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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 rv z,_get_check_estimator_ids..s*SSSdaW^^Aq11SSSrxz{}({})rT)print_changed_onlyz\sN)callablerrr[keywordsfuncjoinitemsrrnr resubstr)objkwstrings rv_get_check_estimator_idsras00}}<#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_parametersrsbase_estimatorr random_stater )C estimatorsest1皙?alphaest2)rzCan't instantiate estimator z parameters )getattrlen issubclassr rr(r!r.r"rr[rrr2r) Estimatorrequired_parametersrsmsgs rv_construct_instancer(s!)-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   rx)r,rrr r[r)rsrshould_be_skippedr9r8r*s` @@rv _maybe_skipr;s !=Y N Nv  (25'(B(BV$$J 5\\      \ Nrxct|tr |jjn|j}t |dpi}||vr d||fSdS)N _xfail_checksrT)Fz*placeholder reason that will never be used)rrr r[rB)rsrr8 xfail_checkss rvr,r,s\)35'(B(BV$$Ji_===CL\!!\*--- > >rxcddltdDrd}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 rv z*parametrize_with_checks..s, 7 7S:c4 7 7 7 7 7 7rxuPassing 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&LdSrA)rBr[rrr2)rsrtrrr0s rvchecks_generatorz1parametrize_with_checks..checks_generators{# B BI ??+D*955 B Bt,,' 5&AAAAAA B B Brxzestimator, check)ids)r0any TypeErrorr. parametrizer)rr'rGr0s` @rvparametrize_with_checksrLsPMMM 7 7J 7 7 777 / nnBBBBBB ; " ",,..4L #  rxF deprecatedc|dkrd}t||dkrd}tj|t|t t rd}t |t jfd}|r |S|D]O\} |#t$r1}tjt|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.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 Estimator : estimator object Estimator instance to check. .. deprecated:: 1.1 ``Estimator`` was deprecated in favor of ``estimator`` in version 1.1 and will be removed in version 1.3. 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. NrMzBEither estimator or Estimator should be passed to check_estimator.ze'Estimator' was deprecated in favor of 'estimator' in version 1.1 and will be removed in version 1.3.rEc3xKtD]&}t|}t|fV'dSrA)rr;r)rrsrts rvrGz)check_estimator..checks_generatorosU&y11 2 2E 511EWUD111 1 1 1 1 2 2rx) rrr FutureWarningrrBrJr[rrr2)rs generate_onlyr%r'rGr exceptionrts` @rvcheck_estimatorrS#sLnY,66RooL   2   c=))) )T"" / nn  ?? #D222222 "!!!,,..;; 5 ; E)     ; ; ; M#i../ : : : : : : : : ;;;s B(( C#2'CC#ct=tdddddd\}}t|}||fatS)Nrr @*) n_samples n_features n_informativebiasnoiser)REGRESSION_DATASETrJrE fit_transform)rrs rv_regression_datasetr`s]!    1    * *1 - -T rxc|}|jj}|dkr|dd|vr|dkr|dd|vr|j)|t d|j|d vr|d |d kr|d |jjd kr|d d|dvr|d|dkr|dd|vr|dd|vr)|t d|jd|vr|dd|vr|dd|vr+|ds|d|dkr|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#-|d.vr|d/|d0kr|d12d3d4g}||vr&t|d5r|d67t|d8r|d69|d:kr|d;<|d=kr2t t#d>krd?nd@}||A|t$vr|d#,|dBkr|dCDdSdS)ENTSNEr  perplexityn_iterrremax_iterrg) LinearSVR LinearSVCrWNMFi MiniBatchNMFT)rgfresh_restarts) MLPClassifier MLPRegressordMiniBatchDictionaryLearning n_resampling)rr n_estimators)rs max_trialsr)rtn_init)ru batch_sizeMLP)rv MeanShift?) bandwidth TruncatedSVDr LassoLarsIC)noise_variance n_clustersn_best SelectFdr?rTheilSenRegressor) n_components)r)HistGradientBoostingClassifierHistGradientBoostingRegressor)min_samples_leafDummyClassifier stratified)strategyRidgeCVRidgeClassifierCVcvrn_splits)r OneHotEncoderignore)handle_unknownQuantileRegressorz1.6.0highszinterior-point)solverSpectralEmbeddinggh㈵>) eigen_tol)rrZr[ set_paramsrgminrsrrrnr~rmax_subpopulationrr,r-r9r:ri)rsparamsrtloo_cvrs rv_set_checking_parametersrs ! ! # #F   'D v~~***6dfnnA&&&V   )  #a1C*D*D E E E - - -  " - - - 5==  # . . .   '> 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,,,,,#"rxc&eZdZdZdZddZdZdS) _NotAnArrayzvAn object that is convertible to an array. Parameters ---------- data : array-like The data. c8tj||_dSrA)rasarraydata)selfrs rv__init__z_NotAnArray.__init__sJt$$ rxNc|jSrA)r)rdtypes rv __array__z_NotAnArray.__array__s yrxcj|jdkrdStd|j)Nmay_share_memoryTz%Don't want to call array_function {}!)r[rJr)rr typesr6r7s rv__array_function__z_NotAnArray.__array_function__s4 =. . .4?FFt}UUVVVrxrA)r[r __qualname____doc__rrrrxrvrrsU%%%WWWWWrxrcJt|dd}t|dkS)zReturns True if estimator accepts pairwise metric. