-i LSrSSKrSSKrSSKrSSKrSSKrSSKrSSKJr SSK r SSK r SSK r SSK r SSKrSSKrSSKrSSKJrJr SSKJr SSKJr SSKJr SSKJr SSKJr SS KJrJr SS KJ r J!r!J"r"J#r# SSK$r$SSK%r&SSK'r(SS K)J*r+ SS K)J,r,J-r-J.r.J/r/ SSK0r0SS K1J2r2J3r3J4r4J5r5J6r6 SSK7J8r8 SSK9J:r:J;r;Jr> SSK?J@r@JArAJBrB /SQrC\RRrES\F4SjrG"SS5rHSASjr*SBSjrISCSjrJSrK\8"S5 SrLSSKOrO\ORR\:SS9rR\ORR\<"5SS9rT\ORR\$RR(+S S9rW\ORR\L(+S!S9rX\ORR\ RS":HS#S9rZ\ORR\K"5(+S$S9r[S%r\SDS&jr]"S'S(5r^SES)jr_SDS*jr`S+raSFS,jrbS-rcS.rdS/reS\f"54S0jrgSSSSSSS\f"54S1jrhSGS2jriSHS3jrjSIS4jrk"S5S6\R5rm"S7S85rn"S9S:5ro"S;S<5rpS=rqS>rrS?rsS@rtg!\M\N4a SrLGNf=f!\Ma Nf=f)JzTesting utilities.N) defaultdict namedtuple)Iterable) dataclass) context_diffwraps) signature)chaingroupby)STDOUTCalledProcessErrorTimeoutExpired check_output)assert_allclose)assert_almost_equalassert_array_almost_equalassert_array_equalassert_array_less)ClassifierTags RegressorTagsTags TargetTagsTransformerTags)_check_array_api_dispatch) _IS_32BITVisibleDeprecationWarning#_in_unstable_openblas_configuration)check_classification_targets) check_arraycheck_is_fitted check_X_y)SkipTestrrrrr'assert_run_python_script_without_outputc[U[5(a9[U[5(a$URn[ SR US95e[U5(a[US9"U5$[US9$)aContext manager and decorator to ignore warnings. Note: Using this (in both variants) will clear all warnings from all python modules loaded. In case you need to test cross-module-warning-logging, this is not your tool of choice. Parameters ---------- obj : callable, default=None callable where you want to ignore the warnings. category : warning class, default=Warning The category to filter. If Warning, all categories will be muted. Examples -------- >>> import warnings >>> from sklearn.utils._testing import ignore_warnings >>> with ignore_warnings(): ... warnings.warn('buhuhuhu') >>> def nasty_warn(): ... warnings.warn('buhuhuhu') ... print(42) >>> ignore_warnings(nasty_warn)() 42 z'obj' should be a callable where you want to ignore warnings. You passed a warning class instead: 'obj={warning_name}'. If you want to pass a warning class to ignore_warnings, you should use 'category={warning_name}') warning_namecategory) isinstancetype issubclassWarning__name__ ValueErrorformatcallable_IgnoreWarnings)objr(r&s I/var/www/html/venv/lib/python3.13/site-packages/sklearn/utils/_testing.pyignore_warningsr4Jsn8#tC!9!9||  78>v[R"5 [R"STR5 T"U0UD6sSSS5 $!,(df  g=f)Nignore)r9catch_warnings simplefilterr()argskwargsfnrAs r3wrapper)_IgnoreWarnings.__call__..wrappers<((*%%h >4*6*+**s )A  Ar)rArKrLs`` r3__call___IgnoreWarnings.__call__s" r +  + r5c"/nUR(aURS5 UR[RSLaURSUR-5 [ U5R nU<SSRU5<S3$)Nz record=Truer9z module=%r(z, ))r:appendr=r;r<r*r-join)rArInames r3__repr___IgnoreWarnings.__repr__sf << KK & < RuntimeErrorr=filters_filters showwarning _showwarningr9rHr(rAs r3 __enter___IgnoreWarnings.__enter__sm ==6=> >  ,, #}}Q/  LL44h 6r5cUR(d[SU-5eURURlUR URl/URSS&g)Nz%Cannot exit %r without entering first)r>rYr[r=rZr]r\r?)rAexc_infos r3__exit___IgnoreWarnings.