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Parameters ---------- estimator : estimator object Estimator instance for which to generate checks. check : partial or callable Check to be marked. expected_failed_checks : dict[str, str], default=None Dictionary of the form {check_name: reason} for checks that are expected to fail. mark : "xfail" or "skip" or None Whether to mark the check as xfail or skip. pytest : pytest module, default=None Pytest module to use to mark the check. This is only needed if ``mark`` is `"xfail"`. Note that one can run `check_estimator` without having `pytest` installed. This is used in combination with `parametrize_with_checks` only. Nxfail)reason)markscNtdtdd)Nz Skipping  for : )rQr)argskwargsrestimator_namer s rnwrappedz_maybe_mark..wrappeds8OK..OO^OOvOO rp)_should_be_skipped_or_markedrireparamrrr) rjrrrpytestshould_be_markedr(r'r s ` @@rn _maybe_markr-s2 <50  f  t|%(1N w||IuFK4E4EV4E4T4T|UUU u         '!!rpreturntuple[bool, str]cH|pi}t|}||vr d||fSdS)a>Check whether a check should be skipped or marked as xfail. Parameters ---------- estimator : estimator object Estimator instance for which to generate checks. check : partial or callable Check to be marked. expected_failed_checks : dict[str, str], default=None Dictionary of the form {check_name: reason} for checks that are expected to fail. Returns ------- should_be_marked : bool Whether the check should be marked as xfail or skipped. reason : str Reason for skipping the check. T)FzCheck is not expected to fail)r)rjrr check_names rnr)r)s?.49rU##J++++J777 1 1rpTrrrc #K|dkrddl}nd}t|j}|tt|fVt ||D];}t||}t ||D]}t|||||V>> 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)rtr4).0ests rn z*parametrize_with_checks..ws, 7 7S:c4 7 7 7 7 7 7rpuPassing a class was deprecated in version 0.23 and isn't supported anymore from 0.24.Please pass an instance instead.c3~K|D]7}||dd}t|r|||d<tdi|Ed{V8dS)Nr)rjrrr)callabler7) estimatorsrrrjr%s rn_checks_generatorz2parametrize_with_checks.._checks_generators# : :I!*fgNND.// S1G1G 1R1R-.199D99 9 9 9 9 9 9 9 9  : :rpzestimator, check)ids)r+any TypeErrorr parametrizerO)rCrrr+msgrDs rnparametrize_with_checksrJ,sRMMM 7 7J 7 7 777 / nn::: ; " "*f.DEE $ #  rpbooleanrvraise) generate_onlyrron_skipon_failcallbackF)prefer_skip_nested_validation)rrrNrOrPrNLiteral['warn'] | NonerOLiteral['raise', 'warn'] | NonerPc t|trd}t||dkr|tdt|j}|r-t jdtt||ddSg} t|||dD]9\}} t|| |\} } | ||t| dd| | d } n#t$rk}|t| |d | | d } |d krEt jd t| d |dt|jd|tYd}~nmd}~wt$r]}|dkr| s|t| || | d} | rd| d<nd}d| d<|d kr tdi| }t j|Yd}~nd}~wwxYw| | |r|di| ;| S)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. scikit-learn also provides a pytest specific decorator, :func:`~sklearn.utils.estimator_checks.parametrize_with_checks`, making it easier to test multiple estimators. Checks are categorised into the following groups: - API checks: a set of checks to ensure API compatibility with scikit-learn. Refer to https://scikit-learn.org/dev/developers/develop.html a requirement of scikit-learn estimators. - legacy: a set of checks which gradually will be grouped into other categories. Parameters ---------- estimator : estimator object Estimator instance to check. 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 .. deprecated:: 1.6 `generate_only` will be removed in 1.8. Use :func:`~sklearn.utils.estimator_checks.estimator_checks_generator` instead. legacy : bool, default=True Whether to include legacy checks. Over time we remove checks from this category and move them into their specific category. .. versionadded:: 1.6 expected_failed_checks : dict, default=None A dictionary of the form:: { "check_name": "this check is expected to fail because ...", } Where `"check_name"` is the name of the check, and `"my reason"` is why the check fails. .. versionadded:: 1.6 on_skip : "warn", None, default="warn" This parameter controls what happens when a check is skipped. - "warn": A :class:`~sklearn.exceptions.SkipTestWarning` is logged and running tests continue. - None: No warning is logged and running tests continue. .. versionadded:: 1.6 on_fail : {"raise", "warn"}, None, default="raise" This parameter controls what happens when a check fails. - "raise": The exception raised by the first failing check is raised and running tests are aborted. This does not included tests that are expected to fail. - "warn": A :class:`~sklearn.exceptions.EstimatorCheckFailedWarning` is logged and running tests continue. - None: No exception is raised and no warning is logged. Note that if ``on_fail != "raise"``, no exception is raised, even if the checks fail. You'd need to inspect the return result of ``check_estimator`` to check if any checks failed. .. versionadded:: 1.6 callback : callable, or None, default=None This callback will be called with the estimator and the check name, the exception (if any), the status of the check (xfail, failed, skipped, passed), and the reason for the expected failure if the check is expected to fail. The callable's signature needs to be:: def callback( estimator, check_name: str, exception: Exception, status: Literal["xfail", "failed", "skipped", "passed"], expected_to_fail: bool, expected_to_fail_reason: str, ) ``callback`` cannot be provided together with ``on_fail="raise"``. .. versionadded:: 1.6 Returns ------- test_results : list List of dictionaries with the results of the failing tests, of the form:: { "estimator": estimator, "check_name": check_name, "exception": exception, "status": status (one of "xfail", "failed", "skipped", "passed"), "expected_to_fail": expected_to_fail, "expected_to_fail_reason": expected_to_fail_reason, } estimator_checks_generator : generator Generator that yields (estimator, check) tuples. Returned when `generate_only=True`. .. TODO(1.8): remove return value .. deprecated:: 1.6 ``generate_only`` will be removed in 1.8. Use :func:`~sklearn.utils.estimator_checks.estimator_checks_generator` instead. Raises ------ Exception If ``on_fail="raise"``, the exception raised by the first failing check is raised and running tests are aborted. Note that if ``on_fail != "raise"``, no exception is raised, even if the checks fail. You'd need to inspect the return result of ``check_estimator`` to check if any checks failed. See Also -------- parametrize_with_checks : Pytest specific decorator for parametrizing estimator checks. estimator_checks_generator : Generator that yields (estimator, check) tuples. Examples -------- >>> from sklearn.utils.estimator_checks import check_estimator >>> from sklearn.linear_model import LogisticRegression >>> check_estimator(LogisticRegression()) [...] r?rLNz9callback cannot be provided together with on_fail='raise'z`generate_only` is deprecated in 1.6 and will be removed in 1.8. Use :func:`~sklearn.utils.estimator_checks.estimator_checks` instead.skipr2passed)rjr1 exceptionstatusexpected_to_failexpected_to_fail_reasonskippedrvzSkipping check r#z because it raised r$)rjr1rWrYrZrrXTfailedrA)rtr4rG ValueErrorrerurv FutureWarningr7r)rrQr) Exceptionr'append)rjrMrrrNrOrPrIr test_resultsr test_can_failr  check_resulter\warnings rncheck_estimatorrfsP)T"" / nn'h2TUUU  ?? #D   T    * fT    L65  B%B% 5!= u4! !  v3  E)   X')%00!"$1+1 LLU    ')%00#$1+1 L&   /k%&8&8//t//Aww'//+,//#  ' ' ''!!-!')%00$1+1 L 2*1 X&&)1 X&&  5EE EE g&&&- 'B L)))  % H $ $| $ $ $ s&1 C F-A!E F-AF((F-ct=tdddddd\}}t|}||fatS)N rB@*) n_samples n_features n_informativebiasnoise random_state)REGRESSION_DATASETr%r6 fit_transform)Xys rn_regression_datasetrws]!    