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if _pairwise is set to True and False otherwise. metricN precomputed)r"bool)rsrs rv_is_pairwise_metricrs(Y$ / /F -' ( ((rxc#K|jdksJd|fVdD]}|||fV|d}|jd|_|jd|_d|fVdD]^}||}|jd|_|jd|_|dz|fV_dS) a;Generate sparse matrices with {32,64}bit indices of diverse format. Parameters ---------- X_csr: CSR Matrix Input matrix in CSR format. 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 rv_generate_sparse_matrixr,s# <5  C;; U^^M::::::: NN5 ! !E   ))EI   ))EI E/''' NN= ) )I$$W-- 8??7++e#Q&&&&& ''rxcPtjd}|d}d||dk<t ||}t j|}d|dzt}tt5t|}dddn #1swxYwYt||}t|}t|D]\}}tt5t|}|dvr|d dddn #1swxYwYd |vr d |d |d} nd |d} t!t"t$fddgd| 5tt5|||dddn #1swxYwYt)|drQ||} |dr| j|jddfksJn| j|jdfksJt)|drI||} |dr|jddf} n|jddf} | j| ksJdddn #1swxYwYdS)Nr)(rrg?rr)ScalerrEF) with_mean64rz doesn't seem to support z_ matrix, and is not failing gracefully, e.g. by using check_array(X, accept_large_sparse=False)z doesn't seem to fail gracefully on sparse data: error message should state explicitly that sparse input is not supported if this is not the case.rSparseTrmay_passrrrr predict_proba binary_onlyr )rrruniform_enforce_estimator_tags_Xr csr_matrixrintrrPr#rrBrrrrJrrrnrshaper) rtrrrrrrsru matrix_formatrpredprobsexpected_probs_shapes rvrprpMs )   " "C  !!AAa#gJ!.!44A  a E S[[b[ ! ! !))#..A - 0 0 0**.)) ***************!)Q//A n % %D3E::';'; q m 4 4 4 6 6n--I333$$u$555 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 = 1EE F F II::: ; ;))QC"H%% i%7 8 8 8  QA  MM!QgM 6 6 6 6 6   %%+VD\\       ;    sB5DC!!-DDD+ct|}tjddgddgddgddgddgddgddgddgddgddgddgddgg }tt ||}tgd}tdgdz}t |dr(t|jd d}|||| dS) Nr r rrrrrrr) r#rrrrrBrreshaper)rtrrsrrrs rvr`r`s n%%I  F F F F F F F F F F F F   A -na@@AAA88899A1#(##G)!3444/ r1-- . . MM!QgM.....rxc<t|}tjd}d}t |||df}tj|dz}t||}dg|z}||||dS)Nrrrr) r#rrrrrarangerr)rtrrsrndrYrrrPs rvrarasn%%I )   " "CI!.#++Iq>+2R2RSSA )q A!)Q//AC)OM MM!QmM44444rxct|}tjddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgg}tjgd}t||}|||tjt |tt5|||tjdt |zdddn #1swxYwYtt5|||tjt |dfddddS#1swxYwYdS)Nr rr rr r r r r r r r r r r r r r r r r) r#rrrrrRr#rr)rtrrsrrs rvrbrbs'n%%I  F F F F F F F F F F F F F F F F!   A( AAABBA!)Q//A MM!Qbgc!ffooM666   ?? a"'!c!ff**=*= >>>???????????????   @@ a"'3q661+*>*> ???@@@@@@@@@@@@@@@@@@s$;D  DD+:E22E69E6rRcDt|}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)Nrrr rr rrrrR)rzFor z; sample_weight=None is not equivalent to sample_weight=onesrTz?, a zero sample_weight is not equivalent to removing the sample)rrP)rrdecision_functionrr)r#rrrfloat64rrRr#vstackhstackr@rrrrnr"r)rtrrS estimator1 estimator2X1y1X2y2sw2rmethodX_pred1X_pred2s rvrdrds ~&&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$$$ T4 T T T  YBF| $ $ YAF| $ $gCGGaK(((CGGIIb"c::: B Y4 X X X  ":r 2 2B ":r 2 2BNN24N000NN23N///PLL >6 * * L1gj&11"55G1gj&11"55G ('7 K K K K LLrxct|}t|dtjddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgddggtj}tjgdt }t ||}tj|jd}d |d<| }| ||| |d }t||| dS) Nrrr rr rrrg$@rz8 overwrote the original `sample_weight` given during fitr) r#rrrrrrrRrrrr)rtrrsrrsample_weight_originalsample_weight_fitrs rvrcrcEsWn%%IYQ////  F F F F F F F F F F F F F F F F! $j'   A* AAAMMMA!)Q//AWQWQZ00 $1.3355 MM!Q&7M888OOOG%'=wOOOOOOrxctjd}t||d}|t }t|}|dddfdzt}t|}t||}| ||t|dr| |t|dr||tt dd 5| ||t dddn #1swxYwYd |d vrPd d i|d<d}tt"|5| ||ddddS#1swxYwYdS| ||dS)Nr)rrrrrrzUnknown label typeTrrstringrfoobarrrz"argument must be a string.* numberr)rrrrrrobjectrBrr#rrrnrrr ExceptionrJ)rtrrrrurrsr's rvrgrgmsH )   " "C!.#++8+2L2LMMA A n % %D 111a41S!!An%%I!)Q//A MM!Qy)$$!y+&&A  !5 E E E++ a&))***+++++++++++++++tI&&%.$2 IS ) ) )  MM!Q                       as$*/E%%E),E)F==GGctjd}|dd|dzz}|dd}|ddd dz}t |}t|d ttd 5| ||ddddS#1swxYwYdS) NrXrry?rr rr lowhighrrzComplex data not supportedr ) rrrrrrandintr#rrrr)rtrrrrrss rvrfrfs )   # #C  rCKKRK$8$888A "aA  ++b0An%%IYQ////  "> ? ? ? as7CC"Cc|dvrdStjd}|dvrd|dz}nd|dz}t ||}|dddft }t|}t||}t|drd |_ t|d rd |_ t|d rd |_ t|d |||d D]\}t||rJ|j}t#||||j|ks Jd |z]dS)N)SpectralCoclusteringrrrrWrrr rr r~rrrrrz$Estimator changes __dict__ during %s)rrrrrrrr#rrnrr~rrr__dict__rr")rtrrrrrsr dict_befores rvrrs  ''' )   " "C """   )) )   )) )!