ddfedOd6gdPegdPdRz ddfedOd6gdSegdSdTz ddfe*dUZ8ejZj]de(ejZj]dVdWdXgejZj]dYejrejtgejZj]dZejrejtgejZj]d[ddgejZj]d\ddgejZj]d]ddgejZj]d^ddgd_Z;ejZj]de)e*ejZj]d[dd`gdaZejZj]de)e*dfZ?ejZj]de)e*ejZj]d[dd`gdgZ@ejZj]de)e*ejZj]d[dd`gdhZAejZj]digdje#dkZBejZj]de)e*ejZj]d[dd`gdlZCejZj]dmeejdnfeejdnfed dodnfeejdpfeejdqfeejdqfeejdrfgdsZFdtZGduZHejZj]dvejgdwejgdxfejZj]dyejgdzejgd{fd|ZJejZj]de)e*d}ZKejZj]de(ejZj]d[dd`gejZj]d~ejrejtfejZj]dddZLejZj]de(ejZj]dd~ejidejdfgdZNejZj]deddieOdfeddiePdfedd#iePdfeddieOdfeddiePdfedd#iePdfgdZQejZj]de)e*dZRejZj]dgddZSycc}w)N)assert_allcloseassert_array_equal)approx)LinearConstraintminimizeminimize_scalarnewton) logsumexp) IdentityLink_inclusive_low_high) _LOSSES AbsoluteErrorBaseLossHalfBinomialLoss HalfGammaLossHalfMultinomialLossHalfPoissonLossHalfSquaredErrorHalfTweedieLossHalfTweedieLossIdentity HuberLoss PinballLoss)assert_all_finite)create_memmap_backed_data skip_if_32bit)_IS_WASMg?)quantileg?power@ct|tr|}|jj}t|tr|d|j j dz }|St|tr|d|j z }|St|dr3t|j dr|d|j jdz }|St|S)Nz (quantile=)clossr z(power=) isinstancer __class____name__rr&rrhasattrr str)paramlossnames a/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/sklearn/_loss/tests/test_loss.pyloss_instance_namer05s%"~~&& dK ( j!4!4 5Q7 7D  i ( j0 0D T7 # G(D gdjj../q1 1D 5zctjj|}|jrtj||j f}|j |d|d||j z|jddtj|jt|j z}||fSt|jtrPt|j\}} tj ||dg}tj"| |dg} || f}|j |d|d|}t|j$\}} t'||d}t)| |d} |j || |}|j$j*dk(r!|j$j,r d|dd|dz<|j$j.dk(r!|j$j0r d|dd|dz<||fS)z9Random generate y_true and raw_prediction in valid range.rr!)lowhighsizeNr5)nprandom RandomState is_multiclassempty n_classesuniformflatarangeastypefloatr'linkr r interval_y_predamaxamininterval_y_truemaxminr3 low_inclusiver4high_inclusive) r- n_samplesy_bound raw_boundseedrngraw_predictiony_truer3r4s r/random_y_true_raw_predictionrSDs ))   %C 9dnn"=>!$! 1T^^+"-" A 9%,,U3dnnD, > !!' dii .+D,@,@AIC''3 ! -.C77D)A,/0Dd I! 9Q !!r1ctj||}||d|zz }|||z }|||z}||d|zz}| d|zzd|zz |zd|zz S)z2Helper function for numerical (first) derivatives.) fill_valuer"(@)r8 full_like)funcxepsh f_minus_2h f_minus_1h f_plus_1h f_plus_2hs r/numerical_derivativeraisy Q3'Aa!a%iJa!eJQU IQQYI JY &Z 7* DPS TTr1r-)idsc|jr2d}tjtjd|dz |d}n0t |j \}}tj||d}|j j r)tj||j jf}|j jr)tj||j jf}|j|sJ|jd}t |j\}}|jrctj|f}tj||||dddf<dd|dddfz z|dddf<dd|dddfz z|dddf<ntj|||}|j|sJ|j j!|}|j#|| y) z4Test interval ranges of y_true and y_pred in losses.r7rr!num N?r"rRrQ)r;r8tilelinspacer rGrJr_r3rKr4in_y_true_rangeshaperDr<in_y_pred_rangerCr-)r-r=rRr3r4ny_predrQs r/test_loss_boundaryrqws  Q A 9EqI'(<(<= TS$B/ ))vt337778 **vt338889    '' ' QA#D$8$89IC 1i.){{3!4q!t a&A,./q!t a&A,./q!t S$A.    '' 'YY^^F+NIIVNI;r1)r皙?drsrt皙?r7)rurvrrsrt)rsrg?g?)rrrvrrr)?r")rurvrrgz!loss, y_true_success, y_true_failc|D])}|jtj|gr)J|D])}|jtj|gs)Jy)z-Test boundaries of y_true for loss functions.N)rlr8array)r-y_true_success y_true_failys r/test_loss_boundary_y_truerT ##BHHaSM222 ''! 