1    * *1 - -T rpc&eZdZdZdZddZdZdS) _NotAnArrayzvAn object that is convertible to an array. Parameters ---------- data : array-like The data. c8tj||_dSr;)npasarraydata)selfr}s rn__init__z_NotAnArray.__init__sJt$$ rpNc|jSr;)r})r~dtypecopys rn __array__z_NotAnArray.__array__s yrpcj|jdkrdStd|j)Nmay_share_memoryTz%Don't want to call array_function {}!)rerGr)r~rtypesr%r&s rn__array_function__z_NotAnArray.__array_function__s4 =. . .4?FFt}UUVVVrp)NN)re __module__ __qualname____doc__rrrrArprnryrysU%%%WWWWWrprycJt|dd}t|dkS)zReturns True if estimator accepts pairwise metric. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if _pairwise is set to True and False otherwise. metricN precomputed)rgr)rjrs rn_is_pairwise_metricrs(Y$ / /F -' ( ((rpc#K|jdksJd|fVdD]}|||fV|d}|jd|_|jd|_d|fVdD]^}||}|jd|_|jd|_|dz|fV_dS) auGenerate sparse matrices or arrays with {32,64}bit indices of diverse format. Parameters ---------- X_csr: scipy.sparse.csr_matrix or scipy.sparse.csr_array Input in CSR format. Returns ------- out: iter(Matrices) or iter(Arrays) In format['dok', 'lil', 'dia', 'bsr', 'csr', 'csc', 'coo', 'coo_64', 'csc_64', 'csr_64'] csr)doklildiabsrcsccoorint64coo_64)rr_64N)rrasformatrowastypecolindicesindptr)X_csr sparse_formatX_coorus rn_generate_sparse_datars# <5  C;; U^^M::::::: NN5 ! !E   ))EI   ))EI E/''' NN= ) )I$$W-- 8??7++e#Q&&&&& ''rprrcFt|}tjd}|d}tjtjfD]}tjd|}t||}|j }| dr3d|vs/| dstj |rd}nd }nd}d |d } tt|| 5|||dddn #1swxYwYdS) Nix)risizerisklearn.test__testingzJInput (y|Y) contains infinity or a value too large for dtype\('float64'\).zInput (y|Y) contains NaN.rdz< should have raised error on fitting array y with inf value.matcherr_msg)rr{random RandomStatestandard_normalnaninffull_enforce_estimator_tags_yr startswithendswithisinfr[r]fit) restimator_origrjrngruvaluerv module_namerrs rnrrsn%%I )   $ $C ))A&"&!   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When check_values is True, it also checks that calling the estimator on the array_api Array gives the same results as ndarrays. rlrrFrr=cNi|]"\}}t|tj||#SrA)rtr{ndarrayr<keyrs rn z)check_array_api_input..0s?!sE*UBJ:W:W UrpT)array_api_dispatchrN'z,' attribute is in wrong namespace, expected z got )xpz not the sameratol)score score_samplesdecision_functionrpredict_log_proba predict_probararray_api_strictrz)' output is in wrong namespace, expected , got .z# did not the return the same resultshaperinverse_transformz;'inverse_transform' output is in wrong namespace, expected z4inverse_transform did not the return the same result)rRr#r_enforce_estimator_tags_Xrrr|rvarsitemsrr;rerg array_devicer:rTr9rrr{rGrtfloatabsrr)!rrrr>r check_valuesrrurvr=X_xpy_xparray_attributesest_xpinput_nsr attribute est_xp_param attribute_nsest_xp_param_npmethodsnumpy_asarray_worksest_fitted_with_as_array method_namemethodresult result_xp result_ns result_xp_npinverse_resultinvese_result_xpinverse_result_nsinvese_result_xp_nps! rnrrs ov 6 6B B / / /DAq %((A!.!44A!.!44A   C ::a: ' 'D ::a: ' 'DGGAqMMM%)#YY__%6%63ZZF 4 0 0 033 4 &&q)2333333333333333 +0022<<Yvs++ t 4 4 4 C C(66q9BL C C C C C C C C C C C C C C Cx''' " " " " " " "('' L))\$-?-?????+LR@@@  < ---#AG,,      ?o&;;;;;?o&;;;;;;G$ 4 4!k-?? $$$$ $ $)::>>$#=#= #  K5{DIIF~g%%tT""""t @I@I k400 >  ' ! !VAq\\F4888 E E8GFK88tDD  E E E E E E E E E E E E E E Efe,, , , ,i// / / / I6I-..1H1HHHHH VAYYF4888 ? ?8GFK88>>  ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?t 4 4 4 = =%i003> J  J 'MM M O55O9 <O9 P77P; >P; +0U''U+ .U+ c,t|||||dS)NT)rr>rr)r)rrrr>rs rn check_array_api_input_and_valuesrs- ! '    rpctr tj}n tj}t |}t jd}|dkrdnd}||df}d||dk<| dd|}t||}t||}||}t|}|j jrN |||d S#t$r(} d|d |j jd } t!| | d } ~ wwxYwd|d |j jd } |||nn#t"t$f$r=} t'jdt+| rYd } ~ d St!| | d } ~ wt$r} t!| | d } ~ wwxYwt!d|d|j jd)zLCheck that estimator tag related with accepting sparse data is properly set.rSpectralCoclustering(r333333?rdz5 raised an exception. The tag self.input_tags.sparse=z might not be consistent with the estimator's ability to handle sparse data (i.e. controlled by the parameter `accept_sparse` in `validate_data` or `check_array` functions).Nzx raised an exception. The estimator failed when fitted on sparse data in accordance with its tag self.input_tags.sparse=z but didn't raise the appropriate error: error message should state explicitly that sparse input is not supported if this is not the case, e.g. by using check_array(X, accept_sparse=False).z [Ss]parsezd didn't fail when fitted on sparse data but should have according to its tag self.input_tags.sparse=z,. The tag is inconsistent and must be fixed.)r]r csr_array csr_matrixrr{rruniformrandintrrrMrrr_AssertionErrorr]rGresearchstr) rrsparse_containerrjrrmrurvrlrdrs rnrrse-!+!,n%%I )   " "C444"I )Q ((AAa#gJ Aqy ))A!)Q//A!)Q//AA I  D #  1 MM!Q      1 1 1CTCC26/2HCCC !))q 0 1 O O O37?3I O O O  1 MM!Q    I& 1 1 1yc!ff--  ))q 0 1 1 1 ))q 0 1 : : :&*o&< : : :   sBC11 D#;#DD#:EF<""F F F<'F77F<ctjd}|d}d||dk<t ||}d||jdztj}tt5t|}dddn #1swxYwYt||}t|}t||D]\}}tt5t|}|dvr|d dddn #1swxYwYd |vr d |d |d } nd |d} tt t"fddgd| 5tt5|||dddn #1swxYwYt'|dra||} |jjr'|jjs| j|jddfksJn| j|jdfksJt'|drM||} |jjs|jddf} n|jddf} | j| ksJdddn #1swxYwYdS)Nr)rrrrrr)Scalerr6F) with_mean64rdz 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, e.g. by using check_array(X, accept_sparse=False).r SparseTrmay_passrrrBrr)r{rrrrrrint32rZr^rrrMr set_paramsr[rGr]rrrrrrrrr) rr sparse_typerrurvrjrl matrix_formatrpredprobsexpected_probs_shapes rn!_check_estimator_sparse_containerr s )   " "C  !!AAa#gJ!.!44A S[[agaj[ ) ) )11"(;;A - 0 0 0**.)) ***************!)Q//A N # #D1++a..AA(;(; 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 = =T==M=== G7T777    #X&     ; ; !-888 $ $ a### $ $ $ $ $ $ $ $ $ $ $ $ $ $ $y),, 7 ((++#079I9W7:!'!*a88888:!'!*6666y/22 ;!//22+7;,-GAJ?((,-GAJ?({&:::::) ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;)(;(;s[%CCC*EE E J2*G  J2 G J2G CJ22J6 9J6 c<t||tjdSr;)rr rrrs rnrrAs%dNF1EEEL R R II::: ; ;))QC"H%%##/= ,##/< , QU ++A  MM!QgM 6 6 6 6 6   %%+VD\\       ;    sCD6,D-D33D66Ect|}tjddgddgddgddgddgddgddgddgddgddgddgddgg }tt ||}tgd}tdgdz}t |}|jjs4|jjr(t|j dd}| |||dS) NrBrrrrrr) rr{rryrrMrrrr}reshaper)rrrjrurvr$rls rnrrsn%%I  F F F F F F F F F F F F   A -na@@AAA88899A1#(##G I  D   )/d.>.K/ r1-- . . MM!QgM.....rpc<t|}tjd}d}t |||df}tj|dz}t||}dg|z}||||dS)Nrrrr) rr{rrrrarangerr)rrrjrndrmrurvrs rnrrsn%%I )   " "CI!.#++Iq>+2R2RSSA )q A!)Q//AC)OM MM!QmM44444rpct|}tjddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgg}tjgd}t||}|||tjt |tt5|||tjdt |zdddn #1swxYwYtt5|||tjt |dfddddS#1swxYwYdS)NrBrrrrBrBrBrBrrrrrBrBrBrBrrrrr) rr{rrroneslenr[r])rrrjrurvs rnrrs'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+:E22E69E6c2t|}t|}t|dt|dtjd}d}|||dz}|dd|}|dd|} |} |} | | d } | | } t| | | d\} } } d | vrtj tj | dtj |d tj |dg}tj | tj |tj |g} tj | ||g} tj | ||g} tt| | }||tj tj | dtj |d tj |dg}tj | ||g} tj | ||g} tt| | }||t%|| } t%|| } ||| } || } || | d|| | | dD]_}t)||rMt+|||}t+|||}d|d|d}t-|||`dS)Nrrrlrrrrr)repeatsaxis)r1cvrB)groupsr3)rvr)rrrrzComparing the output of rzn revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.r)rr\r{rrrandrrepeatrCrhstack full_like ones_likevstacklistr1splitr rrrrgrU)rrrestimator_weightedestimator_repeatedrrmrurvsw X_weighted y_weighted X_repeated y_repeatedgroups_weightedsplits_weightedgroups_repeatedsplits_repeatedrX_pred1X_pred2rs rn _check_sample_weight_equivalencerLs~..~..'a8888'a8888 )   # #CI IM**A Aqy ))A Q  * *BJJ""2A"66J""2"..J!(ZRS!T!T!TJ B ~((****) \*a ( (",q!*<*P>P Q  YBLOOR\!