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''! y(## Y""" MM!QP 9f % % #,1133K &GIv & &q ) ) )%4446?544 rxcX|dp|d S)N_)rr)attrs rv_is_public_parameterrs($$: c(:(: ;;rxc4 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_originalrrrrrr r~c0g|]}t||Sr)r)rrs rvrz3check_dont_overwrite_parameters..s60DS0I0I rxc@g|]}|v|Sr)r)rrdict_before_fits rvrz3check_dont_overwrite_parameters..s6?;O;O;Q;Q0Q0Q0Q0Q0QrxzEstimator 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_fitr"s rvrz3check_dont_overwrite_parameters..s8  C s(; ; ; ; ; ;rxzEstimator changes public attribute(s) during the fit method. Estimators are only allowed to change attributes started or ended with _, but %s changed)rnrr#rrrrrrrrrr~rrrrrr) rtrrsrrrpublic_keys_after_fitattrs_added_by_fitattrs_changed_by_fitr%r"s @@rvrrs~&(=>>n%%I )   " "C CKKWK % %%A!.!44A !!!Q$sA!)Q//Ay.))#!" y,''! Y"""(--//O MM!Q'N%**,,, "  ))& ' '  ( !( $  ))( ) )  * #rxc Dtjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ t|d| ||dD]=}t||r+ttd t!|||d>dS) Nrrrrrr r~rzReshape your data)rrrrrrrr#rrnrr~rrrrr")rtrrrrrsrs rvrrs' )   " "C CKKWK % %%A!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''! Y""" MM!QP 9f % %  /F1K1KQqT   rxcn|}|jdfd|D}t|tkr&|d}tt d|}t j|r|j}d|D}tj |tj |fS)Nr cNg|]!}|d"S)r )r)rbatchr rZs rvrz%_apply_on_subsets..!s1IIIettEMM!Z8899IIIrxrc|dS)Nrr)xs rvz#_apply_on_subsets..&s QqTrxcg|] }|j Sr)A)rr.s rvrz%_apply_on_subsets..*s888113888rx) rrBtuplelistmaprissparser1rravel)r r result_fullresult_by_batchrZs` @rv_apply_on_subsetsr9s$q''KJIIIIIqIIIO KE!!!!n s>>?CCDD {##9!m 88888 8K "(?";"; ;;rxctjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ t|d| ||dD]]}d || }t||r4tt!|||\}} t#|| d | ^dS) Nrrrrrr r~rrr score_samplesrz={method} of {name} is not invariant when applied to a subset.rrtHz>atolr)rrrrrrrr#rrnrr~rrrr9r"r) rtrrrrrsrr'r7r8s rvrr/se )   " "C CKKWK % %%A!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''! Y""" MM!QRROVVW   9f % % R+< 6**A,, (K KtS Q Q Q Q!RRrxc Htjd}d|dz}t ||}|dddftj}t|dr d||dk<t|}t||}t|d rd|_ t|d rd|_ t|d|||tj|jd}d D]v}d || }t||rMt%t'||||t'||||d|wdS)Nrrrrrrr r rr~r;zY{method} of {name} is not invariant when applied to a datasetwith different sample order.r=& .>r?)rrrrrrrrBr#rrnrr~rr permutationrrrr") rtrrrrrsidxrr's rvrrUs )   " "C CKKWK % %%A!.!44A !!!Q$rx  A.m444!q& n%%I!)Q//Ay.))#!" y,''! Y""" MM!Q )   + +C + &T& * * 9f % %  (* 6**1--c2* 6**1S622     rxctjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ t|d|dkr| d |d kr| d gd }tt|d5|||ddddS#1swxYwYdS)Nrr)r rrrr r~OPTICS) min_samplesrbrrc)z1 samplez n_samples = 1z n_samples=1z one samplez1 classz one classTr)rrrrrrrr#rrnrr~rrrrrrtrrrrrsmsgss rvrrs )   " "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)rr rrr r~RandomizedLogisticRegressionr r)z1 feature\(s\)zn_features = 1z n_features=1Tr)rrrrrrrr#rrnrr~sample_fractionresidual_thresholdrrrrrHs rvrrs )   " "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)NrrrWrrr r~)rrrrrrr#rrnrr~rrrr)rtrrrrrss rvrrs& )   " "C CKKbK " ""A  An%%I!)Q//Ay.))#!" y,''! Y"""    asC((C,/C,ctdgdgdgddd\}}t|}t||}|rt ||g\}}t ||||dSNrrrrr r r rr rrYcentersrrZ cluster_std)rHrEr_rr_check_transformer)rtrrVrrs rvrrs III&    DAq &&q))A!+q11A1(!Q001t[!Q/////rxctdgdgdgddd\}}t|}t||}t |}t t j|}t||||t||||dSrP) rHrEr_rrrrrVtolist)rtrrrthis_Xthis_ys rvrrs III&    DAq &&q))A!+q11A ^^F A ' 'Ft[&&999t[!((**ahhjjAAAAArxct\}}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)r`r#rAttributeErrorrr)rtrrrs rvrrs  DAq $$K  $ , , , ,    ! ! a    ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !sA%%A),A)c`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 r rrrrr is non deterministic{Gz?z9fit_transform and transform outcomes not consistent in %sr?z7consecutive fit_transform outcomes not consistent in %s)rr@rrzThe transformer zr does not raise an error when the number of features in transform is different from the number of features in fit.rr)rrrr#rric_rrrr_r2rnrrBrziprrDndimrr)rttransformer_origrrrYrZry_transformer_cloneX_predx_predrX_pred3r'x_pred2x_pred3s rvrVrVs=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 1m>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)Nrrr_rrQrRrr rrSkernelscorer_) rBrrHrr6r#rrr/rr"r) rtrr'rrrspipelinefuncs func_namer  func_pipelineresult result_pipes rvrjrjmsT.