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!!!!O44 I0000////''''J I, , , ,6 6 6 69GGG G G G9HHH H H H ++++ 3T J J JJJJ  3        2222 &&&((((  'T/%:'&&&& J$#### ++++y*%%*)))) %%%% "!!! )4 @ @ @@@@ ////  VU V VO'))%.%6#%;&&MFE"-(7#% 3_UUUUUUU V V V Vc#nKt|}tVtVtVtVt V|dr t VtVttdVttddVtV|dr$tVtVtVtV|dstV|ds tV|d r t Vd |vr t&Vt(Vt*VdS) N multioutputTrXrdrYX_dtype multilabelrZmultioutput_only requires_fit class_weight)r?"check_classifier_data_not_an_arraycheck_classifiers_one_label*check_classifiers_one_label_sample_weightscheck_classifiers_classes'check_estimators_partial_fit_n_featurescheck_classifier_multioutputcheck_classifiers_trainr#check_classifiers_regression_target6check_classifiers_multilabel_representation_invariance2check_classifiers_multilabel_output_format_predict8check_classifiers_multilabel_output_format_predict_probaK|jj}t|}d|dvr6tjd||dt dS|dr/tjd|t dSt|D]}|Vt|rt|D]}|Vt|rt|D]}|Vt|drt|D]}|Vt|trt!|D]}|Vt#|rt%|D]}|Vt&V|dst(Vt*Vt,Vt.Vt0Vt2Vt4Vt6Vt8Vt:V|ds,t<Vt>Vt@V|d r tBV|d r tDVdSdS) N2darrayX_typesz8Can't test estimator {} which requires input of type {} _skip_testz2Explicit SKIP via _skip_test tag for estimator {}.rnon_deterministicrZ requires_yrequires_positive_X)#rgrhr?warningswarnformatrrrrrrr{r isinstancer rrr&check_parameters_default_constructible%check_methods_sample_order_invariancecheck_methods_subset_invariancecheck_fit2d_1samplecheck_fit2d_1featurecheck_get_params_invariancecheck_set_paramscheck_dict_unchangedcheck_dont_overwrite_parameterscheck_fit_idempotentcheck_fit_check_is_fittedcheck_n_features_in check_fit1dcheck_fit2d_predict1dcheck_requires_y_nonecheck_fit_non_negative)rrrchecks r_yield_all_checksr8s   'D i DY'' F M Md9o        L @ G G M M     y)) Y-i88  EKKKKI,Y77  EKKKKy+&&.y99  EKKKK)\**-i88  EKKKK9%%+I66  EKKKK 0000 # $.3333----   %%%%   ))))  ####  (!!!!####   (' ' ' ' !"%$$$$$$%%rct|rt|ts|jS|js |jjSdd|jD}d|jj|St|drMtd5tj ddt|cd d d S#1swxYwYd Sd S) aCreate pytest ids for checks. When `obj` is an estimator, this returns the pprint version of the estimator (with `print_changed_only=True`). When `obj` is a function, the name of the function is returned with its keyword arguments. `_get_check_estimator_ids` is designed to be used as the `id` in `pytest.mark.parametrize` where `check_estimator(..., generate_only=True)` is yielding estimators and checks. Parameters ---------- obj : estimator or function Items generated by `check_estimator`. Returns ------- id : str or None See Also -------- check_estimator ,c@g|]\}}d||S)z{}={})r).0kvs r z,_get_check_estimator_ids..s*SSSdaW^^Aq11SSSrz{}({})rT)print_changed_onlyz\sN)callablerrrhkeywordsfuncjoinitemsrr{r resubstr)objkwstrings r_get_check_estimator_idsr%qs00}}<#w'' < | %8$ $88SScl>P>P>R>RSSSTTsx0(;;;sL!!/ t 4 4 4 / /6%SXX.. / / / / / / / / / / / / / / / / / ///s5#C%%C),C)ct|dg}t|r\|dgdgfvrt|tr|t }n0t|t r|t }nt|tr|td}n|td}n|dgfvrst|t r/|d t d fd t d fg }no|d td fd tdfg }n@d|j d|}tj |tt||}|S)z)Construct Estimator instance if possible._required_parametersrbase_estimatorr random_stater9)C estimatorsest1皙?alphaest2)r,zCan't instantiate estimator z parameters )getattrlen issubclassrrrr rr!rrhrrrr@) Estimatorrequired_parametersrmsgs r_construct_instancer8s!)-CRHH % K=3C2D"E E E )_55 ?%I&6&8&899 I~66 ?%Iegg.. I77 ?%Il&B&B&BCC %I&81&=&=&=>> l^$5 5 5)^44 %I!'S)9)9)9 :VUQR^^^.wrappeds6 Q Q Q)<)E Q Q Q Q   r)r<rrrrhr)rrshould_be_skippedrIrHr:s` @@r _maybe_skiprKs !=Y N Nv  (25'(B(BV$$J 5\\      \ Nrct|tr |jjn|j}t |dpi}||vr d||fSdS)N _xfail_checksrT)Fz*placeholder reason that will never be used)rrrrhr?)rrrH xfail_checkss rr<r<s\)35'(B(BV$$Ji_===CL\!!\*--- > >rcddltdDrd}t|fd}jd|t S)aPytest specific decorator for parametrizing estimator checks. The `id` of each check is set to be a pprint version of the estimator and the name of the check with its keyword arguments. This allows to use `pytest -k` to specify which tests to run:: pytest test_check_estimators.py -k check_estimators_fit_returns_self Parameters ---------- estimators : list of estimators instances Estimators to generated checks for. .. versionchanged:: 0.24 Passing a class was deprecated in version 0.23, and support for classes was removed in 0.24. Pass an instance instead. .. versionadded:: 0.24 Returns ------- decorator : `pytest.mark.parametrize` See Also -------- check_estimator : Check if estimator adheres to scikit-learn conventions. Examples -------- >>> from sklearn.utils.estimator_checks import parametrize_with_checks >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.tree import DecisionTreeRegressor >>> @parametrize_with_checks([LogisticRegression(), ... DecisionTreeRegressor()]) ... def test_sklearn_compatible_estimator(estimator, check): ... check(estimator) rNc3@K|]}t|tVdSN)rtype)rests r z*parametrize_with_checks..s, 7 7S:c4 7 7 7 7 7 7ruPassing a class was deprecated in version 0.23 and isn't supported anymore from 0.24.Please pass an instance instead.c3KD]K}t|j}t|D]%}t||}t ||V&LdSrQ)rRrhrrrB)rrrr,r@s rchecks_generatorz1parametrize_with_checks..checks_generator's{# B BI ??