__= > >Y Aq122 Y Aq122     $ $Z $ H H   %%%999) \*a ( (",q!*<*P>P Q  Y Aq122 Y Aq122     $ $Z $ H H   %%%999*+=zJJJ*+=zJJJ#%%j11 %%j11 :4HHH:2FFFP L L >6 * * L9g0&99!<DQQQQQQrpctr tj}n tj} t |||dS#t $rYdSwxYwr;)r]r rrrLrG)rrrs rnrr,s_-!+!,(~?OPPPPP s5 AAct|}t|dtjddgddgddgddgddgddgddgddgddgddgddgddgddgddgddgddggtj}tjgdt }t ||}tj|jd}d |d<| }| ||| |d }t||| dS) NrrrBrrrrr-g$@rz8 overwrote the original `sample_weight` given during fitr6) rr\r{rrrrr.rrrrT)rrrjrurvsample_weight_originalsample_weight_fitrs rnrr8sWn%%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%'=wOOOOOOrpctjd}t||d}|t }t|}|dddfdzt}t|}t||}| ||t|dr| |t|dr||d}tt dd | 5| ||t dddn #1swxYwY|jjsPd d i|d <d}tt&|5| ||ddddS#1swxYwYdS| ||dS)Nr)rrirrrrzy with unknown label type is passed, but an error with no proper message is raised. You can use `type_of_target(..., raise_unknown=True)` to check and raise the right error, or include 'Unknown label type' in the error message.zUnknown label typeTr foobarrrz#argument must be .* string.* numberr)r{rrrrrobjectrMrrrrrrrr[r_rstringrG) rrrrurlrvrjrrIs rnrr`sV )   " "C!.#++8+2L2LMMA A N # #D 111a41S!!An%%I!)Q//A MM!Qy)$$!y+&&A   !5g V V V++ a&))***+++++++++++++++ ? ! %.$4 IS ) ) )  MM!Q                       as$-/E((E,/E,GG Gctjd}|dd|dzz}|dd}|ddd dz}t |}t|d ttd 5| ||ddddS#1swxYwYdS) Nrlriry?r&rBrrlowhighrrzComplex data not supportedrW) r{rrrr'rrr\r[r]r)rrrrurvrjs rnrrs )   # #C  rCKKRK$8$888A "aA  ++b0An%%IYQ////  "> ? ? ? as7CC"Cc&tjd}d|dz}t ||}|dddft }t|}t||}t|d| ||dD]\}t||rJ|j }t||||j |ks Jd|z]dS)NrrrrrBrrrrz$Estimator changes __dict__ during %s)r{rrrrrrrrr\rr__dict__rrg)rrr+rurvrjr dict_befores rnr r s )   " "C CKKWK % %%A!.!44A !!!Q$sAn%%I!)Q//AY""" MM!QP 9f % % #,1133K &GIv & &q ) ) )%4446?544 rpcX|dp|d S)N_)rr)attrs rn_is_public_parameterres($$: c(:(: ;;rpc4 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_originalrrrr n_componentsrB n_clustersc0g|]}t||SrA)re)r<rs rn z3check_dont_overwrite_parameters..s60DS0I0I rpc@g|]}|v|SrA)r)r<rdict_before_fits rnrkz3check_dont_overwrite_parameters..s6?;O;O;Q;Q0Q0Q0Q0Q0QrpzEstimator 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|SrArA)r<rdict_after_fitrms rnrkz3check_dont_overwrite_parameters..s8  C s(; ; ; ; ; ;rpzEstimator changes public attribute(s) during the fit method. Estimators are only allowed to change attributes started or ended with _, but %s changed)rrrr{rrrrrrrrhrir\r`rrrjoin) rrrjr+rurvpublic_keys_after_fitattrs_added_by_fitattrs_changed_by_fitrprms @@rnrrs ~&(=>>n%%I )   " "C CKKWK % %%A!.!44A !!!Q$sA!)Q//Ay.))#!" y,''! Y"""(--//O MM!Q'N%**,,, " "&+=!>!>  ?( $  "677  8rpctjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ t|d| ||dD]_}t||rMttd 5t!|||ddddn #1swxYwY`dS) NrrrrrhrBrir_zReshape your datarW)r{rrrrrrrrrrhrir\rr[r]rg)rrr+rurvrjrs rnrrs )   " "C CKKWK % %%A!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''! Y""" MM!QP11 9f % % 1 *=>>> 1 1* 6**1Q4000 1 1 1 1 1 1 1 1 1 1 1 1 1 1 111s D66D: =D: c|}|jdfd|D}t|tr&|d}tt d|}t j|r |}d|D}tj |tj |fS)NrBcNg|]!}|d"S)rB)r')r<batchrrns rnrkz%_apply_on_subsets..s1IIIettEMM!Z8899IIIrprc|dS)NrrA)xs rnz#_apply_on_subsets.. s QqTrpc6g|]}|SrA)toarray)r<rzs rnrkz%_apply_on_subsets..s @@@1199;;@@@rp) rrttupler=mapr issparser}r{ravel)rru result_fullresult_by_batchrns` @rn_apply_on_subsetsrs$q''KJIIIIIqIIIO+u%%E!!n s>>?CCDD {##A!))++ @@@@@ 8K "(?";"; ;;rpctjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ t|d| ||dD]]}d || }t||r4tt!|||\}} t#|| d | ^dS) NrrrrrhrBrirrrrrz={method} of {name} is not invariant when applied to a subset.rrHz>rr)r{rrrrrrrrrrhrir\rrrrgrT) rrr+rurvrjrrIrrs rnrrse )   " "C CKKWK % %%A!.!44A !!!Q$sAn%%I!)Q//Ay.))#!" y,''! Y""" MM!QRROVVW   9f % % R+< 6**A,, (K KtS Q Q Q QRRrpc tjd}d|dz}t ||}|dddftj}t|}|j|jj s d||dk<t|}t||}t|drd|_ t|drd|_t|d|||tj|jd}d D]}d || } t||r]t)t+t-||||t-||t+||d | dS)NrrrrrBrrhrirzY{method} of {name} is not invariant when applied to a datasetwith different sample order.r& .>r)r{rrrrrrrMrrrrrrhrir\r permutationrrrUr8rg) rrr+rurvrlrjidxrrIs rnrr:s )   " "C CKKWK % %%A!.!44A !!!Q$rx  A N # #D '0D0P'!q& n%%I!)Q//Ay.))#!" y,''! Y""" MM!Q )   + +C + &T& * * 9f % %  (9wy&99!<!S+A+ABB     rpctjd}d|dz}t ||}|dddft }t|}t||}t|drd|_ t|drd|_ t|d|dkr| d |d kr| d gd}tt|d5|||ddddS#1swxYwYdS)Nrr)rBrirrhrBriOPTICS?) min_samplesTSNE?) perplexity)z1 samplez n_samples = 1z n_samples=1z one samplez1 classz one classTrr )r{rrrrrrrrrrhrir\r r[r]rrrr+rurvrjmsgss rnrrgs )   " "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)rirBrrhrBriRandomizedLogisticRegressionRANSACRegressorr)z1 feature\(s\)zn_features = 1z n_features=1Tr)r{rrrrrrrrrrhrisample_fractionresidual_thresholdr\r[r]rrs rnrrs )   " "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)NrrrkrrhrBri)r{rrrrrrrrrhrir\r[r]r)rrr+rurvrjs rnrrs& )   " "C CKKbK " ""A  An%%I!)Q//Ay.))#!" y,''! Y"""    asC((C,/C,ctdgdgdgddd\}}t|}t||}|rt ||g\}}t ||||dSNr)rrrrBrBrBrr皙?rmcentersrrrn cluster_std)r"r6rtrrY_check_transformer)rrrrurvs rnrrs III&    DAq &&q))A!+q11A1(!Q001t[!Q/////rpctdgdgdgddd\}}t|}t||}t |}t t j|}t||||t||||dSr) r"r6rtrryr{r|rtolist)rrrurvthis_Xthis_ys rnrrs III&    DAq &&q))A!+q11A ^^F A ' 'Ft[&&999t[!((**ahhjjAAAAArpct\}}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.r6)rwrr[AttributeErrorr]r)rrrurvs rnrrs  DAq $$K  $ , , , ,    ! ! a    ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !sA%%A),A)ctjd}|d}t ||}t |}||}|jd|jdksJdS)ztCheck that using transform without prior fitting doesn't raise a NotFittedError for stateless transformers. rrkrrN)r{rrrrrrr)rrrruX_transs rnrrs )   " "C  !!A!+q11A $$K##A&&G = qwqz ) ) ) ) ) )rpcltj|j\}}t|}t ||t vrrtjtj|tj|f}|ddddfxxdzcc<t|trt|}n|}| ||t|}| ||} t| tr| D]} | jd|ksJn| jd|ksJt|dr|t vr.| ||} | ||} n,| |} | ||} t|jr|dz} t!| t| trZt| trEt#| | | D]2\} }}t%| |dd|z t%| |dd |z 3nVt%| | d|zd t%| | dd |z t'| |ksJt'| |ksJt|d rt|jrv|jdkrm|jddkr^t-t.d |d5| |ddddfddddS#1swxYwYdSdSdSdSdSdS)NrrBrvrr is non deterministic{Gz?z9fit_transform and transform outcomes not consistent in %srz7consecutive fit_transform outcomes not consistent in %srrzThe transformer zr does not raise an error when the number of features in transform is different from the number of features in fit.r6r&)r{r|rrr\rNc_rtryrrtr~rrrMrrQziprUr^rndimr[r])rtransformer_origrurvrmrnry_transformer_cloneX_predx_predrKX_pred3rIx_pred2x_pred3s rnrrs8JqMM/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 $ % % 7 00C3--  fe $ $ 6GU)C)C 6,/,I,I  (,W!" -U!"   )S      )Q      ((I5555((I5555 Aw   1%%2 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 s0"LL#&L#c8t|jr|dz}t|tdgdgdgddd\}}t ||t }t |}t||}t|t|}| ||| ||d d g}|D]M}t||d} | 8t||} | ||} | ||} t| | NdS) Nrr)rrrrrrkernelrrt) rMrrQr"rr0rrr\r5rrgrU) rrrIrurvrjpipelinefuncs func_namer func_pipeliner result_pipes rnrrasQ1,,smm III&    DAq ".!JGGGAn%%I!)Q//AYY''H MM!Q LLA o &E>> y)T22  #Hi88MT!QZZF'-1--K ( = = = >>rpc tttfttfttfttft tft tfttfttfttfttfttfg }g}t| }|D]P\}}||vrG||vrC||||kr|||fQdd|D}|rJ|d|dS)z5Check that mixins are inherited in the correct order. c3>K|]\}}|jd|jVdS)z comes before/left side of Nre)r<mixin_amixin_bs rnr>z$check_mixin_order..sN GW  JJ8HJJrpa is inheriting from mixins in the wrong order. In general, in mixin inheritance, more specialized mixins must come before more general ones. This means, for instance, `BaseEstimator` should be on the right side of most other mixins. You need to change the order so that: N)rrrrrrrrrrrrr4mroindexr`rq)rrdag violationsrrr violation_strs rnrrsY -( ' }% =) ' }- (-8 }% }% ]+ =) CJ ~   " " $ $C22 sNN3 '""SYYw%7%777   w0 1 1 1II *M       rpc ^tjd}d}||df}t ||}tj|dz}t |}t||}t|gd}|D]}t||d} | | ||dt| j D} | ddkr | dd} | dd vs&Jd |d t|jd | d dS)Nrr)rr)rr partial_fitrrtcg|] }|j SrAr)r<ps rnrkz+check_fit_score_takes_y..sHHHqAFHHHrpr~rB)rvYz.Expected y or Y as second argument for method  of z. Got arguments: r)r{rrrrr*rrr\rgr parametersvaluesr4re) rrr+rmrurvrjrrrr%s rnr}r}sX )   " "CI )Q ((A!.!44A )q An%%I!)Q//AY K K KE   y)T22   DAJJJHHIdOO$>$E$E$G$GHHHDAw&  ABBx7j((((99d9oo666>)((  rpctjd}d|dtjz}t ||}|tj}|tj}|tj }tj ddgdztj}t||}gd }||||fD]e} t|} t| d| | ||D]+} t| | rt!