&9:::,,smm III&    DAq ".!JGGGAn%%I!)Q//AYY''H MM!Q LLA o &E>> y)T22  #Hi88MT!QZZF'-1--K ( = = = >>rxc ^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)Nrrrr)rro partial_fitrr_cg|] }|j Sr)rt)rps rvrz+check_fit_score_takes_y..sHHHqAFHHHrxrr )rYz.Expected y or Y as second argument for method z of z. Got arguments: .)rrrrrrr#rrr"r parametersvaluesrBr[) rtrrrYrrrsrqrrr r6s rvr^r^sX )   " "CI )Q ((A!.!44A )q An%%I!)Q//AY K K KE   y)T22   DAJJJHHIdOO$>$E$E$G$GHHHDAw&  ABBx7j((((99d9oo666>)((  rxctjd}d|dtjz}t ||}|tj}|tj}|tj }|dddf}t||}gd}||||fD]e} t|} t| d| | ||D]+} t| | rt| | | ,fdS)NrrrWrrrr )rrrrrr{rrrint32rr#rrrnr") rtrr X_train_32 X_train_64X_train_int_64X_train_int_32rmethodsX_trainrsrs rvr]r]sM )   " "CS[[g[..55bjAAAJ*>:FFJ""2:..J&&rx00N&&rx00Nqqq!tA!.!44ALLLG NNK44.)) A&&& gq!!! 4 4Fy&)) 4* 6**7333 4 44rxc ptdgdgdgdd\}}t|}t||}t |dD]}||}t |}t||||}||| |}t||gd d gD]M\} } t| tr| d} | j |ks J|d | d |jd | j dNdS)NrrQrRrr)rYrTrrUrrr_rz (method=z3) does not preserve dtype. Original/Expected dtype=z , got dtype=r{)rHrEr_rrBrr#rrrrbrr2rr[) rtrdrrrX_castrX_trans1X_trans2Xtrs rvrrs III&    DAq &&q))A!"2A66A,2CDDD%,-- %%%,,VQ77??61--77??x2_k4RSS  JB"e$$ U8u$$$SS&SS+0>SSGIxSSS%$$$ rxcNt|}t|dtjddd}d|d}t t |5||gdddn #1swxYwYtjddd}t|tj gd}d }t t | 5|||ddddS#1swxYwYdS) Nr rrThe estimator zc does not raise a ValueError when an empty data is used to train. Perhaps use check_array in train.rr) r rr rr rr rr rr rzG0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* is required.r ) r#rremptyrrrrrr)rtreX_zero_samplesrX_zero_featuresrr's rvrhrhs 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)Nrrrrr rrrz& 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.rr rNaNrrr)rrrrrrrrRrrrPr#rrrrrnrr) rtrrX_train_finite X_train_nan X_train_infrerror_string_fiterror_string_predicterror_string_transformrrss rvrkrksX )   " "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.rWr)rYrZzThe pairwise estimator z+ does not raise an error on non-square datarN)rHr#rrrrtrrrrss rvrlrl,s 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||||tj|} |j} | drd| vs| dsd| vsJtj| } t+} |D].} t-|| rt/|| || | </| D]1} t/| | |}t1| | |2dS)z'Test that we can pickle all estimators.rrrQrRrr rrSrmrXrXrF)replacerrrrsversionN)rHrr6rBrrrchoicerrrr#rrrpickledumpsrrrloadsdictrnr"r)rtr check_methodsrrrurmaskrspickled_estimatorrunpickled_estimatorrtrunpickled_results rvrqrq<sSRRM III&    DAq ".!JGGGA n % %D K%i##B''zz!&"ez44 f " dn%%I!)Q//AY MM!Q Y//&Kj))/;+"6"6z"B"B..... ,'899 VVF;; 9f % % ;7WY77::F6NGG?7#6??BB$VF^5EFFFFGGrxc$t|dsdSt|}tdd\}}t||}t ||} t |r-t j|}||||n|||n#t$rYdSwxYwttd|d5||dddd f|ddddS#1swxYwYdS) Nrw2r rYrclassesrzZ does not raise an error when the number of features changes between calls to partial_fit.rr) rnr#rHrrr%runiquerwNotImplementedErrorrr)rtrrsrrrs rvrros >= 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)rXrrrX)rrYn_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 {}.r axis)rBr#rIrrrrrrSrnrrrndarrayrrclasses_rrr3rangeargmaxroundr )rtrsrYrrrurry_preddecisiondec_preddec_expy_probry_proba y_decisions rvrrs%-"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.rWrrcompute_labelsF)rN)rHr#rrnrrrr)rtrrrrrrs rvrrs  3 3 3DAqn%%IYy*++---""**1--E222--""**1--7G,,,,, --rxcFd}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)rrrrrRrrPr#rrrraised_and_matchedrr) rtclassifier_origrrrrX_testrrcms rvrr.sOU )   " "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 rPz\bclass(es)?\bz\bsample_weight\bNTrrr)rrrrrrr#rCAssertionErrorrrJrrrrrrR)rtr error_fit error_predictrrrrrPrrerr_typerrs rvrrEs  ; ; ;XXXM )   " "Ckkxk((G [[h[ ' 'F " AFFHHM''J_55:"M2+Z8)'$& 3T' g F F F  "w???                v & & ]                       s!E 7E  EErc 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,rrrrr ) BernoulliNB MultinomialNB ComplementNB CategoricalNBrrWThe 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.rrrrg(\?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.rQrr rrrrpredict_log_probarB)r@))rHrr@rEr_rrrBappendrrr#rr#rrrrrrrXrnrr)rrTrr6rrrrrrsumrRrrlogargsort)rtrrVr}X_my_my_bX_bproblemsrurrrrrYrZrr msg_pairwiser'rrr y_log_probs rvrrjs 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)Nr zThe 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)rsortr#r) num_outliersexpected_outliersrstartendsorted_decisionr's rvcheck_outlier_corruptionrs'''!