+D*955 B Bt,,' 5&AAAAAA B B Brzestimator, check)ids)r@any TypeErrorr> parametrizer%)r,r7rWr@s` @rparametrize_with_checksr\sPMMM 7 7J 7 7 777 / nnBBBBBB ; " ",,..4L #  rFcVttrd}t|tjfd}|r |S|D]O\} |#t$r1}t jt|tYd}~Hd}~wwxYwdS)aCheck if estimator adheres to scikit-learn conventions. This function will run an extensive test-suite for input validation, shapes, etc, making sure that the estimator complies with `scikit-learn` conventions as detailed in :ref:`rolling_your_own_estimator`. Additional tests for classifiers, regressors, clustering or transformers will be run if the Estimator class inherits from the corresponding mixin from sklearn.base. Setting `generate_only=True` returns a generator that yields (estimator, check) tuples where the check can be called independently from each other, i.e. `check(estimator)`. This allows all checks to be run independently and report the checks that are failing. scikit-learn provides a pytest specific decorator, :func:`~sklearn.utils.estimator_checks.parametrize_with_checks`, making it easier to test multiple estimators. Parameters ---------- estimator : estimator object Estimator instance to check. .. versionadded:: 1.1 Passing a class was deprecated in version 0.23, and support for classes was removed in 0.24. generate_only : bool, default=False When `False`, checks are evaluated when `check_estimator` is called. When `True`, `check_estimator` returns a generator that yields (estimator, check) tuples. The check is run by calling `check(estimator)`. .. versionadded:: 0.22 Returns ------- checks_generator : generator Generator that yields (estimator, check) tuples. Returned when `generate_only=True`. See Also -------- parametrize_with_checks : Pytest specific decorator for parametrizing estimator checks. rUc3xKtD]&}t|}t|fV'dSrQ)rrKr)rrrs rrWz)check_estimator..checks_generatorlsU&y11 2 2E 511EWUD111 1 1 1 1 2 2rN) rrRrZrhr@rrr"r)r generate_onlyr7rWr exceptionrs` @rcheck_estimatorra3s^)T"" / nn  ?? #D222222 "!!!,,..;; 5 ; E)     ; ; ; M#i../ : : : : : : : : ;;;s A++ B&5'B!!B&ct=tdddddd\}}t|}||fatS)Nrr9@*) n_samples n_features n_informativebiasnoiser*)REGRESSION_DATASETrr, fit_transform)rrs r_regression_datasetrn}s]!    1    * *1 - -T rcR|}|jj}|dkr|dd|vr|dkr|dd|vr|j)|t d|j|d vr|d |d kr|d |d kr|d d|jjdkr|d d|dvr|d|dkr|dd|vr|dd|vr)|t d|jd|vr|dd|vr|dd|vr+|d s|d!|d"kr|d#$|d%krd&|_|d'kr|d#(t|d)rt |j d|_ t|d*rd&|_ |d+kr|d,-|d.krd|_ t|tr|d/t|tr|d&0|d1vr|d2|d3kr|d45d6d7g}||vr&t|d8r|d9:t|d;r|d9<|d=kr|d>?|d@kr2t t#dAkrdBndC}||D|t$vr|d&/|dEkr|dFG|dHkr|d&IdSdS)JNTSNEr  perplexityn_iterrrsmax_iterru) LinearSVR LinearSVCreNMFiDictionaryLearning lasso_lars)rutransform_algorithm MiniBatchNMFT)rufresh_restarts) MLPClassifier MLPRegressordMiniBatchDictionaryLearning n_resampling)r n_estimators)r max_trialsr)rn_init)r batch_sizeMLP)r MeanShift?) bandwidth TruncatedSVDr9 LassoLarsIC)noise_variance n_clustersn_best SelectFdr?r/TheilSenRegressor) n_components)r)HistGradientBoostingClassifierHistGradientBoostingRegressor)min_samples_leafDummyClassifier stratified)strategyRidgeCVRidgeClassifierCVcvrn_splits)r OneHotEncoderignore)handle_unknownQuantileRegressorz1.6.0highszinterior-point)solverSpectralEmbeddinggh㈵>) eigen_tolHDBSCAN min_samples)rrgrh set_paramsruminrrrr{rrmax_subpopulationrr.rr7r6rv)rparamsrloo_cvrs r_set_checking_parametersrs ! ! # #F   'D v~~***6dfnnA&&&V   )  #a1C*D*D E E E - - -  " - - - 5==  # . . . ' ' '  ", O O O   '> 9 9  "T B B B 4 4 4  # . . . 0 0 0  ! , , ,!,,,#a1G*H*HIIIv+++6A&&&vdooe&<&<+++ { s+++ ~"#  } C000y,''<"9#7;; y(##  {3''' """&) #)122- !,,,)[))" q!!! RRR a000     l333, -F 6gi66"""y*%%)a((( H555 """&-*@*@@@FVF+++ """!,,, """t,,, y+++++rc&eZdZdZdZddZdZdS) _NotAnArrayzvAn object that is convertible to an array. Parameters ---------- data : array-like The data. c8tj||_dSrQ)rasarraydata)selfrs r__init__z_NotAnArray.__init__sJt$$ rNc|jSrQ)r)rrfs r __array__z_NotAnArray.__array__s yrcj|jdkrdStd|j)Nmay_share_memoryTz%Don't want to call array_function {}!)rhrZr)rrtypesrFrGs r__array_function__z_NotAnArray.__array_function__s4 =. . .4?FFt}UUVVVrrQ)rhr __qualname____doc__rrrrrrrsU%%%WWWWWrrcJt|dd}t|dkS)zReturns True if estimator accepts pairwise metric. Parameters ---------- estimator : object Estimator object to test. 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Returns ------- out: iter(Matrices) In format['dok', 'lil', 'dia', 'bsr', 'csr', 'csc', 'coo', 'coo_64', 'csc_64', 'csr_64'] csr)doklildiabsrcsccoorint64coo_64)rr_64N)rcopyasformatrowastypecolindicesindptr)X_csr sparse_formatX_coors r_generate_sparse_matrixr/s# <5  C;; U^^M::::::: NN5 ! !E   ))EI   ))EI E/''' NN= ) )I$$W-- 8??7++e#Q&&&&& ''rrc)r2rfc  tj|}n #t$rt|dwxYw ddl}n#t $rtdwxYw||d}|dkr|dkr|jstd|d vr5ddl }|j j dkrtd td \} } | |d } t|| } t!|| } t#|} || |} || |} | | | dt'| D}t#| }t+d5|| | dddn #1swxYwY|D]\}}t-||}t |dt | dksJd|dt/|t/| ksJt1||}t3|||dt5j| jjdzd}|D]}t-| |d}||| }t+d5t-||| }dddn #1swxYwYt |dt | dksJd|dt/|t/| ksJt1||}t3|||dt5j| jjdzdS)zBCheck that the array_api Array gives the same results as ndarrays.z/ is not installed: not checking array_api inputrNz?array_api_compat is not installed: not checking array_api inputr9r`rbz2PyTorch test requires cuda, which is not available>r_r^z/CuPy test requires cuda, which is not availablerfr)Frr1cNi|]"\}}t|tj||#Sr)rrndarrayrrrs r z)check_array_api_input..