666r1z!loss, y_pred_success, y_pred_failc|D])}|jtj|gr)J|D])}|jtj|gs)Jy)z-Test boundaries of y_pred for loss functions.N)rnr8r})r-y_pred_success y_pred_failrs r/test_loss_boundary_y_predrrr1zDloss, y_true, raw_prediction, loss_true, gradient_true, hessian_truer{g@rVg@gп)rdeltag@@gȿg?rzg@xg@xDg@g@@gBgB@gB%ggT`Grc|tj|gtj|g}|jtj|gtj|g}|jtj|gtj|g\}} |j tj|gtj|g\} } |t |ddk(sJ|t |ddk(sJ|9|t |ddk(sJ| t |ddk(sJ| t |ddk(sJ|| t |ddk(sJyy)z7Test losses, gradients and hessians at specific values.rhV瞯<)relabsN)r8r}gradient loss_gradientgradient_hessianr) r-rRrQ loss_true gradient_true hessian_trueloss1grad1loss2grad2grad3hesss r/test_loss_on_specific_valuesrsbx &*288^DT;U VE MMxx!"((N;K2L  E%%xx!"((N;K2L&LE5''xx!"((N;K2L(KE4 F9%U; ;; ; F9%U; ;; ; }%UCCCC}%UCCCC}%UCCCCvl5AAAA r1readonly_memmapFTdtype_in dtype_out sample_weightout1out2 n_threadsctr|rtjd|}d}t||ddd\} } | j |} | j |} |t j d g|z| }|t j| | }|t j| | }|r#t| } t| } | t|}|j| | ||| |j| | ||| |j| | |||| |#|jrt j| | }|j| | |||||| | ||j| ||j| |t!|dr|j#| t!|dr|j%| | ||||yy)a0Test acceptance of dtypes, readonly and writeable arrays in loss functions. Check that loss accepts if all input arrays are either all float32 or all float64, and all output arrays are either all float32 or all float64. Also check that input arrays can be readonly, e.g. memory mapped. zmemmap not fully supported)reasonrrrtrf*r-rLrMrNrONrdtype)rRrQrloss_outr)rRrQr gradient_outr)rRrQrrrr)rRrQrr hessian_outrrRrQrrRr predict_proba)rQgradient_proba)rRrQrr proba_outr)rpytestxfailrSrAr8r} empty_likerr-rrr;rfit_intercept_onlyconstant_to_optimal_zeror*rr) r-rrrrrrrrLrRrQs r/test_loss_dtyperws$O 89 6DI9   FN]]8 $F#**84N #!2(C  }}V95 }}^9=*622>B  $5mDMII%#  MM%#  %#  D..}}^9=%#  ~]S6G!!}!Mt_% .9t%& )'  'r1rangect|dddd\}}|dk(r2tjd|jd|jd }tj|}tj|}tj|}tj|}tj|}tj|} |j |||| |j j |||| ft|||j|||| |j j|||| t|||j j||||| |j j||||| t||t|||j||||| |j j||||| t||t|| y)z:Test that Python and Cython functions return same results.rrrrrr!rrdrRrQrrrRrQrrrRrQrrrrRrQrrrN) rSr8rjrmrr-r&rrrr) r-rrRrQout_l1out_l2out_g1out_g2out_h1out_h2s r/test_loss_same_as_C_functionsrs:   FN Av||AFLLOL ]]6 "F ]]6 "F ]]> *F ]]> *F ]]> *F ]]> *FII%#   JJOO%#  FF#MM%#   JJ%#  FF#JJ%#  JJ%# FF#FF#%#  JJ%#  FF#FF#r1cFt|ddd|\}}|dk(r2tjd|jd|jd}tj|}tj|}tj|}tj|}tj|} tj|} |j |||| } |j |||| } |j||||| \} }|j|||| | \}}t| | t| |tj| |sJt| |tj| |sJt| |t| |t| |tj| |sJt||tj||sJt|| tj|| sJt|d r|jsJtj|}tj|}|j|||||\}}t| |t| |t||ttj|dddyy)zTest that loss and gradient are the same across different functions. Also test that output arguments contain correct results. rrrrrr!rrdrrrrrrRrQrrraxisdy=)rtolN)rSr8rjrmrr-rrrrr shares_memoryr*r;rsum)r-rglobal_random_seedrRrQrrrrout_g3out_h3l1g1l2g2g3h3out_g4 out_probag4probas r/ test_loss_gradients_are_the_samer&s:   FN Av||AFLLOL ]]6 "F ]]6 "F ]]> *F ]]> *F ]]> *F ]]> *F %#  B %#  B   %#  FB " "%# #FBBr6"  B '' 'r6"  B '' 'BBr6"  B '' 'r6"  B '' 'r6"  B '' 't%&!!!!