#!Q gh''O 9 rys344 5 5 : : :C : : : : :rxTcxd}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$5||jdddn #1swxYwYt1|d rt1|d sd }||z }|| ||||}tj|dk}||kr,||} t7||| dSdSdSdS)Nrrrrrrrr floatrnoveltyrr)rHr@rrr#rrrXrrrSrrrrrr<rrrrroffset_rrnrrr)rtrrVrYrrrZrsrrscoresoutputry_decrrrs rvrr sI   : : :DAq"""A) %a ( (GIzn%%IYMM! MM!((**   q ! !F >JJcft|dsdSd|jvrdS|jd}td|Dstd|D][}t |t rD|jtkr*|jdkr|j dkr|jdks|j dvs Jd \dS) N_parameter_constraintsrc8g|]}t|tSr)rr)rcs rvrz/check_outlier_contamination.._ s"KKKA 1h''KKKrxzDcontamination constraints should contain a Real Interval constraint.rr>rightneitherz:contamination constraint should be an interval in (0, 0.5]) rnr rIrrrrBrleftr closed)rtrcontamination_constraints constraints rvrrS s >#; < <nCCC . Eo V KK1JKKK L L  R   0LL j( + + L4''Os**$++_q((J,=AU,U,U,UK-V,UWLLrxc tddddddd\}}t|}|dd |dd }}|d d}|}t|}t |} t | | |||} | |||} | |||} t| | t| | | j | j ksJ| j | j ksJt| t| ksJt| t| ksJdS) Nrpr rrrTrrYrZrrlengthallow_unlabeledrP) rIrFrXr3r#rrrrrrB) rtrrrry_trainry_train_list_of_listsy_train_list_of_arraysrry_pred_list_of_listsy_pred_list_of_arrayss rvrrn 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 5rxc 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.rprr rrTrrNr 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 r{z>.predict does not output the same dtype than the targets. Got ) r#rrIrFrr"rrrrrBrr)rtrrrY test_size n_outputsrrrrry_testresponse_method_namepredict_methodrs rvrr s''JZ   &0#Iy) )   DAq aA ) na nVG ) na nVGNN7G$$$$Z)=tDDN$OO1EOOOPPP ^F # #F fbj ) )  # # << # # # ) <6< ' ' '  & & & &l & & & ( ' ' <6< ' ' '  9 9| 9 9)/ 9 9 9 ( ' ' ' 'rxc tt|}t|d\}}}t|d|dddd\}}t|}|d| || d} }|d| } ||| d } t || d} | t |d | d | | } t| trt| |ks!Jd |d t| d|d| D]|}|j |dfksJd |d|j d|dfd|j j dksJd |d|j dd |d}t|dd|}dSt| tjrz| j ||fksJd |d| j d||fd| j j dksJd |d| j dd |d}t#d| |t#| d|dSt%dt'| d|d)zkCheck the output of the `predict_proba` method for classifiers supporting multilabel-indicator targets.rr rrTrrNrr r!zWhen zn.predict_proba returns a list, the list should be of length n_outputs and contain NumPy arrays. Got length of r#r{zx.predict_proba returns a list, this list should contain NumPy arrays of shape (n_samples, 2). Got NumPy arrays of shape fzW.predict_proba returns a list, it should contain NumPy arrays with floating dtype. Got r"z.predict_proba returns a list, each NumPy array should contain probabilities for each class and thus each row should sum to 1 (or close to 1 due to numerical errors).r rrzX.predict_proba returns a NumPy array, the expected shape is (n_samples, n_outputs). Got zN.predict_proba returns a NumPy array, the expected data type is floating. Got z.predict_proba returns a NumPy array, this array is expected to provide probabilities of the positive class and should therefore contain values between 0 and 1.zUnknown returned type z by z4.predict_proba. A list or a Numpy array is expected.)r#rrIrFrr"rrr3r#rrrSrrrrrrrB)rtrrrYr$r%rrrrrr'predict_proba_methodrrrs rvrr s''JZ   &0#Iy) )   DAq aA ) na nVG ) nGNN7G$$$*":/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   rxc |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.rr rrTrrNrr r!z<.decision_function is expected to output a NumPy array. Got r"z].decision_function is expected to provide a NumPy array of shape (n_samples, n_outputs). Got r#r{r*z?.decision_function is expected to output a floating dtype. Got )r#rrIrFrr"rrrrrBrrrS)rtrrrYr$r%rrrrrr'decision_function_methodrs rvrr s''JZ   &0#Iy) )   DAq aA ) na nVG ) nGNN7G$$$.&z3GNN'$OO1EOOOPPP % %f - -F fbj ) )  ( (V  ( ( ( ) . scSrxrzzPDataConversionWarning('A column-vector y was passed when a 1d array was expected)rBrrrrrrrr#rrrrr simplefilterr0RuntimeWarningnewaxisrr#rr6) rtrrurrYrrrsrw y_pred_2dr's rvrr{ s n % %D )   " "CI!.#++Iq>+2R2RSSA )q A!.!44An%%IY MM!Q   q ! !FY   - - -+h(=>>>h777 a111bj=)***+++++++++++++++!!!