|s?!sE*UBJ:W:W UrT)array_api_dispatch'z!' attribute is in wrong namespace)xpz not the samerratol)decision_functionrpredict_log_proba predict_probarinverse_transformz' output is in wrong namespacez# did not the return the same result) importlib import_moduleModuleNotFoundErrorr@array_api_compat ImportErrorr0rhas_cudar_rbruntimegetDeviceCountrr_enforce_estimator_tags_Xrrrvarsrr r2 array_devicer/rBrfinforfeps)rrrer2rf array_modrrr_rrrSX_xpy_xparray_attributesest_xpr attribute est_xp_paramest_xp_param_npmethods method_namemethodresult result_xp result_xp_nps rrrPs +O<<     O O O          M      ' ' (9(9!(<(< = =B'!!f&6&6r{&6KLLL 6 6 6 9  + + - - 2 2LMM M B / / /DAq U##A!.!44A!.!44A   C ::a: ' 'D ::a: ' 'DGGAqMMM%)#YY__%6%63ZZF 4 0 0 0 4 +0022  Yvs++ , ' ' *mD.A.A!.D D D D 5s 5 5 5 E D DL))\$-?-?????+LR@@@  )))!'""&,      G  k400 >  t 4 4 4 ; ;4 44T::I ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ) $ $Q '=+>+>q+A A A A 5v 5 5 5 B A AI&&,t*<*<<<<<(r:::   BBB!'""&,      #  s54=AG''G+.G+4LL !L cPtjd}|d}d||dk<t ||}t j|}d|dzt}tt5t|}dddn #1swxYwYt||}t|}t|D]\}}tt5t|}|dvr|d dddn #1swxYwYd |vr d |d |d} nd |d} t!t"t$fddgd| 5tt5|||dddn #1swxYwYt)|drQ||} |dr| j|jddfksJn| j|jdfksJt)|drI||} |dr|jddf} n|jddf} | j| ksJdddn #1swxYwYdS)Nr)(rrg?rr)Scalerr,F) with_mean64rz doesn't seem to support z_ matrix, and is not failing gracefully, e.g. by using check_array(X, accept_large_sparse=False)z doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case.r SparseTrmay_passrrrr9r binary_onlyr )rrruniformrr csr_matrixrintrI FutureWarningrrr?rrrJrZrrr{rshaper) rrrrrrrr matrix_formatrpredprobsexpected_probs_shapes rr}r}s )   " "C  !!AAa#gJ!.!44A  a E S[[b[ ! ! !))#..A - 0 0 0**.)) ***************!)Q//A n % %D3E::';'; q m 4 4 4 6 6n--I333$$u$555 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 = 1EEEL R R II::: ; ;))QC"H%% i%7 8 8 8 , QU ++A  MM!QgM 6 6 6 6 6   %%+VD\\       ;    sB9D C%%-DDD/ct|}tjddgddgddgddgddgddgddgddgddgddgddgddgg }tt ||}tgd}tdgdz}t |dr(t|jd d}|||| dS) Nr9r rr rrrrr) rrrrrr?rreshaper)rrrrrr#s rrmrms n%%I  F F F F F F F F F F F F   A -na@@AAA88899A1#(##G)!3444/ r1-- . . MM!QgM.....rc<t|}tjd}d}t |||df}tj|dz}t||}dg|z}||||dS)Nrrrr) rrrrrrarangerr)rrrrndrgrrrSs rrnrn8sn%%I )   " "CI!.#++Iq>+2R2RSSA )q A!)Q//AC)OM MM!QmM44444rct|}tjddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgg}tjgd}t||}|||tjt |tt5|||tjdt |zdddn #1swxYwYtt5|||tjt |dfddddS#1swxYwYdS)Nr9rr r r9r9r9r9r r r r r9r9r9r9r r r r r) rrrrrrUr3rJr)rrrrrs rroroGs'n%%I  F F F F F F F F F F F F F F F F!   A( AAABBA!)Q//A MM!Qbgc!ffooM666   ?? a"'!c!ff**=*= >>>???????????????   @@ a"'3q661+*>*> ???@@@@@@@@@@@@@@@@@@s$;D  DD+:E22E69E6rUcDt|}t|}t|dt|dtjddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgddggtj}tjgdt }|d kr-|}|}tjt| } d |d } n|d krtj||dzg}tj |d|z g}tjt|dz } d| t|d<t||| d\}}} d |d} ntt||}t||}| ||d| ||| dD]V} t|| rDt|| |} t|| |} t!| | | WdS)Nrr)r9rr r rfr,rU)rzFor z; sample_weight=None is not equivalent to sample_weight=onesrWz?, a zero sample_weight is not equivalent to removing the sample)rrS)rrrrr)rrKrrrcrrUr3vstackhstackr<rrrr{r2rC)rrrV estimator1 estimator2X1y1X2y2sw2rrX_pred1X_pred2s rrqrqls ~&&J~&&JZa0000Za0000  F F F F F F F F F F F F F F F F! $j'   B* BBB# N N NB v~~  gCGG$$$ T4 T T T  YBF| $ $ YAF| $ $gCGGaK(((CGGIIb"c::: B Y4 X X X  ":r 2 2B ":r 2 2BNN24N000NN23N///PLL >6 * * L1gj&11"55G1gj&11"55G ('7 K K K K LLrct|}t|dtjddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgddggtj}tjgdt }t ||}tj|jd}d |d<| }| ||| |d }t||| dS) Nrr)r9rr r r.r,g$@rz8 overwrote the original `sample_weight` given during fitr/) rrKrrrcrrrUrrrrB)rrrrrsample_weight_originalsample_weight_fitrs rrprpsWn%%IYQ////  F F F F F F F F F F F F F F F F! $j'   A* AAAMMMA!)Q//AWQWQZ00 $1.3355 MM!Q&7M888OOOG%'=wOOOOOOrctjd}t||d}|t }t|}|dddfdzt}t|}t||}| ||t|dr| |t|dr||tt dd 5| ||t dddn #1swxYwYd |d vrPd d i|d<d}tt"|5| ||ddddS#1swxYwYdS| ||dS)Nr)rrrr rrzUnknown label typeTrrstringrfoobarrrz#argument must be .* string.* numberr)rrrrrrobjectr?rrrrr{rrrJ ExceptionrZ)rrrrrrrr7s rrtrtsJ )   " "C!.#++8+2L2LMMA A n % %D 111a41S!!An%%I!)Q//A MM!Qy)$$!y+&&A  !5 E E E++ a&))***+++++++++++++++tI&&%.$4 IS ) ) )  MM!Q                       as$*/E%%E),E)F==GGctjd}|dd|dzz}|dd}|ddd dz}t |}t|d ttd 5| ||ddddS#1swxYwYdS) Nrfrry?r%r9rr lowhighrr)zComplex data not supportedrD) rrrrr&randintrrKrJrr)rrrrrrs rrsrss )   # #C  rCKKRK$8$888A "aA  ++b0An%%IYQ////  "> ? ? ? as7CC"Cc|dvrdStjd}|dvrd|dz}nd|dz}t ||}|dddft }t|}t||}t|drd |_ t|d rd |_ t|d rd |_ t|d |||d D]\}t||rJ|j}t#||||j|ks Jd |z]dS)N)SpectralCoclusteringr)rrrerrr rr9rrrrrrz$Estimator changes __dict__ during %s)rrrrrrrrrr{rrrrKr__dict__rr2)rrr*rrrr dict_befores rrr s  ''' )   " "C """   )) )   )) )!