~.MM.1 '')' ( E F#By)u1-qu='r1onesr9c nd}t||dd|\}}|dk(r&tj|tj}nNtjj |}|j |jtj}t|j|||||j||d z|j||d \}}|j|||\} } t||z| |jst||z| nt||d d d fz| |j||d \}} |j|||\} } |jst||z| t| |z| y t||d d d fz| t| |d d d fz| y ) zTest sample weights in loss, gradients and hessians. Make sure that passing sample weights to loss, gradient and hessian computation methods is equivalent to multiplying by the weights. rtrrrr)rmrr6rN) rSr8rfloat64r9r:normalrArr-rr;r) r-rrrLrRrQrPlossesr losses_sw gradient_swhessian hessian_sws r/test_sample_weight_multipliesrwsI9   FNirzzB ii##$67 299"**E  )'   )))   ))%*FH "//%#0I{ F]*I6   =0+>=D#99;G--%.Hg #33%#4K   =0+>-/<=D#99;G-4"88*Er1ct|dddd\}}|jdk(r|dddf}t|j|||j||t|j |||j ||t|j |||j ||t|j |||j ||yy) z5Test that reshaped raw_prediction gives same results.rrrrrr!Nrh)rSndimrr-rrr)r-rRrQraw_prediction_2ds r/test_graceful_squeezingrs:   FNa*1d73 IIV4EI F IIVNI C     f=N  O   f^  L   MM8IM J MMM G    ! !@Q ! R  ! ! ! O  r1c|jstjgd}t|jt r{d}|j j}|j js||z}|j j}|j js||z }tj|||}|jj|}ntj|jjt }tj"|j|jftj$d t }tj$d|j&dd|jdz<|dk(r2tj(d|j*d|j*d }|j-||| }|j/|| }t1|| d d y)z~Test value of perfect predictions. Loss of y_pred = y_true plus constant_to_optimal_zero should sums up to zero. )rrvrrsr7rf绽|=rf)rmrUrNr!rrrdrr+=r)atolr)r;r8r}r'rCr rDr3rJr4rKclipinverser@r=rArBfullexpr?rjrmr-rr) r-rrQr[r3r4rR loss_value constant_terms r/test_loss_of_perfect_predictionrs   "<= dii .C&&**C''55Ci'',,D''66czWW^S$?N"">24>>*11%8>>4>>2r {  68VVBZ1t~~112 Av||AFLLOL %#J 11]2M JUGr1c" d}t|dd|\ dk(r2tjd jd jdj  \}}|j jk(sJ|j jk(sJj sV fd }t | d }t||d d fd}t | d } jryt|| d dytjD]w  fd}t | dd fd }t|dd f|d d  fd}t | dd fd } jrbt|dd f| d dyy)zTest gradients and hessians with numerical derivatives. Gradient should equal the numerical derivatives of the loss function. Hessians should equal the numerical derivatives of gradients. rrrrrr!rrdrc,j|SNrr-rZr-rrRs r/ loss_funcz6test_gradients_hessians_numerically..loss_func.s"99 + r1gư>)r[h㈵>rrrc,j|Srrrs r/ grad_funcz6test_gradients_hessians_numerically..grad_func8s"== +! r1c^j}||ddf<j|Sr)copyr-rZrawkr-rQrrRs r/rz6test_gradients_hessians_numerically..loss_funcMs=$))+AqD yy!#&"/!r1Ngh㈵>clj}||ddf<j|ddfSr)r rr s r/rz6test_gradients_hessians_numerically..grad_funcYsK$))+AqD }}!#&"/%Q$ r1) rSr8rjrmrr;rarapprox_hessianrr=) r-rrrLgr\r g_numericr h_numericr rQrRs `` @@@r/#test_gradients_hessians_numericallyrsI9   FN Av||AFLLOL  %# ! DAq 77n** ** * 77n** ** *    )NM 94e< )NM     Ayt% @t~~&A  -Yq!t8LRVWI AadGYT F  -Yq!t8LRVWI""!Q$EJ9'r1zloss, x0, y_true) ) squared_errorgr)rg@]@g?)rrzrz) binomial_lossrrs)rir)rrx) poisson_lossrWr{)rrzr)rg6g$@ctdtjgtjtj|gtj}dtjdtjffd }dtjdtjffd }dtjdtjffd }t ||||d d }j |j }tjj|t||d d tj|d dy)acTest that gradients are zero