| | | ,fdS) NrrrrrBrrirPr_)r{rrrrrrrrr rrrr\rrrg) rrr+ X_train_32 X_train_64X_train_int_64X_train_int_32rvrX_trainrjrs rnrrsY )   " "CS[[g[..55bjAAAJ*>:FFJ""2:..J&&rx00N&&rx00N !Q"BH---A!.!44ALLLG NNK44.)) A&&& gq!!! 4 4Fy&)) 4* 6**7333 4 44rpc t|}t|dr|dtdgdgdgdd \}}t |}t ||}t|jj D]}| |}t||||}| || |}t||gd d gD]H\} } t| t r| d} | j|ksJ|d | d |d| jdIdS)N set_outputdefaultrr)rrrr)rmrrrrrtrz (method=z3) does not preserve dtype. Original/Expected dtype=z , got dtype=r)rrrr"r6rtrrMrrrr\rrrrtr~r) rrrrurvrX_castX_trans1X_trans2Xtrs rnrrs())K{L))4333 III&    DAq &&q))A!"2A66A*++<L%%%%,,VQ77??61--77??x2_k4RSS  JB"e$$ U8u$$$JJ&JJ+0JJ>@hJJJ%$$$  rpcNt|}t|dtjddd}d|d}t t |5||gdddn #1swxYwYtjddd}t|tj gd}d }t t | 5|||ddddS#1swxYwYdS) NrBrrThe estimator zc does not raise a ValueError when an empty data is used to train. Perhaps use check_array in train.r6r) rBrrBrrBrrBrrBrrBrzG0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* is required.rW) rr\r{emptyr'r[r]rrr)rrrdX_zero_samplesrX_zero_featuresrvrIs rnrr 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)NrrirrrVrirrdz& 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.rrrBrNaNrrr)r{rrrrrrr.rrZr^rr\r[r]rrrr) rrr+X_train_finite X_train_nan X_train_infrverror_string_fiterror_string_predicterror_string_transformrrjs rnrr sX )   " "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.rkri)rmrnzThe pairwise estimator z+ does not raise an error on non-square datar6N)r"rr[r]rrrrurvrjs rnrrK s r 2 2 2DAqn%%I  Wd W W W     a sA""A&)A&cgd}tdgdgdgddd\}}t||t }t|}|jjr^t jd }| |j d d }t j | d|<t|} t| |}t| | |||rt#| } nct%j| } | j} | drd| vs| dsd| vsJt%j| } t1} |D].}t3| |rt5| ||| |</| D]1}t5| ||}t7| ||2dS)z'Test that we can pickle all estimators.r_r)rrrrrrrrlriF)rfr&rrrs_sklearn_versionN)r"rr0rMrrr{rrchoicerrr'rrr\rrYpickledumpsrrrloadsdictrrgrU)rrr check_methodsrurvrlrmaskrjunpickled_estimatorpickled_estimatorrrrunpickled_results rnrr[ sSRRM III&    DAq ".!JGGGA N # #D  %i##B''zz!&"ez44 f " dn%%I!)Q//AY MM!Q >7 BB#L33*  ! !* - - < { " "k&:&::&F&F "'*;;;;;$l+<== VVF;; 9f % % ;7WY77::F6NGG?7#6??BB$VF^5EFFFFGGrpc$t|dsdSt|}tdd\}}t||}t ||} t |r-t j|}||||n|||n#t$rYdSwxYwttd|d5||dddd f|ddddS#1swxYwYdS) Nr2rBrmrrclassesrzZ does not raise an error when the number of features changes between calls to partial_fit.r6r&) rrr"rrrr{uniquerNotImplementedErrorr[r])rrrjrurvrs rnrr s >= 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|||\}}t||}|||||} | j||fks%Jd||f| j| jj dksJt|dr| |} t| tjsJ| j||fks%Jd||f| j| dkt } |j| } t%| | t|d rK||} t| t(r|jjst/|D]}| |j|d fks+Jd |d f| |jt%tj| |d t | dd|fnx|jjsl| j||fks%Jd ||f| jt%| t | t|drt|d r|t/|D]n}||dd|f}| |}t%t5|t5|dd|fkdSdSdS)N)rlrrrl)rrrmn_labels n_classeszSThe 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 {}.rrrzTThe shape of the probability for multioutput data is incorrect. Expected {}, got {}.rBr2)rMrr$rrrrrrkindrrrtr{rrrclasses_rWrr=r poor_scorerangeargmaxroundr)rrrmr r rlrjrurvy_preddecisiondec_preddec_expy_probry_proba y_decisions rnrr s%-"Ix N # #Dn%%I )9x9   DAq ")Q//A MM!Q   q ! !F ->? ? ? ? ? ;28G,,bhw.?.?@ @ @ @ @ " "7 + +F If%%Mryq!1=3Dq3HII  w & & & &y,''3Y 55 A~r!222223322sG11G58G5cztdd\}}t|}t|t|drx|||}|d|||}t||dSdS)z2Check that predict is invariant of compute_labels.rkrrcompute_labelsF)r2N)r"rr\rrrr rW)rr,rurvrrJrKs rnrrB s  3 3 3DAqn%%IYy*++---""**1--E222--""**1--7G,,,,, --rpcpd}d}t|}tjd}|d}|d}t |||\}}tjd}tt5ttd d | 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.rrrX_testrirrclassTr r6)rr{rrrrr.rZr^r[r]rraised_and_matchedrWr) rclassifier_origrrrr+rr5rvcms rnrrQ sOU''J )   " "Ckkwk''G [[g[ & &F/ GFSSSOGV  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<D+6C D+C D+ C ! D+9%D++D/2D/c|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)ririrrirrz\bclass(es)?\bz\bsample_weight\bNTr rr6)r{rrrr*rrr`rr]rGr[rr7rWrr.)rr8 error_fit error_predictr+rr5rvrrrerr_typerr9s rnrri s  ; ; ;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|} | jj r| ||f|D]\} } tj | } t| } | j\}}t|}t!|| } t#|| } t%|| jsPt)t*d|d  5|| | dd dddn #1swxYwY|| | || | t1|d sJ|| }|j|fksJ| jjst7| |d ksJd}d}| js| jjrlt)t*||d 5|| d ddddn #1swxYwYn\t)t*||d 5|| j dddn #1swxYwYt1|dr |!| }| dkrk| j"j#r|j|fksJn|j|dfksJ|$dktJ}tM||n3|j|| fksJtMtj'|d|| js| jjrlt)t*||d 5|!| d ddddn #1swxYwYn\t)t*||d 5|!| j dddn #1swxYwYn#tP$rYnwxYwt1|dr|)| }|j|| fksJtMtj'|d|tUtj+|dtj,|| js| jjrlt)t*||d 5|)| d ddddn #1swxYwYn\t)t*||d 5|)| j dddn #1swxYwYt1|drn|-| }t]|tj/|ddtMtj0|tj0|dS)N,rrr rr) BernoulliNB MultinomialNB ComplementNB CategoricalNBThe 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.r6r&rg(\?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.rrBrrrrr)r)1r"rrCr6rtminrYrMrrr`r{r r/rrrrr\rr[r]rrrrrr+rrrr'TrrrrrrWrr rrVsumr.rrTlogargsort)rr8rrX_my_my_bX_bproblemsrlrurvrr rmrnrr msg_pairwiserIrrr y_log_probs rnrr s 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$$$! *>'5@'~)====='~)Q???? ( 0 01 4<i-CCCCC&ry'B'B'BFKKK) >/ >#&$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;MNNNapOpOsF::F> F> -*K##K' *K' MM M C6S2*R = S2 R S2R .S2?S& S2&S* *S2-S* .S22 S?>S?*XX X :Y!!Y% (Y% c||kr|}|dz}n|}|dz}tj|}d}ttj|||dks J|dS)NrBzThe 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)r{sortr/r ) num_outliersexpected_outliersrstartendsorted_decisionrIs rncheck_outlier_corruptionrZ s'''!#!Q gh''O 9 rys344 5 5 : : :C : : : : :rpcxd}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$5||jdddn #1swxYwYt1|d rt1|d sd }||z }|| ||||}tj|dk}||kr,||} t7||| dSdSdSdS)Nr?rrr rrr&rBrrnoveltyr)r)r"rCrYrrr\rrrrrrWr{r rrrr[r]rHrroffset_rTrr rIrZ)rrrrmrurcrnrjrrscoresoutputry_decrVrrUs rnrr/ sI   : : :DAq"""A) %a ( (GIzn%%IYMM! MM!((**   q ! !F >JJcft|dsdSd|jvrdS|jd}td|Dstd|D][}t |t rD|jtkr*|jdkr|j dkr|jdks|j dvs Jd \dS) N_parameter_constraintsrc8g|]}t|tSrA)rtrE)r<cs rnrkz/check_outlier_contamination.. s"KKKA 1h''KKKrpzDcontamination constraints should contain a Real Interval constraint.rr>rightneitherz:contamination constraint should be an interval in (0, 0.5]) rrcrFrrtrEr4r leftrgclosed)rrcontamination_constraints constraints rnrrw s >#; < <nCCC . Eo V KK1JKKK L L  R   0LL j( + + L4''Os**$++_q((J,=AU,U,U,UK-V,U,U LLrpc Dtddddddd\}}t|}|dd |dd }}|d d}t||| \}}|}t |}t |} t | | |||} | |||} | |||} t| | t| | | j | j ksJ| j | j ksJt| t| ksJt| t| ksJdS) Nr$rrrrTrrmrnr r lengthallow_unlabeledrrPr4) r$r7rrr=rr\rrrWrr4) rr8rurvry_trainr5y_train_list_of_listsy_train_list_of_arraysrry_pred_list_of_listsy_pred_list_of_arrayss rnrr s )   DAq aA"vq"vWG rssVF/QWXXXOGV#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 5rpc t|}t|d\}}}t|d|dddd\}}t|}|d| || d} }|d| || d} } t ||| \}} ||| 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.r$rrrrTrrnNr4r does not have a  method.z2.predict is expected to output a NumPy array. Got instead.z(.predict outputs a NumPy array of shape instead of rz>.predict does not output the same dtype than the targets. Got )rr\r$r7rrrgrQrtr{rr4rr)rr8rrm test_size n_outputsrurvrr5rry_testresponse_method_namepredict_methodrs rnrr s ''JZ   &0#Iy) )   DAq aA ) na nVG ) na nVG/QWXXXOGVNN7G$$$$Z)=tDDN$OO1EOOOPPP ^F # #F fbj ) )  # # << # # # <6< ' ' '  & & & &l & & & ( ' ' <6< ' ' '  9 9| 9 9)/ 9 9 9 ( ' ' ' 'rpc t|}t|d\}}}t|d|dddd\}}t|}|d| || d} }|d| } t ||| \}} ||| d } t || d} | t|d | d | | } t| trt| |ks!Jd |dt| d|d| D]|}|j |dfksJd |d|j d|dfd|j j dksJd |d|j dd |d}t|dd|}dSt| t jrz| j ||fksJd |d| j d||fd| j j dksJd |d| j dd |d}t%d| |t%| d|dSt'dt)| d|d)zkCheck the output of the `predict_proba` method for classifiers supporting multilabel-indicator targets.rxrrrTrrnNr4rrzr{zWhen zn.predict_proba returns a list, the list should be of length n_outputs and contain NumPy arrays. Got length of r}rzx.predict_proba returns a list, this list should contain NumPy arrays of shape (n_samples, 2). Got NumPy arrays of shape fzW.predict_proba returns a list, it should contain NumPy arrays with floating dtype. Got r|z.predict_proba returns a list, each NumPy array should contain probabilities for each class and thus each row should sum to 1 (or close to 1 due to numerical errors).rBrr6zX.predict_proba returns a NumPy array, the expected shape is (n_samples, n_outputs). Got zN.predict_proba returns a NumPy array, the expected data type is floating. Got z.predict_proba returns a NumPy array, this array is expected to provide probabilities of the positive class and should therefore contain values between 0 and 1.zUnknown returned type z by z4.predict_proba. A list or a Numpy array is expected.)rr\r$r7rrrgrQrtr=r/rrrrTrIr{rrXr]r4)rr8rrmr~rrurvrr5rrrpredict_proba_methodrrrs rnrr s''JZ   &0#Iy) )   DAq aA ) na nVG ) nG/QWXXXOGVNN7G$$$*":/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   rpc t|}t|d\}}}t|d|dddd\}}t|}|d| || d} }|d| } t ||| \}} ||| d } t || d} | t|d | d | | } t| tj sJ|d t| d| j ||fksJ|d| j d||fd| j jdksJ|d| j ddS)zoCheck the output of the `decision_function` method for classifiers supporting multilabel-indicator targets.rxrrrTrrnNr4rrzr{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}rrz?.decision_function is expected to output a floating dtype. Got )rr\r$r7rrrgrQrtr{rr4rrr)rr8rrmr~rrurvrr5rrrdecision_function_methodrs rnrr? s''JZ   &0#Iy) )   DAq aA ) na nVG ) nG/QWXXXOGVNN7G$$$.&z3GNN'$OO1EOOOPPP % %f - -F fbj ) )  ( (V  ( ( ( . scSrpzPDataConversionWarning('A column-vector y was passed when a 1d array was expected)rMr{rrrrr*rrr\rrrur+ simplefilterr&RuntimeWarningnewaxisrqrrr/rTr) rrrlr+rmrurvrjrw y_pred_2drIs rnrr s N # #D )   " "CI!.#++Iq>+2R2RSSA )q A!.!44An%%IY MM!Q   q ! !FY   - - -+h(=>>>h777 a111bj=)***+++++++++++++++!!!$$I 5 Q99 C   ( 1vvzzz3zzz 7:= > > > >FLLNNIOO$5$566666s+AEEEcFtj|}t|}|dkr||k}t ||||||}t|dr||}t|tj sJt|dkr| dk t}|j|} t!| |d|ddt%t&| ddt%t&|d  nt)|d d d krtj|d  t} |j| } t!| |d|ddt%t&| ddt%t&|d  t!||jd|ddt%t&|ddt%t&|jd  dS)Nr@rrrz0decision_function does not match classifier for z : expected 'rnz', got 'rr6decision_function_shapeovrrBrz"Unexpected classes_ attribute for )r{r rmeanr\rrrrrtrr/rrrrrWrqrrrgr) rurvrr8rrrrrr decision_yy_exps rncheck_classifiers_predictionsr sillG''J } LZ   NN1a    " "Fz.//"//22(BJ///// w<<1   ((1,44S99H )(3G  JJIIc#w//0000IIc#v..////     Z!:E B Be K K8!444;;C@@J' 3E  JJIIc#uoo....IIc#v..////     JJ IIc#w'' ( ( ( ( IIc#z233 4 4 4 4        rpc|dvr|n|S)N)LabelPropagationLabelSpreadingSelfTrainingClassifierrA)rrvy_namess rn _choose_check_classifiers_labelsr s S S S  rpctddd\}}t||d\}}t|}||dk}||dk}t ||}t ||}gd}d d g}t j||}t j||} ||| fg} t|jj r| |||f| D]C\} } } | | d fD]%}t|| |}t| |||&Dd d g}t j||} t||| }t||||dS)Nr)rr)rmrrrr rr)onetwothreerrOr&rB)r"rCr6rtrr{takerMrrr`rrr)rr8 X_multiclass y_multiclassX_binaryy_binarylabels_multiclass labels_binaryy_names_multiclassy_names_binaryrPrurvr y_names_irs rnrr$ s!+1#"""L,")|RS!T!T!TL,!##11,??LLA-.HLA-.H,_lKKL((CCH///ENM!2LAAW]H55N8^45H  0<J|5GHIII!HH 1g!7>>##6#67 H HI1$9EEB )!R G G G G HGMW]H55N/hOOH!(HdOLLLLLrpc Tt\}}t||dd}tjd}|d|jd}t||}tjd}t|}t|}t|t||tvrFtj |d|z|dt|zg}|j }n|}|||||} |||t"||} t%| | d|dS)Nrrrrrrr)rwrr{rrrrrrr\rNr<r/rHrrrrrT) rregressor_origrurcr+rv regressor_1 regressor_2rpred1r.s rnrrF sx  DAq!.!CRC&99A )   " "C AAGAJ ''A!.!44A )   " "C''K''K[!!![!!! """ Y1q53;;qs1vv;#>#>>? @ @ T OOAr    " "EOOAryy''(((    " "EE5tT::::::rpc `t\}}||}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|jjs|||d ksJdSdS) NrrralphasalpharPassiveAggressiveRegressorrDrEr6r&r)rwrr7rrrrNr{rrr<rr/rHrYrrCr[r]rr\rrrrMrrr) rrrrrurvrr+rrs rnrrb st  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   - 8,q"%%++++,,++s;E&&E*-E*cDtjd}t|}|d}t ||}t ||dddf}|||gd}|D]}t||rJdS)Nr)rirr)rrr) r{rrrnormalrrrr)rrrrrurvrrs rnrr s )   " "Cn%%I    A!.!44A!)Qqqq!tW55A MM!Q G G GE11 9i00000011rpct|jjrddg}ndg}|D]}t|dd\}}t ||dd\}}}} t|jjr t||}t||}ttj |}|dkrdd d } ndd d d } t| | } t| d r| dt| dr| dt| dr| dt| dr| dt| | ||| |} t|jjstj| dkdksJdS)Nrrrrk)rrrrrr~rrg-C6?)rrB)rrBrrn_iterr$rr&r%min_weight_fraction_leafr)rn_iter_no_change)rgףp= ?)rMrrr"r3rrr0r/r{r rr rr\rrrr) rr8rP n_centersrurvrr5rrrrrrs rnrr s(  0<q63#/#/ )!LLL1+; qCa, , , (& O $ $ / 8 300F '22G '**++ >>#//LL#6::L?++66L6QQ :x ( ( .  ! ! ! - - - :z * * 1  ! !4 ! 0 0 0 :9 : : A  ! !4 ! @ @ @ :1 2 2 7  ! !2 ! 6 6 6$$$w(((##F++((8C /76Q;''$....G#/#/rpct|}t|dr|dt|dr|dt||||||}|d|||||} t || d t ||d ksJdS) Nrr$rr&rr%balancedrweighted)average)rrr r\rrr-) rr8rrrr5rr$rry_pred_balanceds rn'check_class_weight_balanced_classifiersr s!''Jz8$$*S)))z:&&-t,,,Z   NN7G$$$    ' 'Fz222NN7G$$$ ((00O FOZ @ @ @8 DDD      rpctjddgddgddgddgddgg}tjgd}t|}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.rgrrf)rBrBrBr&r&rrrr&r%r3rr5rrrBr&)rBr&z>Classifier %s is not computing class_weight=balanced properly.r6N)r{rrrr r\rcoef_rr/rr rIrT) rrrurvr coef_balancedrmr r coef_manuals rnrr s 4,q D$<#sc3ZPQQA """##A~&&Jz8$$+ T***z:&&-t,,,z4  $###Z   z222NN1a((.3355MAIc")A,,''((I qAv2 3 R94 5L|444..A&&,1133KPSWWrpc tdd\}}t||t}t|}t ||}t ||}t|}||||}| D]N\}} ||} tj | tj | ksJd|d|d| d| d OdS) Nrrrrrdz, should not change or mutate the parameter z from z to z during fit.) r"rr0rrr\rrrrjoblibhash) rrrurvrjparamsoriginal_params new_params param_nameoriginal_value new_values rnr~r~s) 1 3 3 3DAq!.!JGGGAn%%I!)Q//AY ! ! # #Fv&&OMM!Q%%''J&5&;&;&=&=   " Nz* {9%%^)D)DDDDDttZZZ <EDDD   rpc. t|}n!#t$rtd|dwxYwtt|jdrdSt t|j}ddt|jDD}tt|t|z t|z }td|D}|rJd|dt|d dS) zCheck setting during init.rdz9 should store all parameters as an attribute during init.rgNcg|] }|D]}| SrArA)r< params_parentr*s rnrkz3check_no_attributes_set_in_init..Ds? "  rpc34K|]}t|VdSr;)rS)r<parents rnr>z2check_no_attributes_set_in_init..Fs*VVFi//VVVVVVrpc<g|]}|d|S)rc)r)r<rds rnrkz3check_no_attributes_set_in_init..Ms)RRRT__S=Q=QRRRRrpzR should not set any attribute apart from parameters during init. Found attributes r) rrrr4rrS__mro__setrsorted)rrrj init_paramsparents_init_params invalid_attrs rnr|r|4sW .))     X X X X    tI')>??DOO455KVVd9oo>UVVVtI''#k*:*::SAT=U=UULRRRRRSSL 44 %%%% 's0c tjddgddgddgddgddgddgddgddgddgg }tjgd}t||}t|}|||||}|tj|j sJ||}t||tj tj |}tj|j sJ||}t||dS)Nr&rBr) rBrBrBrrrrrr)r{rrrrrrr rrrWrrr)rrrurvr= pred_origrs rnrrUsI  H H H F F F H F H   A ,,,--A!.!44A   CGGAqMMM AILLNNN ?39 % %%%% ;;q>>DtY''' ,v|C(( ) )C ?39 % %%%% ;;q>>DtY'''''rpctjddgddgddgddgddgddgddgddgddgddgddgddgg }t||}tjgd}t||}dD]}t |||||dS)NrrrBrr) rBrBrBrrrrBrBrBrrr NotAnArrayPandasDataframe)r{rrr"check_estimators_data_not_an_arrayrrrurvobj_types rnrrxs  F F F F F F F F F F F F   A ".!44A 55566A!.!44A5QQ*4AxPPPPQQrpct\}}t||}t||}dD]}t|||||dS)Nr)rwrrrrs rnrrse  DAq!.!44A!.!44A5QQ*4AxPPPPQQrpc|tvrtdt|}t|}t|t||dvr"t d||dkrCt tj|}t tj|}n ddl } tj|}|j dkr| |d}n| |d}| tj|d}n#t$rtd wxYw|||||} |||||} t!