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''! y(## Y""" MM!QP 9f % % #,1133K &GIv & &q ) ) )%4446?544 rcX|dp|d S)N_)rr)attrs r_is_public_parameterrU4s($$: c(:(: ;;rc4 t|jdrdSt|}tjd}d|dz}t||}|dddft}t||}t|drd|_ t|drd|_ t|d|j ||||j d D} fd |D}|r Jd d |z fd |D}|r Jdd |zdS)Ndeprecated_originalrrrNrrr9rc0g|]}t||Sr)rU)rrs rrz3check_dont_overwrite_parameters..Ps60DS0I0I rc@g|]}|v|Sr)r)rrdict_before_fits rrz3check_dont_overwrite_parameters..Ts6?;O;O;Q;Q0Q0Q0Q0Q0QrzEstimator adds public attribute(s) during the fit method. Estimators are only allowed to add private attributes either started with _ or ended with _ but %s added, c4g|]}||u|Srr)rrdict_after_fitrZs rrz3check_dont_overwrite_parameters..cs8  C s(; ; ; ; ; ;rzEstimator changes public attribute(s) during the fit method. Estimators are only allowed to change attributes started or ended with _, but %s changed)r{rrrrrrrrrrrrrKrPrrrr) rrrr*rrpublic_keys_after_fitattrs_added_by_fitattrs_changed_by_fitr]rZs @@rrr8s ~&(=>>n%%I )   " "C CKKWK % %%A!.!44A !!!Q$sA!)Q//Ay.))#!" y,''! Y"""(--//O MM!Q'N%**,,, "  ))& ' '  (( $  ))( ) )  *rc Dtjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ t|d| ||dD]=}t||r+ttd t!|||d>dS) NrrrNrrr9rrOzReshape your data)rrrrrrrrrr{rrrKrrGrr2)rrr*rrrrs rr r ss' )   " "C CKKWK % %%A!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''! Y""" MM!QP 9f % %  /F1K1KQqT   rcn|}|jdfd|D}t|tkr&|d}tt d|}t j|r|j}d|D}tj |tj |fS)Nr9cNg|]!}|d"S)r9)r&)rbatchrrhs rrz%_apply_on_subsets..s1IIIettEMM!Z8899IIIrrc|dS)Nrr)xs rz#_apply_on_subsets..s QqTrc6g|]}|Sr)toarray)rrfs rrz%_apply_on_subsets..s @@@1199;;@@@r) rrRtuplelistmapr issparseArravel)rr result_fullresult_by_batchrhs` @r_apply_on_subsetsrrs$q''KJIIIIIqIIIO KE!!!!n s>>?CCDD {##A!m @@@@@ 8K "(?";"; ;;rctjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ t|d| ||dD]]}d || }t||r4tt!|||\}} t#|| d | ^dS) NrrrNrrr9rrrr score_samplesrz={method} of {name} is not invariant when applied to a subset.rrHz>rr)rrrrrrrrrr{rrrKrrrrr2rB) rrr*rrrrr7rprqs rrrse )   " "C CKKWK % %%A!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''! Y""" MM!QRROVVW   9f % % R+< 6**A,, (K KtS Q Q Q QRRrc Htjd}d|dz}t ||}|dddftj}t|dr d||dk<t|}t||}t|d rd|_ t|d rd|_ t|d|||tj|jd}d D]v}d || }t||rMt%t'||||t'||||d|wdS)NrrrNrrrr9r rrrtzY{method} of {name} is not invariant when applied to a datasetwith different sample order.rv& .>rx)rrrrrrrr?rrr{rrrKr permutationrrrCr2) rrr*rrridxrr7s rrrs )   " "C CKKWK % %%A!.!44A !!!Q$rx  A.m444!q& n%%I!)Q//Ay.))#!" y,''! Y""" MM!Q )   + +C + &T& * * 9f % %  (* 6**1--c2* 6**1S622     rctjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ t|d|dkr| d |d kr| d gd}tt|d5|||ddddS#1swxYwYdS)Nrr)r9rrrr9rOPTICSrrrprrq)z1 samplez n_samples = 1z n_samples=1z one samplez1 classz one classTr?)rrrrrrrrrr{rrrKrrJrrrrr*rrrmsgss rrrs )   " "C CKKWK % %%A!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''! Y""" x--- v~~,,,   D  $ 6 6 6 asEEEctjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ |dkrd|_ |d krd |_ t||}t|dgd }tt |d 5|||ddddS#1swxYwYdS)Nrr)rr9rrr9rRandomizedLogisticRegressionrr)z1 feature\(s\)zn_features = 1z n_features=1Tr?)rrrrrrrrrr{rrsample_fractionresidual_thresholdrKrJrrrs rrrs )   " "C CKKWK % %%A!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''!  ---$% !    '* $!)Q//AY""" @ @ @D  $ 6 6 6 asD33D7:D7ctjd}d|dz}|t }t |}t||}t|drd|_ t|drd|_ t|dtt5|||ddddS#1swxYwYdS)Nrrrerrr9r)rrrrrrrrr{rrrKrJrr)rrr*rrrs rr r 7s& )   " "C CKKbK " ""A  An%%I!)Q//Ay.))#!" y,''! Y"""    asC((C,/C,ctdgdgdgddd\}}t|}t||}|rt ||g\}}t ||||dSNr(rrrr9r9r9rr r.rgcentersr*rh cluster_std)rr,rmrrH_check_transformer)rrrYrrs rrrJs III&    DAq &&q))A!+q11A1(!Q001t[!Q/////rctdgdgdgddd\}}t|}t||}t |}t t j|}t||||t||||dSr) rr,rmrrrrrtolist)rrrrthis_Xthis_ys rrr\s III&    DAq &&q))A!+q11A ^^F A ' 'Ft[&&999t[!((**ahhjjAAAAArct\}}t|}tttfd|d5||ddddS#1swxYwYdS)NzThe unfitted transformer z\ does not raise an error when transform is called. Perhaps use check_is_fitted in transform.r/)rnrrJAttributeErrorrr)rrrrs rrrns  DAq $$K  $ , , , ,    ! ! a    ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !sA%%A),A)ctjd}|d}t ||}t |}||}|jd|jdksJdS)ztCheck that using transform without prior fitting doesn't raise a NotFittedError for stateless transformers. rrerrN)rrrrrrrr)rrrrX_transs rrrs )   " "C  !!A!+q11A $$K##A&&G = qwqz ) ) ) ) ) )rc`tj|j\}}t|}t ||t vrrtjtj|tj|f}|ddddfxxdzcc<t|trt|}n|}| ||t|}| ||} t| tr| D]} | jd|ksJn| jd|ksJt|dr|t vr.| ||} | ||} n,| |} | ||} t|dr|dz} t| t| trZt| trEt!| | | D]2\} }}t#| |d d |z t#| |d d |z 3nVt#| | d |zd t#| | d d |z t%| |ksJt%| |ksJt|drt|dsv|jdkrm|jddkr^t)t*d|d5| |ddddfddddS#1swxYwYdSdSdSdSdSdS)Nr r9rrrrr is non deterministic{Gz?z9fit_transform and transform outcomes not consistent in %srxz7consecutive fit_transform outcomes not consistent in %srrrzThe transformer zr does not raise an error when the number of features in transform is different from the number of features in fit.r/r%)rrrrrKrvc_rrrrmrjr{rr?r@ziprCrLndimrJr)rtransformer_origrrrgrhry_transformer_cloneX_predx_predr:X_pred3r7x_pred2x_pred3s rrrs=JqMM/Iz())K[!!! """ U2:a=="*Q--/ 0 33Q36 a a % % !RB OOArk**  , ,Q" , 5 5F&%  , 0 0F<?i///// 0|A)++++{K((=1 & & &!++Ar22G!//R/88GG!++A..G!//R/88G &,? @ @ @ 00C3--  fe $ $ 6GU)C)C 6,/,I,I  (,W!" -U!"   )S      )Q      ((I5555((I5555 Aw   1{ <<< 1!  