__exit__sI}}FMN N#}} #'#4#4   r5)r>r[r=r:r]r(r?N) r- __module__ __qualname____firstlineno____doc__rBrNrVr_rc__static_attributes__r5r3r1r1vs  ! 27r5r1Tc  /n[R"U5[R"U5pURUR/nUc2UVs/sHo[R:XaSOSPM n n[ U 5n[ UUUUUUUS9 gs snf)adtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) Ng-C6?Hz>)rtolatol equal_nanerr_msgverbose)np asanyarraydtypefloat32maxnp_assert_allclose) actualdesiredrnrorprqrrdtypesrurtolss r3rrsjFmmF+R]]7-CGllGMM *F |DJKF5"**,$6FK5z  Ls !B c[RRU5(a[RRU5(aUR5nUR5nUR 5 UR 5 [ UR UR US9 [ URURUS9 [URURX#US9 g[RRU5(d0[RRU5(d [XX#US9 g[S5e)a@Assert allclose for sparse and dense data. Both x and y need to be either sparse or dense, they can't be mixed. Parameters ---------- x : {array-like, sparse matrix} First array to compare. y : {array-like, sparse matrix} Second array to compare. rtol : float, default=1e-07 relative tolerance; see numpy.allclose. atol : float, default=1e-9 absolute tolerance; see numpy.allclose. Note that the default here is more tolerant than the default for numpy.testing.assert_allclose, where atol=0. err_msg : str, default='' Error message to raise. )rq)rnrorqzGCan only compare two sparse matrices, not a sparse matrix and an array.N) spsparseissparsetocsrsum_duplicatesrindicesindptrrdatar.)xyrnrorqs r3assert_allclose_dense_sparsers2 yy!!3!3A!6!6 GGI GGI  199aiiA188QXXw?TgN YY   " "299+=+=a+@+@4GD U  r5cLSUR5;aURUS9 gg)aZSet random state of an estimator if it has the `random_state` param. Parameters ---------- estimator : object The estimator. random_state : int, RandomState instance or None, default=0 Pseudo random number generator state. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. random_state)rN) get_params set_params) estimatorrs r3set_random_stater"s+--//,70r5c:SSKng![[4a gf=f)NrTF)numpydoc ImportErrorAssertionError)rs r3 _is_numpydocr2s(  (s Fzskipped on 32bit platforms)reasonz+OpenBLAS is unstable for this configurationzjoblib is in serial modez>6 :;;FGG>+ & & MM+ & ' P  MM@;N O Ps:>*A-,A-c.\rSrSrSrSSjrSrSrSrg) TempMemmapiz9 Parameters ---------- data mmap_mode : str, default='r' cX lXlgN) mmap_moder)rArrs r3rBTempMemmap.__init__s " r5cR[URURSS9uolU$)NT)r return_folder)create_memmap_backed_datarr temp_folder)rAdata_read_onlys r3r_TempMemmap.__enter__s)+D IIt, ((r5c.[UR5 gr)rr)rAexc_typeexc_valexc_tbs r3rcTempMemmap.__exit__st''(r5)rrrN)r) r-rerfrgrhrBr_rcrirjr5r3rrs )r5rc([R"SS9n[R"[R "[ USS95 [R"US5n[R"X5 [R"XAS9nU(dUnU$XS4nU$)z^ Parameters ---------- data mmap_mode : str, default='r' return_folder : bool, default=False sklearn_testing_)prefixT)rzdata.pkl)r) tempfilemkdtempatexitregister functoolspartialroprTjoblibdumpload)rrrrfilenamememmap_backed_dataresults r3rrs""*<=K OOI%%nkMNww{J/H KKXC"/  M7I5V  Mr5c[U5RnUR5VVs/sH/up4URUR UR 4;dM-UPM1 nnnU(aZUR5Vs/sH+nURUR :XdMURPM- nn[U5S:XaSnXQ4$U$![a /s$f=fs snnfs snf)z!Helper to get function arguments.rN) r parametersr.itemskindVAR_POSITIONAL VAR_KEYWORDvaluesrUlen)functionvarargsparamskeyparamrIs r3 _get_argsrs8$// !,,. (JC ::e22E4E4EF F (     (zzU111 EJJ(  w<1 G} %     s(C,CC<CC CCcJ/n[R"U5nU(aURUR5 URnX0R:wa#URUSUR