$$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 ''rdecision_function_shapeovrr rrz"Unexpected classes_ attribute for )rrr#meanrrrrnrrrr#r6rrrrrr4rr"r) rrrtrrrrrrr decision_yy_exps rvcheck_classifiers_predictionsrF 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        rxc|dvr|n|S)N)LabelPropagationLabelSpreadingSelfTrainingClassifierr)rtry_namess rv _choose_check_classifiers_labelsrL s S S S  rxctddd\}}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)Nrrr)rYrrUrrr )onetwothreerNrOrrOrr ) rHr@rEr_rrtakerBrrrLrF)rtr X_multiclass y_multiclassX_binaryy_binarylabels_multiclass labels_binaryy_names_multiclassy_names_binaryrrrrK y_names_ires rvrr 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!(HdOLLLLLrxc Tt\}}t||dd}tjd}|d|jd}t||}tjd}t|}t|}t|t||tvrFtj |d|z|dt|zg}|j }n|}|||||} |||t"||} t%| | d|dS)Nrrrrr r`r?)r`rrrrrrrr#rrirr#rrrrrr) rtregressor_origrrrr regressor_1 regressor_2repred1rs rvrr sx  DAq!.!CRC&99A )   " "C AAGAJ ''A!.!44A )   " "C''K''K[!!![!!! """ Y1q53;;qs1vv;#>#>>? @ @ T OOAr    " "EOOAryy''(((    " "EE5tT::::::rxc 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 ralphasr r`PassiveAggressiveRegressorrrrrrrr)r`rrFr#rrrirrrrrr#rrrnr rrrrrrXrrrBro) rtr]rVr}rrrrrers rvrr( 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)rrr)rrr) rrrr#normalrrrrn)rtr]rrrrrqrrs rvrr[ s )   " "Cn%%I    A!.!44A!)Qqqq!tW55A MM!Q G G GE11 9i00000011rxct|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 rrrW)rTrrUrr$rrQg-C6?)rr )rr r rrerprfrgrhmin_weight_fraction_leafr`)rjn_iter_no_change)rkrgףp= ?)rBrHr3r6r#rrr#rrnrrrrC) rtrr n_centersrrrrrr&rrrs rvrrl 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$$$w(((##F++/|<<< /76Q;''$....G#/#/rxct|}t|dr|dt|dr|dt||||||}|d|||||} t || d t ||d ksJdS) Nrerprfrgrhrhbalancedriweighted)average)r#rnrrrrr+) rtrrrrr&rrry_pred_balanceds rv'check_class_weight_balanced_classifiersrr s!''Jz8$$*S)))z:&&-t,,,Z   NN7G$$$    ' 'Fz222NN7G$$$ ((00O FOZ @ @ @8 DDD      rxctjddgddgddgddgddgg}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.rgryr )r r r rrrerhrfrgrhrrrrnrir r)r rz>Classifier %s is not computing class_weight=balanced properly.rN) rrrnrrrcoef_rr#rrrr) rt Classifierrrr coef_balancedrYrr coef_manuals rv-check_class_weight_balanced_linear_classifierry s 4,q D$<#sc3ZPQQA """##AJz8$$+ T***z:&&-t,,,z4  $###Z   z222NN1a((.3355MAIc")A,,''((I qAv2 3 R94 5L|444..A&&,1133KPSWWrxc tdd\}}t||t}t|}t ||}t ||}t|}||||}| D]N\}} ||} tj | tj | ksJd|d|d| d| d OdS) Nrr2r3rmrz, should not change or mutate the parameter z from z to z during fit.) rHrr6r#rrrrrrjoblibhash) rtrrrrsroriginal_params new_params param_nameoriginal_value new_values rvrmrm s) 1 3 3 3DAq!.!JGGGAn%%I!)Q//AY ! ! # #Fv&&OMM!Q%%''J&5&;&;&=&=   " Nz* {9%%^)D)DDDDDttZZZ <EDDD   rxcF t|}n!#t$rtd|dwxYwtt|jdrdSt t|j}t rdD]}||vr||ddt|jDD}tt|t|z t|z }|rJd|dt|d dS) zCheck setting during init.rz9 should store all parameters as an attribute during init.rN)rcg|] }|D]}| Srr)r params_parentr-s rvrz3check_no_attributes_set_in_init.. s? "  rxc34K|]}t|VdSrAr)rparents rvrDz2check_no_attributes_set_in_init.. s*VVFi//VVVVVVrxzR should not set any attribute apart from parameters during init. Found attributes r{) r#r\rnrBrrr remove__mro__setvarssorted)rtrrs init_paramsrparents_init_params invalid_attrs rvr\r\ sp .))     X X X X    tI')>??DOO455K( ( (Ck!!""3'''VVd9oo>UVVVtI''#k*:*::SAT=U=UUL 44 %%%% ' s0c tjddgddgddgddgddgddgddgddgddgg }tjgd}t||}t|}|||||}|tj|j sJ||}t||tj tj |}tj|j sJ||}t||dS)Nrr r ) r r r r r r rrr)rrrr#rrrYrr5rurrrr)rtrrrrC pred_origrs rvroro sI  H H H F F F H F H   A ,,,--A!.!44A   CGGAqMMM AILLNNN ?39 % %%% % ;;q>>DtY''' ,v|C(( ) )C ?39 % %%% % ;;q>>DtY'''''rxctjddgddgddgddgddgddgddgddgddgddgddgddgg }t||}tjgd}t||}dD]}t |||||dS)Nrrr r r) r r r r r r r r r r r r  NotAnArrayPandasDataframe)rrrr"check_estimators_data_not_an_arrayrtrrrobj_types rvrrB s  F F F F F F F F F F F F   A ".!44A 55566A!.!44A5QQ*4AxPPPPQQrxct\}}t||}t||}dD]}t|||||dS)Nr)r`rrrrs rvrr[ se  DAq!.!44A!.!44A5QQ*4AxPPPPQQrxc|tvrtdt|}t|}t|t||dvr"t d||dkrCt tj|}t tj|}n ddl } tj|}|j dkr| |}n| |}| tj|}n#t$rtdwxYw|||||} |||||} t!| | d| dS) NzoSkipping check_estimators_data_not_an_array for cross decomposition module as estimators are not deterministic.rzData type {0} not supportedrrr zDpandas is not installed: not checking estimators for pandas objects.r`r?)rirr#rrrrrrrrcrrrrrr) rtrrrr estimator_1 estimator_2reX_rr`rs rvrrd s """ %   ''K''K[!!![!!!8886==hGGHHH< A ' ' A ' '     ABw!||YYr]]\\"%%bjmm,,BB   V   OOB    # #EOOAq    " "EE5tT::::::s A5D;;Ec@ |j}tt5t|}t |t |||usJt|jd|j} d fdt|j D}n##ttf$rYddddSwxYw|}|tt|dgd}|D]}|j|jks(Jd|jdt'|jdt*t,t.t0t2t'dt&h}|t6jjj t'|j|vpt?