Q5t555 1 1%%a3B3i000 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1k=1=1\ 1 1 1 1 s*"LL Lc2t|dr|dz}t|tdgdgdgddd \}}t||t }t |}t ||}t|t|}| ||| ||d d g}|D]M}t||d} | 8t||} | ||} | ||} t| | NdS)Nrrrr(rrrr r.rkernelscorerm) r?r@rrr'rrrKr+rr2rC) rrr7rrrpipelinefuncs func_namer func_pipeliner result_pipes rrwrwsT.&9:::,,smm III&    DAq ".!JGGGAn%%I!)Q//AYY''H MM!Q LLA o &E>> y)T22  #Hi88MT!QZZF'-1--K ( = = = >>rc ^tjd}d}||df}t ||}tj|dz}t |}t||}t|gd}|D]}t||d} | | ||dt| j D} | ddkr | dd} | dd vs&Jd |d t|jd | d dS)Nrr(rr)rr partial_fitrrmcg|] }|j Sr)r)rps rrz+check_fit_score_takes_y..sHHHqAFHHHrrr9)rYz.Expected y or Y as second argument for method  of z. Got arguments: .)rrrrrr)rrrKr2r parametersvaluesrRrh) rrr*rgrrrrrrrFs rrkrk sX )   " "CI )Q ((A!.!44A )q An%%I!)Q//AY K K KE   y)T22   DAJJJHHIdOO$>$E$E$G$GHHHDAw&  ABBx7j((((99d9oo666>)((  rctjd}d|dtjz}t ||}|tj}|tj}|tj }|dddf}t||}gd}||||fD]e} t|} t| d| | ||D]+} t| | rt| | | ,fdS)NrrrrrOr9)rrrrrrdrrcrint32rrrKrr{r2) rrr* X_train_32 X_train_64X_train_int_64X_train_int_32rrX_trainrrs rrjrj(sM )   " "CS[[g[..55bjAAAJ*>:FFJ""2:..J&&rx00N&&rx00Nqqq!tA!.!44ALLLG NNK44.)) A&&& gq!!! 4 4Fy&)) 4* 6**7333 4 44rc ptdgdgdgdd\}}t|}t||}t |dD]}||}t |}t||||}||| |}t||gd d gD]M\} } t| tr| d} | j |ks J|d | d |jd | j dNdS)Nr(rrrr.)rgrr*rrrrmrz (method=z3) does not preserve dtype. Original/Expected dtype=z , got dtype=r)rr,rmrr?rrrKrrrrrjrfrh) rrrrrfX_castrX_trans1X_trans2Xtrs rrr?s III&    DAq &&q))A!"2A66A,2CDDD%,-- %%%,,VQ77??61--77??x2_k4RSS  JB"e$$ U8u$$$SS&SS+0>SSGIxSSS%$$$ rcNt|}t|dtjddd}d|d}t t |5||gdddn #1swxYwYtjddd}t|tj gd}d }t t | 5|||ddddS#1swxYwYdS) Nr9rrThe estimator zc does not raise a ValueError when an empty data is used to train. Perhaps use check_array in train.r/r) r9rr9rr9rr9rr9rr9rzG0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* is required.rD) rrKremptyr&rJrrrr)rreX_zero_samplesrX_zero_featuresrr7s rruru_s nAQXa[[((A..N I I I I   G , , ,"" nb!!!"""""""""""""""hqkk))"a00O "!RX.R.R.R%S%STTA WC  # & & &"" oq!!!""""""""""""""""""s$$BB B 6DD!DcVtjd}t||d}|d}tj|d<|d}tj|d<tjd}d|dd<t||}d|d}d|d }d|d } ||fD]X} tt 5t|} t| d ttd dg|5| | |dddn #1swxYwY| ||t!| drFttd dg|5| | dddn #1swxYwYt!| drFttd dg| 5| | dddn #1swxYwYdddn #1swxYwYZdS)NrrrrrCrrrz& doesn't check for NaN and inf in fit.z* doesn't check for NaN and inf in predict.z, doesn't check for NaN and inf in transform.rr9rNaNrrr)rrrrrrrrUrrIrrrKrJrrr{rr) rrr*X_train_finite X_train_nan X_train_infrerror_string_fiterror_string_predicterror_string_transformrrs rrxrxwsX )   " "C.  11N++7+++KK++7+++KK  A AbqbE!.!44APDPPPXXXXGTGGG -11 m 4 4 4 1 1n--I Y * * * 5%.BRSSS * * gq))) * * * * * * * * * * * * * * * MM.! , , ,y),, / %.0// %%g... ///////////////y+.. 1 %.211 ''000 111111111111111) 1 1 1 1 1 1 1 1 1 1 1 1 1 1 111s'9H E7 HE H E AH F/# H/F3 3H6F3 7,H#H9 HH H H HH #H ctdd\}}t|}ttd|d5|||ddddS#1swxYwYdS)z8Test that error is thrown when non-square data provided.rer)rgrhzThe pairwise estimator z+ does not raise an error on non-square datar/N)rrrJrrrrrrrs rryrys r 2 2 2DAqn%%I  Wd W W W     a sA""A&)A&cgd}tdgdgdgddd\}}t||t }t|}|d r^tjd }||jd d }tj | d|<t|} t| |}t| | |||rt| } nct!j| } | j} | drd| vs| dsd| vsJt!j| } t-} |D].}t/| |rt1| ||| |</| D]1}t1| ||}t3| ||2dS)z'Test that we can pickle all estimators.rOr(rrrr r.rrr[rfrF)replacer%rrrsversionN)rrr'r?rrrchoicerrr&rrrKrrHpickledumpsrrrloadsdictr{r2rC)rrrY check_methodsrrrrmaskrunpickled_estimatorpickled_estimatorrrrunpickled_results rr~r~sSRRM III&    DAq ".!JGGGA n % %D K%i##B''zz!&"ez44 f " dn%%I!)Q//AY MM!Q >7 BB#L33*  ! !* - - 3 { " "k&:&::&F&F "!22222$l+<== VVF;; 9f % % ;7WY77::F6NGG?7#6??BB$VF^5EFFFFGGrc$t|dsdSt|}tdd\}}t||}t ||} t |r-t j|}||||n|||n#t$rYdSwxYwttd|d5||dddd f|ddddS#1swxYwYdS) Nr2r9rgr*classesrzZ does not raise an error when the number of features changes between calls to partial_fit.r/r%) r{rrrrrruniquerNotImplementedErrorrJr)rrrrrrs rrrs >= 1 1n%%I  3 3 3DAq!.!44A!.!44A  # # (illG  ! !!Q ! 8 8 8 8  ! !!Q ' ' '   GT G G G   ,, a3B3i+++,,,,,,,,,,,,,,,,,,s%AB** B87B8#DD  D c d\}}}t|}t|}td|||\}}|||||}|j||fks%Jd||f|j|jjdksJt|dr| |} t| tj sJ| j||fks%Jd||f| j| dkt} |j| } t#| |t|d rC||} t| t&r|d st)|D]} | | j|d fks+Jd |d f| | jt#tj| | d t|dd| fnt|d sl| j||fks%Jd ||f| jt#| t|t|drt|d r|t)|D]n} ||dd| f}| |}t#t/|t/|dd| fkdSdSdS)N)rfrrrf)r*rgn_labels n_classesSThe shape of the prediction for multioutput data is incorrect. Expected {}, got {}.irzaThe shape of the decision function output for multioutput data is incorrect. Expected {}, got {}.rr poor_scorer zTThe shape of the probability for multioutput data is incorrect. Expected {}, got {}.r9axis)r?rrrrrrrfrVr{rrrrrrclasses_rErrkrangeargmaxroundr )rrrgrrrrry_preddecisiondec_preddec_expy_probry_proba y_decisions rrr s%-"Ix i Di  I )9x9   DAqMM!Q   q ! !F  C9%%  ay9a.88877=v"Aq 88988 #IfQia00077<->? ? ? ? ? ;28G,,bhw.?.?@ @ @ @ @ " "7 + +F If%%Mryq!1=3Dq3HII  w & & & &y,''3Y 55 A~r!222223322s G..G25G2cztdd\}}t|}t|t|drx|||}|d|||}t||dSdS)z2Check that predict is invariant of compute_labels.rerrcompute_labelsF)r N)rrrKr{rrrrE)rrrrrr9r:s rrrs  3 3 3DAqn%%IYy*++---""**1--E222--""**1--7G,,,,, --rcFd}d}tjd}|d}|d}tjd}t t 5t|}ttdd | 5} | ||dddn #1swxYwY| j r ddddSt| ||| ddddS#1swxYwYdS) Nz6Classifier can't train when only one class is present.z8Classifier can't predict when only one class is present.rrrrrclassTrr/)rrrrrUrIrrrJrrraised_and_matchedrEr) rclassifier_origrrr*rX_testrrcms rrrsOU )   " "Ckkwk''G [[g[ & &F  A - 0 0 0 X X?