S55 URUR5 SR U5$)zGet function full name. Parameters ---------- func : callable The function object. Returns ------- name : str The function name. N.)inspect getmodulerSr-rffindrT)funcpartsmodulequalnames r3_get_func_namers| E   t $F  V__%  H==  X2 c 234 LL 88E?r5c ^SSKJn /nTc/OTm[U5nURS5(aURS5(aU$[R "U5(aU$UR S5SS;aU$UR S5S S :XaU$[[U4S j[U555n[U5S:aUSS :XaURS 5 Ucr/n[R"S S9 [R"S[5 UR!U5nSSS5 [U5(a[)SU<SUS<35e/n USHupn U R+5(d{SU ;a7U SU R-S5SSR+5(a UUSU --/- nO>U R/5R1S5(aUUSU R35--/- nSU ;dMU R%U R S5SR+S55 M [U5S:aU$[[U4SjU 55n /n [5[7[U 5[U555H%nXnX:wdMU SU<SXn<S X<3/- n O [U5[U 5:aU S!U[U 5-/- n O-[U5[U 5:aU S"U [U5-/- n [U 5S:Xa/$SSKnSSKnUR=U 5R?5nUR=U5R?5nU S#/- n U RAS$URCUU555 URAU 5 SU-/U-nU$![amnS[#U5;a9[#U5R S5SSn USU3/U -- nUsSnAsSSS5 $UR%[#U55 SnAGNSnAf[&a)nXES-[#U5-/- nUsSnAsSSS5 $SnAff=f!,(df  GN3=f)%a>Helper to check docstring. Parameters ---------- func : callable The function object to test. doc : str, default=None Docstring if it is passed manually to the test. ignore : list, default=None Parameters to ignore. Returns ------- incorrect : list A list of string describing the incorrect results. r) docscrapeNzsklearn.zsklearn.externalsr) setup_moduleteardown_moduleestimator_checksc>UT;$rrjrrFs r3,check_docstring_parameters..s AVOr5rAT)recorderrorz"potentially wrong underline length z In function: z parsing error: z Error for z: Parameters:z9 There was no space between the param name and colon (%r)z6 Parameter %r has an empty type spec. Remove the colon*z` c>UT;$rrjrs r3rr>s qr5z\There's a parameter name mismatch in function docstring w.r.t. function signature, at index z diff: z != zbParameters in function docstring have less items w.r.t. function signature, first missing item: %sz`Parameters in function docstring have more items w.r.t. function signature, first extra item: %sz Full diff:c3B# UHnUR5v M g7fr)strip.0lines r3 -check_docstring_parameters..isRD Rs)"rrr startswithrisdatadescriptorsplitlistfilterrrremover9rGrH UserWarning FunctionDocstrrS ExceptionrYrindexrstripendswithlstriprangemindifflibpprintpformat splitlinesextendndiff)rdocrFr incorrect func_nameparam_signaturerecordsexpmessage param_docsrUtype_definition param_docir r param_docs_formattedparam_signature_formatteds ` r3check_docstring_parametersrso"#I>RvFt$I    + +y/C/C00%%sB#FFsA"446";Yt_MNO ?aOA$6&$@v& {  $ $D 1  ! !'; 7 !++D12 w<<iLM MJ,/ ,=(y$$&&d{t$5djjo6rs;AACCQTXXY '',,N{{}&&  d?   djjoa066t< =%->, 9~f6 CDJ G 3s:O(<= >   . &'(:JMK G ? ?c*o- :c*o. /   _ J / 8_-. /   7|q !>>*5@@B & ? J J L ~G NNMM";=QR W!9,- 9I  )73s8C"#hnnT22A6GM)"=!>!HHI$$2 1s3x(( !*<.s3OHsz ) rrrrrrrTr r$)docstrings_groupedmsg_diffref_str ref_group docstringgroupdiffstarts r3 _get_diff_msgr6sHGI.446 y  G   U #  !Y5z    BGGT"1X''#DH).s&(CuE (CszThe z of z' is inconsistent between rzChecking was skipped for z: z' as they were not found in all objects.)rsumrrrSresearchrtextwrapfillrkeysr6rTr9r) items_docs type_or_descsection n_objectsdescr_regex_pattern ignore_typesskippedr!r.objs not_matchedr2r3msgr/ obj_groupss r3_check_consistency_itemsrNsLG)3)9)9);% &8&?