| | d | dS) NzoSkipping check_estimators_data_not_an_array for cross decomposition module as estimators are not deterministic.rzData type {0} not supportedrrrBFrzDpandas is not installed: not checking estimators for pandas objects.rr)rNrQrr\r]rryr{r|rrr!r r"rrrT) rrrurvr estimator_1 estimator_2rX_r#rr.s rnrrs """ %   ''K''K[!!![!!!8886==hGGHHH< A ' ' A ' '     ABw!||YYrY..\\"5\11bjmm%88BB   V   OOB    # #EOOAq    " "EE5tT::::::s A;EEcx t|dS#t$r}td|d|d|d}~wwxYw)z+Checks whether the estimator can be cloned.z Cloning of  failed with error: rN)rr_r)rrrds rnrxrxsdR n RRRI4IIQIIIJJPQQRs 949ct|} t|dS#t$r}td|d|d|d}~wwxYw)z0Check that the estimator has a functioning repr.zRepr of rrN)rreprr_r)rrrjrds rnr{r{skn%%IO Y OOOFFF!FFFGGQNOs" AAAc |j}t|}tt5||usJt |jd|j} ddfdt|j Dfdt|j D}n##ttf$rYddddSwxYw| fd D}|di|}| }|D]}tttt t"t%dt$h} | t(j t%|j| vpt/|j} | sWJd|jd |jd t%|jjd |jd t5d | Dd |j|vr$|jJd|jd |jd*||j} t9| t(jrt=| |jhd|jd} t?| r| |jus J| | |jks J|  ddddS#1swxYwYdS)Nrrrgcx|jdko/|j|jko|j|jko|j|jkSz*Identify hyper parameters of an estimator.r~rr VAR_KEYWORDVAR_POSITIONALrrrs rnparam_default_valuezCcheck_parameters_default_constructible..param_default_valuesIFf$-!-/-!"22- QW, rpcx|jdko/|j|jko|j|jko|j|jkSrrrs rnparam_requiredz>check_parameters_default_constructible..param_requiredsKFf$ -!-/ -!"22 - QW, rpc4g|]}| |jSrAr)r<rrs rnrkz:check_parameters_default_constructible.. s;%%%~~VWGXGX%%%%rpc*g|]}| |SrArA)r<rrs rnrkz:check_parameters_default_constructible..s;$$$BUBUVWBXBX$$$$rpc*i|]}|v||SrArA)r<r* old_paramsrequired_params_namess rnrz:check_parameters_default_constructible..s6   --- :e$---rpz Parameter 'z' of estimator 'z ' is of type z which is not allowed. 'z(' must be a callable or must be of type c3$K|] }|jV dSr;r)r<r4s rnr>z9check_parameters_default_constructible..?s$??t}??????rprzEstimator parameter 'zT' is not returned by get_params. If it is deprecated, set its default value to None.z Parameter z> was mutated on init. All parameters must be stored unchanged.rA) rirrZr^r rgrrrrrGr]rrrrrr~r4updater{ sctypeDictrrBrrerrrtrrWrD)rr Estimatorrjinitdefault_value_paramsrr init_param allowed_types allowed_value param_value failure_textr rrr s @@@@rnrrs(In%%I - 0 0 0uKuK##%%2222y)+@)BTUU(       "%%%% )$ : A A C C%%% !$$$$$T??5<<>>$$$ :&    kuKuKuKuKuKuKuKuKd ))++      #   I,, ,, %%''.3 K3 KJT M  !5!5!7!7 8 8 8Z'((M9- +,, !  CjoCC&CC *++4CCOCC??????? CCC   fkkmm33!)11JJOJJ!*JJJ211  1K+rz22 K"; 0BCCCC;;;;!--K&**<<<|jdkr3tj ||j dk||j ddz}|jj r!|jj stj |dS|S)NrBr)r&rB)rMrrrrGrrrr{whereflatrrr')rjrvrls rnrrYs I  D % QQUUWW  ($0 ) FQJJ HQ!&)^Qq A 6 6 $&T-=-K&z!W%%% Hrpct|jjr|dddf}| |dddf}t|jjr0||z }|||z }t|jjrt|jjr tjn tj }tj ||z  |}|: . .n%%I ;;D It{rA "9b 1 1BY"""!.!44A MM!R 6"*Y.//14 5 5 ?rpct|}t|dr|tvrgdgdgdgdg}ddgdd gdd gd dgg}n-td gd gdgddd\}}t ||}t |d||||tvr|jD] }|dksJ dS|jdksJdSdS)Nr&)rfrfr)rrfrf)@r*r*)r*rj@rgɿg?g?gg333333?r)rrrrrrB)rrrNr"rr\rr')rrrjruriter_s rnrrs9n%%Iy*%%* & & & ///???OOOTA+SzC;d DBB"III. EAr*.!<z.check_get_params_invariance..s4NNtt{((***NNNNNNrp)rrr&r)rrrdshallow_paramsr2s @rnr r ss nA\\u\--N,,D,))K NNNN~7K7K7M7MNNN N NNNNNNrpc t|}|d}d}|jdi||d}t|t|ks J||D]\}}|||us J|t j t jdg}t|} |D]} || } |D]} | | | < |jdi| |d}t| t|ks J||D]\}}| ||us J|#ttf$r} | j j }tjd|| |d|}|}|d} t|t|ksJ|D]\}}|||usJn$#t $rtj|YnwxYwYd} ~ d} ~ wwxYw| | | <dS)NFr.z>get_params result does not match what was passed to set_paramszn{0} occurred during set_params of param {1} on {2}. It is recommended to delay parameter validation until fit.z9Estimator's parameters changed after set_params raised {}rA)rrr rrrr{rrrGr]rirerurvrr)rrrj orig_paramsrI curr_paramskv test_values test_paramsr default_valuerrde_typechange_warning_msgparams_before_exceptions rnr r sin%%I&&E&22K JCI'';'''&&E&22K {!! " "c+*:*:*<*<&=&= = = =s = = =!!##((11~"""C""""F7BFD)K;''K!&&((#0#0 #J/  4 4E&+K # 4$ $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 c&t\}}t||}t|}d}d}t|jsHt t ||5|||ddddS#1swxYwYdSdS)NzWhen a classifier is passed a continuous target, it should raise a ValueError with a message containing 'Unknown label type: ' or a message indicating that a continuous target is passed and the message should include the word 'continuous'zUnknown label type: |continuousr)rwrrrMrr[r]r)rrrurvrdrrIs rnrr6s  DAq!.!44A nA  ,C A;; $ Jc7 ; ; ;   EE!QKKK                  s BB Bc .ddg}tddd|dd\}}t||d d \}}}}t|} t| d r.qs/RRR5:&',,..RRRrp)r"r3rrrrrrrrVrr{rr rKrWr*r/)rrrrurvrr5rrrrja rank_scoregrouped_y_score sorted_idxrErFs @@rnrrJs vG     DAq(8 1!((($GVWfn%%Iy-..G79o3V3VG gw'''  # #F + +AAAqD 1 7 7 7 D D  ' ' / / 5 5r 5 B B!)!hqkk J G %j* = = = = = G G G !hRRRRRBIj.sP  9f % %* 6**622rprrrz&Idempotency check failed for method {})rrtolr)r{rrrr\rrr rrr rrnextr2r>r4rrrg issubdtyperfloatingfinfoepsrrUmaxr)rrrrrmrurvtraintestrrrrrr new_resulttolr5rjs @@rnr r sSRRM )   " "Cn%%IYy++--224444...I s)Q 00A!)Q//AN##7 JJIJ & & KKAAIK 6 6!)Q//A|cDDDJJ1MMNNKE4"9aE::GW Aq$>>NFFMM'7####FY MM'7### 9f % % 3F33F;;Jz7++ 3  "+11 3"(:#34488"(2:..22 (vd^^d^^@GGOO     rpctjd}t|}t |d|vr|dd}|d|df}t||}t|r||}n| d d| }t||}t|j r< t|t|jjd #t$$rYnwxYw||| t|dS#t$$r}t%d |d}~wwxYw) NrlrOFrPr$rrQrrr[z) passes check_is_fitted before being fit!zFEstimator fails to pass `check_is_fitted` even though it has been fit.)r{rrrr\rr rrr rrrMrr_rrirer(r)rrrrjrmrurvrds rnr r s )   # #Cn%%IYy++----...I s)Q 00A!)Q//AN##7 JJIJ & & KKAAIK 6 6!)Q//A '   I & & & &/     D  MM!Q """""  T   s*8+D## D0/D0 E E:%E55E:cntjd}t|}t |d|vr|dd}|d|df}t||}t|r||}n| dd| }t||}t|d rJ| ||t|d sJ|j|jd ksJdS) NrrOFrPr$rrQrr[n_features_in_rB)r{rrrr\rr rrr rrrrrar)rrrrjrmrurvs rnrrs< )   " "Cn%%IYy++----...I s)Q 00A!)Q//AN##7 JJIJ & & KKAAIK 6 6!)Q//Ay"233333 MM!Q 9. / ////  #qwqz 1 1 1 1 1 1rpcttjd}t|}t |d}|d|df}t ||}d} ||ddS#t$r(tfd|DsYddSdwwxYw)Nrr$rrQ)z1requires y to be passed, but the target y is Nonezz(check_requires_y_none..?s-??c3#b''>??????rp) r{rrrr\rrrr]rF)rrrrjrmruexpected_err_msgsrds @rnrr)s )   " "Cn%%IYI s)Q 00A!)Q//A  a ????->????? H      s-B B7B22B7c t|}|jjp |jj}|r|jrdSt jd}t|}t|d| vr| dd}| |df}t||}t|r| |}n|dd| }t!||}d | } |||t'|d s J| |j|jd ks J| gd} |ddd gf} d} t-j| } d|jd d} | D]} t'|| st1|| }| dkrt3||}t5t6| | || 5|| dddn #1swxYwYt'|dsdSt|}t9|r+|||t j|n||||j|jd ksJt5t6| 5|| |ddddS#1swxYwYdS)NrrOFrPrirrrr[z`{name}.fit()` does not set the `n_features_in_` attribute. You might want to use `sklearn.utils.validation.validate_data` instead of `check_array` in `{name}.fit()` which takes care of setting the attribute.rrarB)rrrrra `{name}.{method}()` does not check for consistency between input number of features with {name}.fit(), via the `n_features_in_` attribute. You might want to use `sklearn.utils.validation.validate_data` instead of `check_array` in `{name}.fit()` and {name}.{method}()`. This can be done like the following: from sklearn.utils.validation import validate_data ... class MyEstimator(BaseEstimator): ... def fit(self, X, y): X, y = validate_data(self, X, y, ...) ... return self ... def {method}(self, X): X = validate_data(self, X, ..., reset=False) ... return X z'X has 1 features, but \w+ is expecting z features as inputrr)rrrrrrW)rMrrr rr{rrrr\rr rrr rrrrrrartextwrapdedentrgrr[r]rrr )rrrlis_supported_X_typesrrjrmrurvrrX_badrIrcallable_methods rnrrCs N # #D?6U$/:U 4#5 )   " "Cn%%IYy++----...I A ''A!)Q//AI7 JJIJ & & KKAAIK 6 6!)Q//A VV&&  MM!Q 9. / /88888  #qwqz 1 1 17 1 1 1M aaa!fIEG(og&&G SQWQZ S S SC # #y&))  !)V44 W  %o;;;O  c7>>tF>+S+S    # # OE " " " # # # # # # # # # # # # # # # 9m , ,n%%IY$aBIaLL9999a###  #qwqz 1 1 1 1  # & & &((eQ'''((((((((((((((((((s$ H22H6 9H6 LLLc t|dsJd|dd}t|}t|jtt dfs J|t|jts J|t|jtt dfs J|t|j tt dfs J|t|j tt dfs J|t|jts J|t|jt"s J|t|jt"s J|t|jt"s J|t|jt"s J|t|jt"s J|t|jjt"s J|t|jjt"s J|t|jjt"s J|t|jjt"s J|t|jjt"s J|t|jjt"s J|t|jjt"s J|t|jjt"s J|t|jjt"s J|t|jjt"s J|t|jj t"s J|t|jj!t"s J|t|jj"t"s J|t|jj#t"s J|t|jj$t"s J|t|jjt"s J||j{t|jj%t"s J|t|jj&t"s J|t|jj't"s J||j )t|j j%t"s J||j )t|j j(tRs J|dSdS)z$Check that estimator tags are valid.