++  g>N    '  NN7A & & & ' ' ' ' ' ' ' ' ' ' ' ' ' ' '     X X X X X X X X :--f55qBVWWWW X X X X X X X X X X X X X X X X X Xs<9(D!C8 DC D C D$%DDDc|d}|d}tjd}|d}|d}tjddz}|}t |} t| drd |g} ttf|} } nd } ttfd } } t| | d | 5} | |||| j r d d d d St| |tjd|d d d d S#1swxYwYd S)zCheck that classifiers accepting sample_weight fit or throws a ValueError with an explicit message if the problem is reduced to one class. zu failed when fitted on one label after sample_weight trimming. Error message is not explicit, it should have 'class'.z; prediction results should only output the remaining class.r)rrrrr rSz\bclass(es)?\bz\bsample_weight\bNTrrr/)rrrrr)rrrMAssertionErrorrrZrJrrrErrU)rr error_fit error_predictr*rrrrSrrerr_typerrs rrrs  ; ; ;XXXM )   " "Ckkxk((G [[h[ ' 'F " AFFHHM''J_55:"M2+Z8)'$& 3T' g F F F  "w???                v & & ]                       s!E 7E  EEc tdd\}}||}t||d\}}t|}||dk}||dk}|dvr.||z}||z}|rt ||||g\}}}}||fg}t|} | ds|||f|D]\} } tj | } t| } | j \}}t|}t|| } t|| } t!|| d sPt#t$d |d  5|| | dd dddn #1swxYwY|| | || | t+|dsJ|| }|j |fksJ| dst/| |dksJd}d}| d s| drlt#t$||d 5|| d ddddn #1swxYwYn\t#t$||d 5|| jdddn #1swxYwYt+|dr || }| dkrg| ds|j |fksJn|j |dfksJ|dkt:}t=||n3|j || fksJt=tj|d|| d s| drlt#t$||d 5|| d ddddn #1swxYwYn\t#t$||d 5|| jdddn #1swxYwYn#t@$rYnwxYwt+|dr|!| }|j || fksJt=tj|d|tEtj#|dtj$|| d s| drlt#t$||d 5|!| d ddddn #1swxYwYn\t#t$||d 5|!| jdddn #1swxYwYt+|drn|%| }tM|tj'|ddt=tj(|tj(|dS)N,rrrr)r ) BernoulliNB MultinomialNB ComplementNB CategoricalNBrrZThe classifier does not raise an error when incorrect/malformed input data for fit is passed. The number of training examples is not the same as the number of labels. Perhaps use check_X_y in fit.r/r%rrg(\?zuThe classifier {} does not raise an error when shape of X in {} is not equal to (n_test_samples, n_training_samples)z|The classifier {} does not raise an error when the number of features in {} is different from the number of features in fit.rTrr9rrrrrrz)r))rrr<r,rmrrHr?appendrrr3rrrrrKrJrrrr{rr"rr&TrrorrErrrrDsumrUrrBlogargsort)rrrYrX_my_my_bX_bproblemsrrrrrrgrhrr msg_pairwiser7rrr y_log_probs rrrs Ca888HC **W  CsCa000HC    ( ( - -C cQh-C cQh-C PPP swwyy swwyyM6S#s7KLLS#sc |H o & &D  $c ###pOpO1)A,,LL ! :?++ %j! 4 4 %j! 4 4$$$O$ *> 23@'~)====='~)Q???? ( 0 01 4<i-CCCCC&ry'B'B'BFKKKO, >J' >#&$0$7$7>Q$R$RKK'882q9I9IJJJ KKKKKKKKKKKKKKK $&$'JJt5H$I$I>>'88=== >>>>>>>>>>>>>>> '     : / / O--a00FP>P Q Q Q( 6 # 6" , 3 3D/ J JCC#002q1A1ABBB CCCCCCCCCCCCCCC  " # 4 A A66#00555 666666666666666 z#677 O'99!<<  BF6NNADIIII"2:j#9#92:f;M;MNNNapOpOs F77F; >F; #*KK K L66L: =L: C/S!*Q8, S!8Q< <S!?Q< .S!.S S!S S!S S!! S.-S.:*W00W4 7W4 &Y  Y Y c||kr|}|dz}n|}|dz}tj|}d}ttj|||dks J|dS)Nr9zThe number of predicted outliers is not equal to the expected number of outliers and this difference is not explained by the number of ties in the decision_function values)rsortr3r) num_outliersexpected_outliersrstartendsorted_decisionr7s rcheck_outlier_corruptionr5t s'''!#!Q gh''O 9 rys344 5 5 : : :C : : : : :rTcxd}t|d\}}t|d}|rt|}|j\}}t |}t |||||||}|j|fksJ|j j dksJttj |tjddg||} ||} | | fD]/} | j tj d ksJ| j|fksJ0t#t$5||jdddn #1swxYwY| dkt*} d| | dk<t| |t#t$5||jdddn #1swxYwY| |jz } t/| | t#t$5||jdddn #1swxYwYt1|d rt1|d sd }||z }|| ||||}tj|dk}||kr,||} t7||| dSdSdSdS)Nrrrrr)rr%r9floatrnoveltyr(r)rr<rHrrrKrrrrfrVrErrrrrurJrr#rroffset_rBr{rr$r5)rrrYrgrrSrhrrrscoresoutputry_decr1rr0s rrr sI   : : :DAq"""A) %a ( (GIzn%%IYMM! MM!((**   q ! !F >JJcft|dsdSd|jvrdS|jd}td|Dstd|D][}t |t rD|jtkr*|jdkr|j dkr|jdks|j dvs Jd \dS) N_parameter_constraintsrc8g|]}t|tSr)rr=)rcs rrz/check_outlier_contamination.. s"KKKA 1h''KKKrzDcontamination constraints should contain a Real Interval constraint.rr>rightneitherz:contamination constraint should be an interval in (0, 0.5]) r{r?rYrrr=rRrleftrCclosed)rrcontamination_constraints constraints rrr s >#; < <nCCC . Eo V KK1JKKK L L  R   0LL j( + + L4''Os**$++_q((J,=AU,U,U,UK-V,U,U LLrc tddddddd\}}t|}|dd |dd }}|d d}|}t|}t |} t | | |||} | |||} | |||} t| | t| | | j | j ksJ| j | j ksJt| t| ksJt| t| ksJdS) Nrr rrrTrrgrhrrlengthallow_unlabeledr*P) rr-rrkrrKrrrErfrR) rrrrry_trainry_train_list_of_listsy_train_list_of_arraysrry_pred_list_of_listsy_pred_list_of_arrayss rrr s )   DAq aA"vq"vWG rssVF#NN,,!']]''JZ   ^^GW - - 5 5f = =F%>>'3HIIQQ'NN74JKKSSv4555v3444 <06 6 6 6 6 </5 5 5 5 5 <<4 566 6 6 6 6 <<4 455 5 5 5 5 5 5rc t|}t|d\}}}t|d|dddd\}}t|}|d| || d} }|d| || d} } ||| d } t || d} | t |d | d | | }t|tj sJ|d t|d |j | j ksJ|d|j d| j d|j | j ksJ|d|j d| j ddS)zeCheck the output of the `predict` method for classifiers supporting multilabel-indicator targets.rrr rrTrrJNr does not have a  method.z2.predict is expected to output a NumPy array. Got instead.z(.predict outputs a NumPy array of shape instead of rz>.predict does not output the same dtype than the targets. Got ) rrKrr-rr2r@rrrrRrrf)rrrrg test_size n_outputsrrrrrNy_testresponse_method_namepredict_methodrs rrr s''JZ   &0#Iy) )   DAq aA ) na nVG ) na nVGNN7G$$$$Z)=tDDN$OO1EOOOPPP ^F # #F fbj ) )  # # << # # # <6< ' ' '  & & & &l & & & ( ' ' <6< ' ' '  9 9| 9 9)/ 9 9 9 ( ' ' ' 'rc tt|}t|d\}}}t|d|dddd\}}t|}|d| || d} }|d| } ||| d } t || d} | t |d | d | | } t| trt| |ks!Jd |d t| d|d| D]|}|j |dfksJd |d|j d|dfd|j j dksJd |d|j dd |d}t|dd|}dSt| tjrz| j ||fksJd |d| j d||fd| j j dksJd |d| j dd |d}t#d| |t#| d|dSt%dt'| d|d)zkCheck the output of the `predict_proba` method for classifiers supporting multilabel-indicator targets.rTr rrTrrJNrrVrWzWhen zn.predict_proba returns a list, the list should be of length n_outputs and contain NumPy arrays. Got length of rYrzx.predict_proba returns a list, this list should contain NumPy arrays of shape (n_samples, 2). Got NumPy arrays of shape fzW.predict_proba returns a list, it should contain NumPy arrays with floating dtype. Got rXz.predict_proba returns a list, each NumPy array should contain probabilities for each class and thus each row should sum to 1 (or close to 1 due to numerical errors).r9rr/zX.predict_proba returns a NumPy array, the expected shape is (n_samples, n_outputs). Got zN.predict_proba returns a NumPy array, the expected data type is floating. Got z.predict_proba returns a NumPy array, this array is expected to provide probabilities of the positive class and should therefore contain values between 0 and 1.zUnknown returned type z by z4.predict_proba. A list or a Numpy array is expected.)rrKrr-rr2r@rrkr3rrfrVrBr$rrrFrrR)rrrrgrZr[rrrrrNr]predict_proba_methodrrrs rrrC s''JZ   &0#Iy) )   DAq aA ) na nVG ) nGNN7G$$$*":/CTJJ#$OO1EOOOPPP ! !& ) )F &$/ 6{{i''' 8D 8 8f++ 8 8+4 8 8 8(''  B BD:)Q///%%%)-%%q>%%%0// :?c)))))):)))*))%%%%  DHH!H,,a A A A A A' B B( FBJ ' ' | 95555 5D 5 5=C\ 5 5%y1 5 5 5655 | C''' KD K K39< K K K(''  CD C C C  !VW5555&!W555555 BT&\\ B Bt B B B   rc |t|}t|d\}}}t|d|dddd\}}t|}|d| || d} }|d| } ||| d } t || d} | t |d | d | | } t| tj sJ|d t| d | j ||fksJ|d| j d||fd| j j dksJ|d| j d dS)zoCheck the output of the `decision_function` method for classifiers supporting multilabel-indicator targets.rTr rrTrrJNrrVrWz<.decision_function is expected to output a NumPy array. Got rXz].decision_function is expected to provide a NumPy array of shape (n_samples, n_outputs). Got rYrr`z?.decision_function is expected to output a floating dtype. Got )rrKrr-rr2r@rrrrRrrfrV)rrrrgrZr[rrrrrNr]decision_function_methodrs rrr s''JZ   &0#Iy) )   DAq aA ) na nVG ) nGNN7G$$$.&z3GNN'$OO1EOOOPPP % %f - -F fbj ) )  ( (V  ( ( ( . scSrrzPDataConversionWarning('A column-vector y was passed when a 1d array was expected)r?rrrrrr)rrrKrrrr simplefilterrRuntimeWarningnewaxisrr3rBro) rrrr*rgrrrrw y_pred_2dr7s rrr s n % %D )   " "CI!.#++Iq>+2R2RSSA )q A!.!44An%%IY MM!Q   q ! !FY   - - -+h(=>>>h777 a111bj=)***+++++++++++++++!!!$$I 5 Q99 C   1vvzzz3zzz 7    FLLNNIOO$5$566666s+AEEEctj|}t|}|dkr||k}t ||||||}t|dr||}t|tj sJt|dkr| dk t}|j|} t!| |d|ddt%t&| ddt%t&|d  nt)|d d d krtj|d  t} |j| } t!| |d|ddt%t&| ddt%t&|d  |dkr4t!tj|tj|t!||jd|ddt%t&|ddt%t&|jd  dS)Nrrr rz0decision_function does not match classifier for z : expected 'r[z', got 'rr/decision_function_shapeovrr9rrz"Unexpected classes_ attribute for )rrrmeanrKrrr{rrrr3rorrrrErrlr"r2r) rrrrrrrrrr decision_yy_exps rcheck_classifiers_predictionsr{ sillG''J } LZ   NN1a    " "Fz.//"//22(BJ///// w<<1   ((1,44S99H )(3G  JJIIc#w//0000IIc#v..////     Z!:E B Be K K8!444;;C@@J' 3E  JJIIc#uoo....IIc#v..////     ~ 29Q<<6):):;;; JJ IIc#w'' ( ( ( ( IIc#z233 4 4 4 4        rc|dvr|n|S)N)LabelPropagationLabelSpreadingSelfTrainingClassifierr)rry_namess r _choose_check_classifiers_labelsr_ s S S S  rctddd\}}t||d\}}t|}||dk}||dk}t ||}t ||}gd}d d g}t j||}t j||} ||| fg} t|d s| |||f| D]C\} } } | | d fD]%}t|| |}t| |||&Dddg}t j||} t||| }t||||dS)Nr(rr.)rgr*rrr)r )onetwothreerrrrOr%r9) rr<r,rmrrtaker?r"rrr{)rr X_multiclass y_multiclassX_binaryy_binarylabels_multiclass labels_binaryy_names_multiclassy_names_binaryr+rrr y_names_irs rrri s!+1#"""L,")|RS!T!T!TL,!##11,??LLA-.HLA-.H,_lKKL((CCH///ENM!2LAAW]H55N8^45H o= 9 9 9J|5GHIII!HH 1g!7>>##6#67 H HI1$9EEB )!R G G G G HGMW]H55N/hOOH!(HdOLLLLLrc Tt\}}t||dd}tjd}|d|jd}t||}tjd}t|}t|}t|t||tvrFtj |d|z|dt|zg}|j }n|}|||||} |||t"||} t%| | d|dS)Nrrrrr rrx)rnrrrrrKrrrrKrvr0r3r#rrrr7rB) rregressor_origrrSr*r regressor_1 regressor_2rpred1r s rrr sx  DAq!.!CRC&99A )   " "C AAGAJ ''A!.!44A )   " "C''K''K[!!![!!! """ Y1q53;;qs1vv;#>#>>? @ @ T OOAr    " "EOOAryy''(((    " "EE5tT::::::rc Pt\}}||}t|}t|}t ||}t ||}|t vretj d}tj |d|z| dt|zg}|j }n|}|rt|||g\}}}t|dst|drd|_|dkrd|_t%t&d|d  5|||dd dddn #1swxYwYt+|||||||||} | j|jksJt3|d s|||dksJdSdS)Nrr ralphasr0rPassiveAggressiveRegressorrr r/r%rrr)rnrr-rrrrvrrrr0rKr3r#rHr{r0r+rJrrrKrrrr?r) rrrYrrrrr*rrs rrr ss  DAq A aAn%%I!)Q//A!)Q//A """i##A&& Y1q53;;qs1vv;#>#>>? @ @ T 9,aBZ881b 9h ' 'GIw,G,G  +++   $d $ $ $    ! !  a3B3    ! ! ! ! ! ! ! ! ! ! ! ! ! ! !Y MM!R MM!