|j}|sWJd |jd |jd t'|jjd |jd tAd|Dd |j|!vr$|jJd|jd |jdl||j} tE| t6j#rtI| |jd|jd} tK| r| |jus J| | |jks J|  ddddS#1swxYwYdS)NrrcX|jdko|j|jko|j|jkS)z*Identify hyper parameters of an estimator.r)rtrS VAR_KEYWORDVAR_POSITIONAL)rys rv param_filterz.param_filter s6Ff$3!-/3!"22rxc*g|]}| |Srr)rryrs rvrz:check_parameters_default_constructible.. s6,,q//rxrz parameter r5z has no default valuez Parameter 'z' of estimator 'z ' is of type z which is not allowed. 'z(' must be a callable or must be of type c3$K|] }|jV dSrA)r[)rrBs rvrDz9check_parameters_default_constructible.. s$??t}??????rxr{zEstimator parameter 'zT' is not returned by get_params. If it is deprecated, set its default value to None.z Parameter z> was mutated on init. All parameters must be stored unchanged.)&rZrrPr(r#reprrr"rrr|r}rJrrr#defaultrrtrBr[rrrrr2updatercore numerictypesallTypesr rrrrrr) rtr%rsinitrr init_param allowed_types allowed_value param_value failure_textrs @rvrr sr#I - 0 0 0`K`K' 22  i Y##%%2222y)+@)BTUU    $T??5<<>>KK:&    E`K`K`K`K`K`K`K`K> %%''!#gi9OQS&T&T"U"U"W"WX %9 K9 KJ"j&66666Y(((766 T M  !5!>!E!E!G!G H H HZ'((M9- +,, !  CjoCC&CC *++4CCOCC??????? CCC  =fkkmm33!)11JJOJJ!*JJJ211  1K+rz22 K"; 0BCCCC;;;;!--K&**<<<|jdkr3t j||jdk||jddz}t|drt j|dS|S)Nrequires_positive_yrr rrr)rr )rBabsrrrwhereflatr)rsrs rvrr s)!6777 QQUUWW )///7AFQJJ HQ!&)^Qq A 6 6)!3444&z!W%%% Hrxcdt|dvr |dddf}t|dr||z }dt|dvr4||z tj}|jjdkr||z }t|rt|d }nt|d r |||}|S) N1darrayrrrr categoricalSkewedChi2Sampler euclidean)rrQ) rBrrrrrZr[rr8)rsrrns rvrr sJyi88888 aaadG)!6777 K 9)<<<<< [  * *#'::: K9%% q 5 5 5 I: . . . F1aLL Hrxcgd}|tz }||vrdS|dkr$t|d}nt|}t|drt }|j|j}}t||}t|dt||}| ||tj |j dksJdSdS)N)r!RidgeClassifierrrrjrrJ LassoLarsr rrgrr )rir#rrnrGrtargetrrrrralln_iter_)rtrnot_run_check_n_iterrsirisrres rvrr& s// ### {.))4434?? .)) y*%% .{{ 4;2 &y" 5 5A&&& %na 8 8 avi'1,----- . . .-rxct|}t|dr|tvrgdgdgdgdg}ddgdd gdd gd dgg}n-td gd gdgddd\}}t ||}t |d||||tvr|jD] }|dksJ dS|jdksJdSdS)Nrg)r r ry)ryr r )@rr)rrVg@rgɿg?g?gg333333?rrQrRrr rSr )r#rnrirHrrrr)rtrrsrreiter_s rvrrQ s9n%%Iy*%%* & & & ///???OOOTA+SzC;d DBB"III. EAr*.!<.x s4NNtt{((***NNNNNNrx)r#rrr)rtrrshallow_paramsrs @rvrrp ss nA\\u\--N,,D,))K NNNN~7K7K7M7MNNN N NNN NNNrxc t|}|d}d}|jdi||d}t|t|ks J||D]\}}|||us J|t j t jdg}t|} |D]} || } |D]} | | | < |jdi| |d}t| t|ks J||D]\}}| ||us J|#ttf$r} | j j }tjd|| |d|}|}|d} t|t|ksJ|D]\}}|||usJn$#t $rtj|YnwxYwYd} ~ d} ~ wwxYw| | | <dS)NFrz>get_params result does not match what was passed to set_paramszn{0} occurred during set_params of param {1} on {2}. It is recommended to delay parameter validation until fit.z9Estimator's parameters changed after set_params raised {}r)r#rrrrrrrrrJrrZr[rrrr)rtrrs orig_paramsr' curr_paramsrr test_values test_paramsr default_valuerre_typechange_warning_msgparams_before_exceptions rvrr{ sin%%I&&E&22K JCI'';'''&&E&22K {!! " "c+*:*:*<*<&=&= = = =s = = =!!##((11~"""C""""F7BFD)K;''K!&&((#0#0 #J/  4 4E&+K # 4$ $33{3334(222>> ;++--..#k6F6F6H6H2I2IIII3III'--//44DAq&q>Q.......47z* 6 6 6- ,,2F6:t,L,LPVV# +6''222>> 66;;==>>##((**CC!, 1 1 3 3??16q9Q>>>>>?%666M"4555556- 6:#0 JG#0#0s> F::K  A"K.A,JKJ< 9K;J< <KK ct\}}t||}t|}d}t|dsGt t |5|||ddddS#1swxYwYdSdS)NzUnknown label type: rWrr )r`rr#rBrrr)rtrrrrr's rvrr s  DAq!.!44A nA C a_ - - - Jc * * *   EE!QKKK                  sA>>BBc .ddg}tddd|dd\}}t||d d \}}}}t|} t| d r. s/RRR5:&',,..RRRrx)rHr3r#rnrrrrr rrrrrrrrr#)rtrrTrrrrrr&rsa rank_scoregrouped_y_score sorted_idxrrs @@rvrr s vG     DAq(8 1!((($GVWfn%%Iy-..G79o3V3VG gw'''  # #F + +AAAqD 1 7 7 7 D D  ' ' / / 5 5r 5 B B!)!hqkk J G %j* = = = = = G G G !hRRRRRBIjz(check_fit_idempotent..EsP  9f % %* 6**622rxrBr>z&Idempotency check failed for method {})r@rtolr)rrrr#rrrrrerr&rrnextr4splitr5rrnr" issubdtyperfloatingfinfoepsrrmaxr)rtrrrrYrrtraintestrrr&rtr new_resulttolrrss @@rvrr%sSRRM )   " "Cn%%IYy++--224444...I s)Q 00A!)Q//AN##7 JJIJ & & KKAAIK 6 6!)Q//A|cDDDJJ1MMNNKE4"9aE::GW Aq$>>NFFMM'7####FY MM'7###   9f % % 3F33F;;J}Z-r{;; 3"(:#34488"(2:..22 (vd^^d^^@GGOO       rxctjd}t|}t |d|vr|dd}|d|df}t||}t|r||}n| d d| }t||}t| d ds< t|t|jjd #t$$rYnwxYw||| t|dS#t$$r}t%d |d}~wwxYw)NrXrFrrpr rrrrrz) passes check_is_fitted before being fit!zFEstimator fails to pass `check_is_fitted` even though it has been fit.)rrrr#rrrrerr&rrrBgetr;rrZr[r1r)rtrrrsrYrrrs rvrr_s )   # #Cn%%IYy++----...I s)Q 00A!)Q//AN##7 JJIJ & & KKAAIK 6 6!)Q//A i $ $[% 8 8   I & & & &/     D  MM!Q """""  T   s*+D22 D?