at the minimum of the loss. We check this on a single value/sample using Halley's method with the first and second order derivatives computed by the Loss instance. Note that methods of Loss instances operate on arrays while the newton root finder expects a scalar or a one-element array for this purpose. NrrrZreturncPj|jzS)zCompute loss plus constant term. The constant term is such that the minimum function value is zero, which is required by the Newton method. rhrR)r-rrZr-rRs r/rYztest_derivatives..funcs6 yy!  ) ) ) 89 9r1c*j|S)Nrhrrs r/fprimez test_derivatives..fprimes}}F1}==r1c0j|dS)Nrhr!)rrs r/fprime2z!test_derivatives..fprime2s$$F1$EaHHr1rtgHj>)x0rr!maxitertolrrrrhgƠ>) r r8r}rndarrayr ravelrrCrr)r-r"rRrYrr!optimums` ` r/test_derivativesr)js 4 4=t ,D XXvhbjj 1F 2$bjj )B9 9rzz9>"**>>I2::I"**I   G\\^FmmoGDII%%g.7DM151DMMMH!RVWr1c djs2jjtjddnGtj j tjjzdddd<dk(rtjd d j }fd }jst|d ddi}jtj|}|jtk(sJ|jjk(sJt!||t#|j$d k(|j't#ddk(yt)|tj*jdddidt-tj.djfdd}jtj0|df}|jjk(sJt!|t3||j$ddt3|j'dddy)zzTest that fit_intercept_only returns the argmin of the loss. Also test that the gradient is zero at the minimum. 2rrdrNrrrsr"rcjstj|}n6tjtj|j f}|S)N)rmrUrmr)r;r8rascontiguousarray broadcast_tor=)rZrQr-rLrrRs r/funz%test_loss_intercept_only..funs[!!WWI1EN11)T^^)DEN)'  r1gHz>r#rt)r$optionsrrg-q=)rgvIh%<=SLSQPr!)r$r2method constraintsrrrr%)r;rCrr8rjr@rArr=rrrrXrmtuplerrrrZrrzerosrrrir)r-rar1optgradrLrRs`` @@r/test_loss_intercept_onlyr<sI   ""2;;r1)#DE9%,,RZZ84>>Iss  C :  v]KA     cti5EF}}<<2'  ww%'!!!ww&,,&&&! VCEEt $$  fQE**  HHdnn &$(!T^^1D)Eq!L  }}771y!n5'  ww&,,&&&!355t%8a(!%8r1zloss, func, random_distrc0tj|dS)N)q)r8 percentile)rZs r/rAsr}}Q"/Er1poisson exponentialbinomialc^tjj|}|dk(r|jddd}nt ||d}|j |}t ||t|jj||k(sJ|jj|t||k(sJt|tr%t|jj|||jjrB|j|jj |j |}t ||jj"rC|j|jj$|j |}t |yy)zTest that fit_intercept_only returns the correct functional. We test the functional for specific, meaningful distributions, e.g. squared error estimates the expectation of a probability distribution. rDr!rgrtr6rN)r8r9r:rDgetattrrrrrCrr'r rrGrJfillr3rKr4)r-rY random_distrrPy_trainbaseline_predictions r/ test_specific_fit_intercept_onlyrKsf$ ))   2 3Cj ,,q#C,0+'#{+5111A)* &W )F"G GG G 99  0 1VDM5J JJ J$ % ))*=>@ST )) T))--."55W5E-. ** T))../"55W5E-.+r1c tjjd}d}t|}|j d|dzdj tj }|j|}|j|fk(sJtj||j}t|D]}||k(j||<t|tj|tjtj|z t|d d d f|jj|d d d ftjd tj d fD]Y}|j tj }|j|}|j|jk(sJt#|[y ) zATest that fit_intercept_only returns the mean functional for CCE.rrrr!rtr6rrNrfr.)r8r9r:rrandintrArrrmr8rrmeanrlogrCrr)rPr=r-rIrJpr s r/(test_multinomial_loss_fit_intercept_onlyrQsk ))   "CI  3Dkk!Y]k5<.alt_lossQs; EAIwwrvva"&&!h"77899r1cPd|zdz }| dtj||zzz SrY)r8rrZs r/ alt_gradientz>test_binomial_vs_alternative_formulation..alt_gradientUs. EAIrQH --..r1N)rrArrr)rRrp global_dtyper]r_bin_lossdatums r/(test_binomial_vs_alternative_formulationrc@sv":/  !H ]]< (F ]]< (F V E U vh&67 77 7%H%%u-|U/CDr1c d}t||dd|\}}t|drU|j|}|j||jfk(sJt j |dtdd k(sJt|d rd d t j|ft j|d ft j|t j|ffD]\}}|j||d || \}}|j||jfk(sJt j |dtdd k(sJt||j||d d y y )z2{{y$..9999vve!