((**biikk***   q ! !F <28 # # # # i\ 2 2 2,q"%%++++,,++s;E&&E*-E*cDtjd}t|}|d}t ||}t ||dddf}|||gd}|D]}t||rJdS)Nr)rr r)rrr) rrrrnormalrrrr{)rrrrrrrrs rrr s )   " "Cn%%I    A!.!44A!)Qqqq!tW55A MM!Q G G GE11 9i00000011rct|drdg}nddg}|D]}t|dd\}}t||dd \}}}} t|d r t||}t||}t t j|}|dkrd d d } nd d d d} t|| } t| dr| dt| dr| d t| dr| dt| dr| dt| | ||| |} t|dst j | dkdksJdS)Nrrr rrre)rr*rrrZr*rTg-C6?)rr9)rr9r rrsrrtrurvmin_weight_fraction_leafr)rn_iter_no_change)rrgףp= ?)r?rr)r'r3rrrrr{rKrrrx) rrr+ n_centersrrrrrNr\rrrs rrr s%/}5553q6#/#/ )!LLL1+; qCa, , , (& o: 6 6 6 300F '22G '**++ >>#//LL#6::L?++66L6QQ :x ( ( .  ! ! ! - - - :z * * 1  ! !4 ! 0 0 0 :9 : : A  ! !4 ! @ @ @ :1 2 2 7  ! !2 ! 6 6 6$$$w(((##F++/|<<< /76Q;''$....G#/#/rct|}t|dr|dt|dr|dt||||||}|d|||||} t || d t ||d ksJdS) Nrsrrtrurrvbalancedrweighted)average)rr{rrKrrr$) rrrrNrr\r#rry_pred_balanceds r'check_class_weight_balanced_classifiersr s!''Jz8$$*S)))z:&&-t,,,Z   NN7G$$$    ' 'Fz222NN7G$$$ ((00O FOZ @ @ @8 DDD      rctjddgddgddgddgddgg}tjgd}|}t|dr|d t|d r|d t|d r|d t ||d|||j}t|}tttj |}|tj |dk|zz |tj |dk|zz d}|||||j} t|| d|zdS)z4Test class weights with non-contiguous class labels.rgrrB)r9r9r9r%r%rsrrtrurvrrrrrr9r%)r9r%z>Classifier %s is not computing class_weight=balanced properly.r/N) rrr{rrKrcoef_rr3r7rr$rB) r Classifierrrr coef_balancedrgrr coef_manuals r-check_class_weight_balanced_linear_classifierr. s 4,q D$<#sc3ZPQQA """##AJz8$$+ T***z:&&-t,,,z4  $###Z   z222NN1a((.3355MAIc")A,,''((I qAv2 3 R94 5L|444..A&&,1133KPSWWrc tdd\}}t||t}t|}t ||}t ||}t|}||||}| D]N\}} ||} tj | tj | ksJd|d|d| d| d OdS) Nrrhrirrz, should not change or mutate the parameter z from z to z during fit.) rrr'rrrKrrrrjoblibhash) rrrrrroriginal_params new_params param_nameoriginal_value new_values rrzrzW s) 1 3 3 3DAq!.!JGGGAn%%I!)Q//AY ! ! # #Fv&&OMM!Q%%''J&5&;&;&=&=   " Nz* {9%%^)D)DDDDDttZZZ <EDDD   rcx t|}n!#t$rtd|dwxYwtt|jdrdSt t|j}t rdD]}||vr||ddt|jDD}tt|t|z t|z }td|D}|rJd|d t|d dS) zCheck setting during init.rz9 should store all parameters as an attribute during init.rWN)r#cg|] }|D]}| Srr)r params_parentr=s rrz3check_no_attributes_set_in_init.. s? "  rc34K|]}t|VdSrQ)rA)rparents rrTz2check_no_attributes_set_in_init.. s*VVFi//VVVVVVrc<g|]}|d|S)rS)r)rrTs rrz3check_no_attributes_set_in_init.. s)RRRT__S=Q=QRRRRrzR should not set any attribute apart from parameters during init. Found attributes r) rrr{rRrrAr:remove__mro__setrsorted)rrr init_paramsrparents_init_params invalid_attrs rririy s .))     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Use the type RealNotInt instead of Real.zThe 'z' parameter of z must be .* Got .* instead.z@ does not raise an informative error message when the parameter z% does not have a valid type or value.rc3jK|].}t|to|dV/dSr N)rr"rrX_types rrTz)check_param_validation..sQvs++I0I0Irrrc,g|]}t|Sr)r5rts rrz*check_param_validation..s QQQzz22QQQrc3@K|]}|dVdSrw)rrxs rrTz)check_param_validation..sB"11r)rrrrrKrrrr?rrRrYrrrr{rJr3r?r2r4r)rrrrrestimator_paramsvalidation_paramsunexpected_paramsmissing_paramsrparam_with_bad_type fit_methodsr constraintsrrrrH bad_values rcheck_param_validationrs )   " "C  !!A Aqr ""A!.!44A%00e0<<AACC >*AFFHH 122S9I5J5JJ-..5F1G1GG 4T 4 44E 4 4#1 4 4  !$4444g4442$y"b1133HHHK&I9I9 $;JG / ) )    )       )      $ $$$$$  VUUDUUU K K# K K K  .))   AA ,?@AAA! 5 5F9f-- -UGLLL 5 5",YI"F"F"F5 /GIv..q1111.GIv..q!444 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5RQ[QQQ % 9 9J 6zBB &     !I ; ;J #: ; ; ;% 9 9y&111PPP99&0 &J&J&J9 3 622155552 6221a888999999999999999 9 9iI9I9s8AH<<I I I)) I65I64AL L! $L! c |}d|dvs|drdStjd}t |}|d t |  |ddd t|  t| fd } fd }||d }| D]\}t |}t|s%||} tvr| d} | d ||} tvr| d} t| | dS)NrrrZrrrr rectvr*|S|SrQ)rvrr)rSrrrs rfit_then_transformz6check_set_output_transform..fit_then_transformsQ & & &771a==**1a00 0wwq!}}&&q)))rc0|SrQ)rm)rSrrs rrmz1check_set_output_transform..fit_transforms  A&&&r)rrmrr)r9rrrrrrrKrrKrr{rv set_outputrC) rrrrrrrmtransform_methodstransform_methodX_trans_no_settingX_trans_defaultrrs ` @@rcheck_set_output_transformrs  % % ' 'DY''4+@' )   " "C())K  !!A!"2A66A Aqr ""A!"2A66A[!!!******* ''''''(&#4"9"9";";JJK(( {D))  --k:: & & &!3A!6 333**;77 & & &-a0O %%7IIII#JJrc*i}d||fd||fd||fd||fg}tfddDrp|D]m\}}} |||tvr| |\} } n| } | f||<nd|fd|fg}t d rZ|D]W\}} |tvr| |\} } n| |} | f||<X|S) zGenerate output to test `set_output` for different configuration: - calling either `fit.transform` or `fit_transform`; - passing either a dataframe or a numpy array to fit; - passing either a dataframe or a numpy array to transform. zfit.transform/df/dfzfit.transform/df/arrayzfit.transform/array/dfzfit.transform/array/arrayc38K|]}t|VdSrQ)r{)rmethrs rrTz-_output_from_fit_transform..,s- G G$7; % % G G G G G Gr)rrzfit_transform/dfzfit_transform/arrayrm)rrrvrrer{rm) rrrrlroutputscasescasedata_fitdata_transformrrSrs ` r_output_from_fit_transformrsG B' !2q) !1b) $a+  E  G G G G2F G G GGG K  K K    OOHa ( ( (***(22>1EE %//??$k&G&G&I&IJGDMM R " E{O,,K K KJD$***(66tQ?? %33D!<<$k&G&G&I&IJGDMM Nrc@ddl}|\}}|\}} t||jsJ|dr|nd} ||| d| } |j|| dS#t $r} t |d|d| | d} ~ wwxYw)NrrlFrCrindexz, does not generate a valid dataframe in the z] case. The generated dataframe is not equal to the expected dataframe. 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