>D?E** F 4FF cntjd}t|}t |d|vr|dd}|d|df}t||}t|r||}n| dd| }t||}t|d rJ| ||t|d sJ|j|jd ksJdS) NrrFrrpr rrrn_features_in_r )rrrr#rrrrerr&rrrnrrr)rtrrrsrYrrs rvrrs< )   " "Cn%%IYy++----...I s)Q 00A!)Q//AN##7 JJIJ & & KKAAIK 6 6!)Q//Ay"23333 3 MM!Q 9. / /// /  #qwqz 1 1 1 1 1 1rxcttjd}t|}t |d}|d|df}t ||}d} ||ddS#t$r(tfd|DsYddSdwwxYw)Nrrpr r)z1requires y to be passed, but the target y is Nonez.s-??c3#b''>??????rx) rrrr#rrerrrrI)rtrrrsrYrexpected_err_msgsrs @rvrrs )   " "Cn%%IYI s)Q 00A!)Q//A  a ????->????? 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B """ U2:a=="*Q--/ 0 33Q36 a ++A+44K??U:->->???N  "K L L L??)).1*=>>>???????????????$99.II  ( ( ( ' 4 444 4  "f , , , , CC1BCCC C CCC C+u%%.$Q-a0$*1- .000O>OOs;L7M7MOO 10000sE++E/2E/c ddl}n#t$rtdwxYw|}d|dvs|drdSt dgdgdgdd d \}}t |}t|}t||}|j d }t||}|tvrMtj tj|tj|f}|ddd d fxxd zcc<d t|D} ||| } || |} dt|D} t#t$d5|| dddn #1swxYwY|} || }t)| |t+| t,r| dj d }n | j d }t/| |ksJd|dt/| dS)Nrr rrrWrrQrRr rrSr cg|]}d|Srrrs rvrzBcheck_transformer_get_feature_names_out_pandas..===a a ===rxrr^cg|]}d|S)badrrs rvrzBcheck_transformer_get_feature_names_out_pandas..sBBB1Y1YYBBBrxz0input_features is not equal to feature_names_in_r r,r-)rrrrrHrEr_r#rrrrirrarrrrrr/rrr2r#)rtrdrrurrrrZrefeature_names_indfr.invalid_feature_namesfeature_names_out_defaultfeature_names_in_explicit_namesr1s rv.check_transformer_get_feature_names_out_pandasr>s      V     % % ' 'DY''4+@' III&    DAq &&q))A())K!+q11AJ[!!! B """ U2:a=="*Q--/ 0 33Q36 a ==5+<+<=== a!1 2 2B++B"+55KCBj0A0ABBB  "T U U UAA))*?@@@AAAAAAAAAAAAAAA!, A A C C&1&G&G''#02QRRR+u%%.$Q-a0$*1- %&&.888W>WWs;T7U7UWW 98888s!F55F9<F9c tjd}|d}|ddd}t ||}|d}|rr|j}t|t|z }t|t|z }d|d |d |} ||ks J| td d i} gd } |D]} |j| } | dkrd| d|d}|d| d} t|}|j d i| | i| D]}t||stt|| 5t!dt#|dDrt%|||nt%||||dddn #1swxYwYd| D} | D]} t'|| }n#t($rY wxYw|j d i| |i| D]}t||stt|| 5t!dt#|dDrt%|||nt%||||dddn #1swxYwYՐdS)Nrrrr rWFrz>Mismatch between _parameter_constraints and the parameters of z%. Consider the unexpected parameters z% and expected but missing parameters BadTyper)rrwr_rrWzThe '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/dSrN)rrrrX_types rvrDz)check_param_validation..7sQvs++I0I0Irxrrc,g|]}t|Srr<)rrs rvrz*check_param_validation..Cs QQQzz22QQQrxc3@K|]}|dVdSrB)rrCs rvrDz)check_param_validation..SsB"11rx)rrrrrrrrr rrBr#rrnrr?rIrBr"r>r)rtrrrrestimator_paramsvalidation_paramsunexpected_paramsmissing_paramsrparam_with_bad_type fit_methodsr constraintsrrsrr bad_values rvcheck_param_validationrOsw )   " "C  !!A Aqr ""A!.!44A%00e0<<AACC >*AFFHH 122S9I5J5JJ-..5F1G1GG 4T 4 44E 4 4#1 4 4  !$4444g4442$y"b1133HHHK&;9;9 $;JG / ) ) UUUDUUU 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 6z;OO &     !I ; ;J #: ; ; ;% 9 9y&111PPP99&0 &J&J&J9 3 622155552 6221a888999999999999999 9 9M;9;9s8 AG44G8 ;G8 H"" H/.H/-AK K K cx |}d|dvs|drdStjd}t |}|d t |  |ddd t|  t| fd } fd }||g}|D]o}t |}||} tvr| d} | d ||} tvr| d} t| | pdS) NrrrWrrrr rWctvr*|S|SrA)rirr)rCrrtrs rvfit_then_transformz6check_set_output_transform..fit_then_transformmsQ & & &771a==**1a00 0wwq!}}&&q)))rxc0|SrA)r_)rCrrs rvr_z1check_set_output_transform..fit_transformrs  A&&&rxrr) rrrrr#rrrrrri set_outputr) rtrdrurrrRr_transform_methodstransform_methodX_trans_no_settingX_trans_defaultrrs ` @@rvcheck_set_output_transformrZ]s  % % ' 'DY''4+@' )   " "C())K  !!A!"2A66A Aqr ""A!"2A66A[!!!******* '''''',];-JJK(( --k:: & & &!3A!6 333**;77 & & &-a0O %%7IIIIJJrxci}d||fd||fd||fd||fg}|D]m\}}} ||||tvr|| |\} } n|| } | |f||<nd|fd|fg}|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/arrayzfit_transform/dfzfit_transform/array)rrirr/r_) rrtrr:routputscasescasedata_fitdata_transformX_transrrs rv_output_from_fit_transformrbsPG B' !2q) !1b) $a+  E  G G  !$$$ & & &$..~qAAJGQQ!++N;;G +"C"C"E"EF  R " EGG d & & &$224;;JGQQ!//a88G +"C"C"E"EF Nrxc ddl}|\}}|\}}t||jsJ|||} |j|| dS#t $r} t |d|d| | d} ~ wwxYw)Nrrz, does not generate a valid dataframe in the z] case. 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