$qe(<<<<t%&  2==0 1 ]]> *D 1 ]]> *BMM.,I J  KD% ---"! .KD%;;9dnn"== ==66%a(F1%,@@ @@  !#1"&!%   'r1rorder)CFcd}|dk(rtj|}||}|j|||\}}|jr#|j|fk(sJ|jdk(slJ|j r:|j||j fk(sJ|j||j fk(s&J|j|fk(sJ|j|fk(sJ|j|k(sJ|j|k(sJ|dk(r1|jjsJ|jjsJy|jjsJ|jjsJy)zTest that init_gradient_and_hessian works as expected. passing sample_weight to a loss correctly influences the constant_hessian attribute, and consequently the shape of the hessian array. rrr)rLrrf)r!rgN) r8rinit_gradient_and_hessianconstant_hessianrmr;r=rflags c_contiguous f_contiguous)r-rrrfrLrrs r/test_init_gradient_and_hessiansros[I * m ,D667Hg  ~~)---}}$$$  ~~)T^^!<<<<}}DNN ;;;;}} ,,,}} ,,, >>U "" " ==E !! ! |~~****}}))))~~****}}))))r1zparams, err_msgz+Valid options for 'dtype' are .* Got dtype=z instead.c|}tjttf|5|jdddi|\}}dddy#1swYyxYw)zDTest that init_gradient_and_hessian raises errors for invalid input.matchrLrN)rraises ValueError TypeErrorrj)r-paramserr_msgrrs r/%test_init_gradient_and_hessian_raisesrysI 6D  I.g >:D::QQQ&Q' ? > >s A  Azloss, params, err_type, err_msgrz4quantile must be an instance of float, not NoneType.zquantile == 0, must be > 0.zquantile == 1.1, must be < 1.cltj||5|di|dddy#1swYyxYw)z/Test that loss raises errors for invalid input.rqNrs)rrt)r-rwerr_typerxs r/#test_loss_init_parameter_validationr|s(B xw / v 0 / /s *3cd}t||ddd\}}tj|}tj|}|||t |||k(sJy)z Test that losses can be pickled.rrrrrrhN)rSpickledumpsloadsr)r-rLrRrQ pickled_lossunpickled_losss r/test_loss_picklerspI9   FN<<%L\\,/N vn =f^DB  r1rP)rrr!rwr"r7ct|}t|}d}t||d\}}|jj |}|j |||j |z}|j |||j |z}t|||j||\} } |j||\} } t| || zt| || z|dz| zzy)zCTest for identical losses when only the link function is different.rrfr)r-rLrOrhr"N) rrrSrCrr-rrr) rPhalf_tweedie_loghalf_tweedie_identityrLrRrQrploss_log loss_identity gradient_log hessian_loggradient_identityhessian_identitys r/%test_tweedie_log_identity_consistencyrs2'Q/3!<I9 FN " " * *> :F $$n%11&9:H*..f/66v>?MHm,!1 A An!B!L++@*P*Pf+Q+''L&+<"<=V//&!)>N2NNr1)rrr)Tr~numpyr8r numpy.testingrrrscipy.optimizerrrr scipy.specialr sklearn._loss.linkr r sklearn._loss.lossr rrrrrrrrrrr sklearn.utilsrsklearn.utils._testingrrsklearn.utils.fixesrlistvalues ALL_LOSSESLOSS_INSTANCESr0rSramark parametrizerqinfY_COMMON_PARAMS Y_TRUE_PARAMS Y_PRED_PARAMSrrrOlog1prfloat32rrrrrrrrr)r<rNmedianrKrQrVr}rcreroint64ryrvrur|rrrs0r/rs  = $@    ,K( .'.." # %/0ZT$&Z0 t$!!!#!$!$!$#&    CE""J U5GH <I 1SzRVVGRVV+<=3#srvvgr4-HI1SzRVVGRq"&&+IJ1SzRVVGRq"&&+IJ2&c bffWbff4EF1%'>"&&"&&@QR3'#srvvgr45PQ1%SzRVVGRq"&&3QR1%SzRVVGRq"&&3QRBFF7B266*BCB"&&"c266 :;'2R [1#r241ay"-3!b)2&<1%ay"53'!b1!Q$M2.  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