__sklearn_tags__rdz does not have `__sklearn_tags__` method. This method is implemented in BaseEstimator and returns a sklearn.utils.Tags instance.ztTag values need to be of a certain type. Please refer to the documentation of `sklearn.utils.Tags` for more details.N)*rrMrtrmrr4rrKrrHrrJrrLrrIrrrrrrr one_d_labels two_d_labelsrrrrrr r rYrrr three_d_arrayrrrrr=)rrjrrls rnrzrzs 90 1 1 ST S S S  V  I  D d)Cd+< = =FFwFFF d& 3 3<>w>>> do/ 6 6????? do,d 3 3<. s<<.Os! > > >q "q " " > > >rpzHFeature names unseen at fit time: - another_prefix_0 - another_prefix_1 rz3Feature names seen at fit time, yet now missing: - rc"i|] \}}d|v || S)early_stoppingrArs rnrz.Zs3 C s " " U " " "rpc3K|]}|duV dS)TNrA)r<rs rnr>z;check_dataframe_column_names_consistency.._s& L L5$ L L L L L LrpzBThe feature names should match those that were passed during fit. z did not raiserrrrW)5rr"rQrMrrr rr{rrrr\rrrrrr r rrrur+filterwarningsrwrrr]rtrrrrXrWrrrrrgrr`rGrrrFrrescaper[rr r)rrr#rlrirrjX_origrmrnnamesrurvrrrrkrc invalid_namesrearly_stopping_enabled invalid_nameadditional_messagerj expected_msgrs rn(check_dataframe_column_names_consistencyrs      V    N # #D?6U$/:U 4#5 )   " "Cn%%IY ZZXZ & &F &y& 9 9F"LIz H<<% *;*;<<< = =E VU 77AI7 JJIJ & & KKAAIK 6 6!)Q//A  " " 9       a 91 2 2  7    i12: > >>>>  & , 6 6 6 6y2E:::!+Kz** K'';+?+? +K+K' )? @ @ Q Q Q Q   M88y&))  !)V44 W  %o;;;Ofo67777" 6  $ & &    #=$      F1III                tttTU > >E*,=,= > > >&  "1"I U3uQRRy>> U U U  M#..006688F ! L LFMMOO L L LLL,9,,( ( Q 5 AAy $! $ $  *  LD&,48O8O8O  u                y-00 4J  .))  # # (illG  ! !!Q ! 8 8 8 8  ! !!Q ' ' ' Jl 3 3 3 , ,  ! !% + + + , , , , , , , , , , , , , , ,3,,sP!'4F''F+.F+)LL L  P77P; >P; S00S4 7S4 cxt|}|jjr|jrdSt dgdgdgddd\}}t |}t|}t||}|j d}t||}|tvrMtj tj|tj|f}|ddddfxxdzcc<||| }d t|D} t!t"d 5|| ddddddn #1swxYwY|| } | Jt'| tjsJ| jt,ksJt/d | DsJt'|t0r|dj d} n |j d} t3| | ksJd| dt3| dS)Nr)rrrrrrrBrcg|]}d|S)featurerAr{s rnrkz;check_transformer_get_feature_names_out..s???mmm???rpz'input_features should have length equalrWc3@K|]}t|tVdSr;)rtr)r<rs rnr>z:check_transformer_get_feature_names_out..s,CCz$$$CCCCCCrp Expected  feature names, got )rMrrrr"r6rtrrrr\rNr{rr|rr[r]rrtrrrXr&r~r/) rrrlrurvrrnr X_transforminput_featuresfeature_names_outn_features_outs rn'check_transformer_get_feature_names_outr~s $ % %D ? &$*< III&    DAq &&q))A())K!+q11AJ[!!! B """ U2:a=="*Q--/ 0 33Q36 a ++A+44K??U:->->???N  "K L L L??)).1*=>>>???????????????$99.II  ( ( ( ' 4 4444  "f , , , , CC1BCCC C CCCC+u%%.$Q-a0$*1- .000O>OOs;L7M7MOO 10000s<E''E+.E+c ddl}n#t$rtdwxYwt|}|jjr|jrdStdgdgdgddd\}}t |}t|}t||}|j d }t||}|tvrMtjtj|tj|f}|dddd fxxdzcc<d t%|D} ||| d } | | | } dt%|D} t)t*d5|| dddn #1swxYwY|} || }t/| |t1| t2r| dj d }n | j d }t5| |ksJd|dt5| dS)Nrrwr)rrrrrrBcg|]}d|SrrAr{s rnrkzBcheck_transformer_get_feature_names_out_pandas..===a a ===rpFr|rcg|]}d|S)badrAr{s rnrkzBcheck_transformer_get_feature_names_out_pandas..sBBB1Y1YYBBBrpz0input_features is not equal to feature_names_in_rWrr)rr"rQrMrrrr"r6rtrrrr\rNr{rr|rr r[r]rrWrtr~r/)rrr#rlrurvrrnrfeature_names_indfrinvalid_feature_namesfeature_names_out_defaultfeature_names_in_explicit_namesrs rn.check_transformer_get_feature_names_out_pandasrs      V    $ % %D ? &$*< III&    DAq &&q))A())K!+q11AJ[!!! B """ U2:a=="*Q--/ 0 33Q36 a ==5+<+<=== a!1 > >B++B"+55KCBj0A0ABBB  "T U U UAA))*?@@@AAAAAAAAAAAAAAA!, A A C C&1&G&G''#02QRRR+u%%.$Q-a0$*1- %&&.888W>WWs;T7U7UWW 98888s!F22F69F6c Ztjd}|d}|ddd}t ||}t |}|d}|rr|j }t|t|z }t|t|z } d|d |d | } ||ks J| td d i} gd } |D]} |j | }|dkrtd|Dr/td|Drtd| d|dd| d|d}|d| d} t|}|jd i| | i| D]}t!||s|d| d} t#t$|| 5|jjs |jjrt-|||nt-||||dddn #1swxYwYd|D}|D]} t/|}n#t0$rYwxYw|jd i| |i| D]}t!||s|d| d} t#t$|| 5|jjs |jjrt-|||nt-||||dddn #1swxYwY̐dS)NrrrrrkFr.z>Mismatch between _parameter_constraints and the parameters of z%. Consider the unexpected parameters z% and expected but missing parameters BadTyperA)rrrtrrc3`K|])}t|to|jtkV*dSr;)rtrEr4r r<rls rnr>z)check_param_validation.. sL   z8 , , LH1L      rpc3`K|])}t|to|jtkV*dSr;)rtrEr4r rs rnr>z)check_param_validation..sL   z8 , , HD1H      rpzThe constraint for parameter rzc can't have a mix of intervals of Integral and Real types. 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.zc does not have a valid type. If any Python type is valid, the constraint should be 'no_validation'.rc,g|]}t|SrA)rArs rnrkz*check_param_validation..8s QQQzz22QQQrpa  does not have a valid value. Constraints should be disjoint. For instance [StrOptions({'a_string'}), str] is not a acceptable set of constraint because generating an invalid string for the first constraint will always produce a valid string for the second constraint.)r{rrrrrrMrrrcrr4rFr]rr rr[r?rrnrorgr@r )rrrrurvrlestimator_paramsvalidation_paramsunexpected_paramsmissing_paramsrparam_with_bad_type fit_methodsr constraintsrrjrrl bad_values rncheck_param_validationrs0 )   " "C  !!A Aqr ""A!.!44A N # #D%00e0<<AACC >*AFFHH 122S9I5J5JJ-..5F1G1GG 4T 4 44E 4 4#1 4 4  !$4444g4442$y"b1133HHHK&S9S9 $;JG / ) )    )       )      $ $$$$$  VUUDUUU K K# K K K  .))   AA ,?@AAA! 5 5F9f-- DDDDD  -UGLLL 5 5#05D4D4Q5.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&11""!+"""1PPP99'498H8U92 622155552 6221a888 999999999999999 9 9oS9S9s8*A II I I00 I=<I=A L L #L c t|}|jjr|jrdStjd}t|}|d t|  | ddd t|  t| fd} fd}||d}| D]\}t|}t|s%||} tvr| d} |d ||} tvr| d} t#| | dS) Nrrrrrkctvr*|S|Sr;)rNrr)r=rurrvs rnfit_then_transformz6check_set_output_transform..fit_then_transformisQ & & &771a==**1a00 0wwq!}}&&q)))rpc0|Sr;)rt)r=rurvs rnrtz1check_set_output_transform..fit_transformns  A&&&rp)rrtrr)rMrrrr{rrrrrrrr\rrrNrrU) rrrlrrrrttransform_methodstransform_methodX_trans_no_settingX_trans_defaultrurvs ` @@rncheck_set_output_transformrYs $ % %D ? &$*< )   " "C())K  !!A!"2A66A Aqr ""A!"2A66A[!!!******* ''''''(&#4"9"9";";JJK(( {D))  --k:: & & &!3A!6 333**;77 & & &-a0O %%7IIII#JJrpc*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|VdSr;)r)r<methrs rnr>z-_output_from_fit_transform..s- G G$7; % % G G G G G Grp)rrzfit_transform/dfzfit_transform/arrayrt)r&rrNrrrrt) rrrurrvoutputscasescasedata_fitdata_transformrrcr}s ` rn_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 Nrpc|\}} |\} } || sJ|dr|nd} ||| | } || | dS#t$r}t|d|d||d}~wwxYw)aCheck if the generated DataFrame by the transformer is valid. The DataFrame implementation is specified through the parameters of this function. Parameters ---------- name : str The name of the transformer. case : str A single case from the cases generated by `_output_from_fit_transform`. index : index or None The index of the DataFrame. `None` if the library does not implement a DataFrame with an index. outputs_default : tuple A tuple containing the output data and feature names for the default output. outputs_dataframe_lib : tuple A tuple containing the output data and feature names for the pandas case. is_supported_dataframe : callable A callable that takes a DataFrame instance as input and return whether or E.g. `lambda X: isintance(X, pd.DataFrame)`. create_dataframe : callable A callable taking as parameters `data`, `columns`, and `index` and returns a callable. Be aware that `index` can be ignored. For example, polars dataframes would ignore the idnex. assert_frame_equal : callable A callable taking 2 dataframes to compare if they are equal. rNr}rz, does not generate a valid dataframe in the z] case. The generated dataframe is not equal to the expected dataframe. The error message is: )rr)rrroutputs_defaultoutputs_dataframe_libis_supported_dataframecreate_dataframeassert_frame_equalrfeature_names_defaultdf_transfeature_names_dataframe_libexpected_indexexpected_dataframerds rn_check_generated_dataframersJ&5"G ",A)H) ! !( + ++++ #mmD11;UUtN))4N8%788888  4 4 4 401 4 4    s A A5A00A5c vt|}|jjr|jrdStjd}t|} |d} t|| } | ddd} t|| } t| dt| jdD} d t| jdD} || | | }t| d }t!||| || }|d kr2t| | }t#}nt| }t%|} |5t!||| || }dddn #1swxYwYnP#t&$rC}|}t+|}|d|vsd|vs J|Yd}~dSd}~wwxYw|D]$}t-||| |||||||%dS)aCheck that a transformer can output a DataFrame when requested. The DataFrame implementation is specified through the parameters of this function. Parameters ---------- name : str The name of the transformer. transformer_orig : estimator The original transformer instance. dataframe_lib : str The name of the library implementing the DataFrame. is_supported_dataframe : callable A callable that takes a DataFrame instance as input and returns whether or not it is supported by the dataframe library. E.g. `lambda X: isintance(X, pd.DataFrame)`. create_dataframe : callable A callable taking as parameters `data`, `columns`, and `index` and returns a callable. Be aware that `index` can be ignored. For example, polars dataframes will ignore the index. assert_frame_equal : callable A callable taking 2 dataframes to compare if they are equal. context : {"local", "global"} Whether to use a local context by setting `set_output(...)` on the transformer or a global context by using the `with config_context(...)` Nrrrrrkcg|]}d|SrrAr{s rnrkz9_check_set_output_transform_dataframe..%rrprBcg|]}d|S)rrAr{s rnrkz9_check_set_output_transform_dataframe..&s 4 4 4Q[Q[[ 4 4 4rprrrlocal)transform_outputz% output does not support sparse data.z.The transformer outputs a scipy sparse matrix.)rMrrrr{rrrrrrrr\rrrrrrr] capitalizerr)rr dataframe_librrrcontextrlrrrurvrrrtransformer_defaultrtransformer_dfcontext_to_use outputs_dfrdcapitalized_lib error_messagers rn%_check_set_output_transform_dataframersJ $ % %D ? &$*< )   " "C())K  !!A!"2A66A Aqr ""A!"2A66A[!!!==5+<+<=== 4 4% "3"3 4 4 4E  !%5U C C CB ,,77)7LL01DdArSTUUO'{++666OO${++'GGG   T T3ND!RQRSSJ T T T T T T T T T T T T T T T '2244A  E E E V V?=PPP QPP   "    D ! t  "      s<GF6* G6F::G=F:>G H 8H  Hc  ddln#t$rtdwxYwt||dfdfdjj|dS)Nrz0pandas is not installed: not checking set outputrc.t|jSr;rtr )rur#s rnr{z<_check_set_output_transform_pandas_context..WAr|)D)Drpc6||d|S)NF)r}rr)r )rur}rr#s rnr{z<_check_set_output_transform_pandas_context..Xs$2<< wU%4@4 4 rprrrrr)rr"rQrtestingr)rrrr#s @rn*_check_set_output_transform_pandas_contextrMsK KKKIJJJK* DDDD    :8      s"c(t||ddSNrrrrs rn!check_set_output_transform_pandasr`.t5EwOOOOOrpc(t||ddSNglobalrrs rn$check_global_output_transform_pandasrd.t5ExPPPPPrpc  ddlddlm}n#t$rt dwxYwfd}t ||dfd|||dS)Nr)rz0polars is not installed: not checking set outputct|tjr|}||dS)Nr)schemaorient)rtr{rrr )rur}rpls rnrzD_check_set_output_transform_polars_context..create_dataframeos> grz * * 'nn&&G||Age|<<.yrrpr)rpolars.testingrr"rQr)rrrrrrs @rn*_check_set_output_transform_polars_contextrhsK5555555 KKKIJJJK===== * DDDD)-s (c(t||ddSrrrs rn!check_set_output_transform_polarsrrrpc(t||ddSrrrs rn(check_global_set_output_transform_polarsrrrpctjd}t|}t |d}t |d|\}}t ||}|dvrtj|}t|drtj |d<t|r| |}n| dd | }t||}|}|d |||t|d r|||jjrJt)||dS)aCheck that estimators able to do inplace operations can work on read-only input data even if a copy is not explicitly requested by the user. Make sure that a copy is made and consequently that the input array and its writeability are not modified by the estimator. rr$r)rmrnrr)Lasso ElasticNetMultiTaskElasticNetMultiTaskLassomissing_valuesrVrrr[F)writerN)r{rrrr\r"rrrrr rrrrsetflagsrrflags writeablerT) rrrrjrmrurcrvX_copys rncheck_inplace_ensure_writeablersm )   " "Cn%%IYI  ac J J JDAq!)Q//A OOO  a y*++&$I7 JJIJ & & KKAAIK 6 6!)Q//A VVXXFJJUJ MM!Qy+&&Aw    Avrpct|}t|j}dddtjddgdgigg}|D]%fd|D}|d i|}|jd i|&dS) z3Check that init or set_param does not raise errors.r&g@ helloworldrr+rBci|]}|SrArArs rnrzCcheck_do_not_raise_errors_in_init_or_set_params..s333Sc5333rpNrA)r4rrr{rr )rrrrsmoke_test_valuesrr=rs @rnrrs^$$I y ! ! ,FS,#s0D0Dqc2rR"%%3333F333 i%%*%% $$$$$$%%rpcPt|}t|tddddd\}}d|}t j|}t td| 5|||d d d d S#1swxYwYd S) zCheck that if the classifier has tags.classifier_tags.multi_class=False, then it should raise a ValueError when calling fit with a multiclass dataset. This test is not yielded if the tag is not False. r$rrBr)rmr ron_clusters_per_classrra The estimator tag `tags.classifier_tags.multi_class` is False for {name} which means it does not support multiclass classification. However, it does not raise the right `ValueError` when calling fit with a multiclass dataset, including the error message 'Only binary classification is supported.' This can be achieved by the following pattern: y_type = type_of_target(y, input_name='y', raise_unknown=True) if y_type != 'binary': raise ValueError( 'Only binary classification is supported. The type of the target ' f'is {{y_type}}.' ) rz(Only binary classification is supported.rN) rr\r#rrgrhr[r]r)rrrjrurvrs rnrrs n%%IY     DAq       og&&G Dg     as7BB"B)rr)NNN)rrrrr;)rrr.r/)rrrrrr)rrrr8)NF) rrrrrNrRrOrSrPr8)NrF)Nr)F)Fr)T(r __future__rrrrgru contextlibrrr functoolsrrinspectrnumbersr r typingr r rnumpyr{scipyr scipy.statsr sklearn.baserrrrrrrrrrrrcrbaserrrrr datasetsr!r"r#r$r% exceptionsr&r'r(r)linear_model._baser*metricsr+r,r-metrics.pairwiser.r/r0model_selectionr1r2r3model_selection._validationr4rr5 preprocessingr6r7utilsr8utils._array_apir9r:r;r<r>rutils._param_validationr?r@rArC_missingrD_param_validationrErFrG_tagsrHrIrJrKrLrM_test_common.instance_generatorrNrOrPrrQrRrSrTrUrVrWrXrYrZr[r\fixesr] validationr^r_r`rsrorrrrrrrrrrr-r)r7rJrrBrfrwryrrr^rrrrrrrrrrrrrLrrrrwrrr rerrrrrrrrrrrrrrrr}rrrrrrrrrrrrrrrZrrrrrrrrrrrrrrrrrrrrrr~r|rrrrrxr{rrrrrrr r rrrrr r rrrrzryrrrrrrrrrrrrrrrrrrArprnr5sSWW#""""" """"""$$$$$$$$""""""""$$$$$$$$                             766666CCCCCCCCCCLLLLLLLLLLNNNNNNNNNN555555$$$$$$11111111"""""" 655555 ######DDDDDDDDDD                            ('''''HHHHHHHHHH    ---86-6-6-r,9,9,9^2220'''> 6 6 6222$    ;%;%;%;%|QQQ59+/  *"*"*"*"*"\GK22222F48+/ 222222p.2 ^^^^^^B#+#',Jx(($/J01148t$ #(   {48&,/6 ${{{{{  {|    WWWWWWWW*)))$'''B-(((  )( L  nInInInIj  "5 5 5 p3;3;3;lOOORRR ,-(((+ + )(+ \=***//+*/:=*** 5 5+* 5-(((!@!@)(!@H-(((CLCL)(CLLRRR   %P%P%PP=+6777''87'T   (<<<-(((55)(5p-(((11)(1.<<<$-(((!R!R)(!RH-((()))()X%%%P<$-(((000)(0"-(((BB)(B"-((( ! !)( ! -((( * *)( *X1X1X1v>>>@%%%P:444,D-((("")(".-(((-1-1)(-1`   2G2G2G2Gj-(((,,)(,8-(((:N:N)(:Nz-((()(2-(((737373)(73v-((( - -)( --(((XX)(X.-(((! ! )(! H:CGOGOGOGOT@@@,EPEPEPEPPLLL6-(((&6&6)(&6R-(((**)(*Z-(((Q Q )(Q h-(((++)(+\-((( * *)( *-((( , ,)( ,-(((,,)(,$...:-(((77)(7D777tMMMD-(((;;)(;6-(((*//,/,/,)(/,d 1 1 1 -((()/)/)()/X-((()(*-(((%%)(%P-(((  )( B-((()(@-((((()((D-(((QQ)(Q0-(((QQ)(Q-(((+;+;)(+;\RRROOO|K|K|K~   *48 ))))X-(((11)(1@-((()(.-(((**)(*<-(((OO)(O-(((5050)(50p-((()(&-((()G)G)()GX(P(P(PV999x$$$N22244-(((_(_()(_(D2P2P2Pj,J,J,J,Z-P-P-P`6X6X6Xro9o9o9d-J-J-J`***Z:::zW W W t&PPPQQQ0PPPQQQ-(((**)(*Z % % % %%%%%rp