&?&AB&AdD &AB Ci O NN9 % ] */BK$6$<$<$> yy!4@@&&u-%?mm)'#2,r)E+=>Q=RRSU%S)) 1 1i6O  #((* +a /$%78H &(:(A(A(C&J--|nD"b D%,a)C OC % %=*<> 'y7)<( ( ;CsF. c ^SSKJn [S/SQ5mU4Sjn 0U "XSS5EU "X4SS 5EU "XVS S 5En [5n UHn [R "U 5(d6[R "U 5(d[R"U 5(a*U "[R"U 55XR'M~[S [U 5S 35e [U5nU R5Hunn[S5n[S5nU R5HunnUUHunnn[UU5(dMSR!UR#5R%55nSR![&R("SU555nUUUR+U5 UUUR+U5 M M [-USUUUS9 [-USUUUS9 M g)a+ Check consistency between docstring parameters/attributes/returns of objects. Checks if parameters/attributes/returns have the same type specification and description (ignoring whitespace) across `objects`. Intended to be used for related classes/functions/data descriptors. Entries that do not appear across all `objects` are ignored. Parameters ---------- objects : list of {classes, functions, data descriptors} Objects to check. Objects may be classes, functions or data descriptors with docstrings that can be parsed by numpydoc. include_params : list of str or bool, default=False List of parameters to be included. If True, all parameters are included, if False, checking is skipped for parameters. Can only be set if `exclude_params` is None. exclude_params : list of str or None, default=None List of parameters to be excluded. If None, no parameters are excluded. Can only be set if `include_params` is True. include_attrs : list of str or bool, default=False List of attributes to be included. If True, all attributes are included, if False, checking is skipped for attributes. Can only be set if `exclude_attrs` is None. exclude_attrs : list of str or None, default=None List of attributes to be excluded. If None, no attributes are excluded. Can only be set if `include_attrs` is True. include_returns : list of str or bool, default=False List of returns to be included. If True, all returns are included, if False, checking is skipped for returns. Can only be set if `exclude_returns` is None. exclude_returns : list of str or None, default=None List of returns to be excluded. If None, no returns are excluded. Can only be set if `include_returns` is True. descr_regex_pattern : str, default=None Regular expression to match to all descriptions of included parameters/attributes/returns. If None, will revert to default behavior of comparing descriptions between objects. ignore_types : tuple of str, default=tuple() Tuple of parameter/attribute/return names to exclude from type description matching between objects. Examples -------- >>> from sklearn.metrics import (accuracy_score, classification_report, ... mean_absolute_error, mean_squared_error, median_absolute_error) >>> from sklearn.utils._testing import assert_docstring_consistency ... # doctest: +SKIP >>> assert_docstring_consistency([mean_absolute_error, mean_squared_error], ... include_params=['y_true', 'y_pred', 'sample_weight']) # doctest: +SKIP >>> assert_docstring_consistency([median_absolute_error, mean_squared_error], ... include_params=True) # doctest: +SKIP >>> assert_docstring_consistency([accuracy_score, classification_report], ... include_params=["y_true"], ... descr_regex_pattern=r"Ground truth \(correct\) (labels|target values)") ... # doctest: +SKIP r)NumpyDocStringrI)rr arg_namecd>U(aUSLa[SUSUS35eUSLa0$UT"XU50$)NTz The 'exclude_z-' argument can be set only when the 'include_z' argument is True.F) TypeError)rr rQ section_nameArgss r3 _create_args2assert_docstring_consistency.._create_argsNsT wd*z*$:%8:  e Id7X>??r5rrattrs AttributesreturnsReturnszDAll 'objects' must be one of: function, class or descriptor, got a: rc [[5$rrrrjr5r3r.assert_docstring_consistency..n T):r5c [[5$rr]rjr5r3rr^or_r5r*c3@# UHoR5v M g7fr)rrs r3r/assert_docstring_consistency..us7VQU QUsr9)rHr8)rGN)numpydoc.docscraperPrdictrr isfunctionisclassgetdocr-rSr*rrrr"rTrrr from_iterablerSrN)objectsinclude_paramsexclude_params include_attrs exclude_attrsinclude_returnsexclude_returnsrGrHrPrV section_args objects_docr2rFrErI type_items desc_itemsobj_nameobj_docr!type_defdescrUs @r3assert_docstring_consistencyrxsZ2 f@ AD@ ~x N }Wl K I NL &K  $ $S ) )!!#&&s##(6w~~c7J(KK %s)A' G I%++-  !:;  !:; !,!2!2!4 Hg-4W-=) 8T' 488"xx(8(>(>(@AH88E$7$77VQU7V$VWDy)(3::8Dy)$/66x@.>"5 !   %   !     3  ).r5c[R"SS9up4[R"U5 [ US5nUR UR S55 SSS5 [RU/n[R"[R"[R"[R5S55n[RR!5n[R"RXxS/5US'U[&US.n [RR)S 5n U (aXS S 'X)S '[+U40U D6n U R3S5n [4R6"X5(a"US :XaSn OSU<3n U SU <3n[9U5e[R<"U5 g!,(df  GNb=f![$a XxS'Nf=f![,a,n [/S U R0R3S5-5eSn A ff=f![:a,n [/SU R0R3S5-5eSn A ff=f![R<"U5 f=f)a'Utility to check assertions in an independent Python subprocess. The script provided in the source code should return 0 and the stdtout + stderr should not match the pattern `pattern`. This is a port from cloudpickle https://github.com/cloudpipe/cloudpickle Parameters ---------- source_code : str The Python source code to execute. pattern : str Pattern that the stdout + stderr should not match. By default, unless stdout + stderr are both empty, an error will be raised. timeout : int, default=60 Time in seconds before timeout. z_src_test_sklearn.py)suffixwbzutf-8Nz.. PYTHONPATH)cwdstderrenvCOVERAGE_PROCESS_STARTrtimeoutzscript errored with output: %s.+zExpected no outputz%The output was not supposed to match z$, got the following output instead: z!script timeout, output so far: %s)rmkstemprcloseopenwriteencoder; executablernormpathrTdirnamesklearn__file__rcopypathsepKeyErrorr rrrrYoutputdecoder>r?rrunlink) source_codepatternrfd source_filefcmdr}rrJ coverage_rcoute expectationrs r3r$r$s$&&.DEOBHHRL' +t $ GGK&&w/ 0%~~{+kk"''"**W-=-=">EFjjoo $ " ,6G0H IC s;jjnn%=> 6A5M2 3#y  "31&1 **W%Cyy&&d?"6K$I'"UK(M)McWU$W--' +M% $ $ #  $& "58PP  4qxxw7OO   +s I!F6BI!&G;I GAH6 GIGIGI H&'H  HH I 'II  II&cUS:Xa0Uc [U5$[R"XS9R5$US:Xa9Uc [ U5$[ [R"XS9R55$US:Xa[R"XS9$US;aI[ R "SUS9nURXUS S 9nUbUHnXxRS 5Xx'M U$US :XGa[ R "S US9n [R"U5n U RS :Xa U SS2S4OU n Uc+[U RS 5V s/sHn SU 3PM nn [U5V V s0sHupXSS2U 4_M n n n U RRU 5nUbT[UR5H;upX;dM UR!XUR#U5R%55nM= U$US:Xal[ R "SUS9nURXSS9nUbBUH<nUR'UR)U5R+UR,55nM> U$US:Xa$[ R "SUS9nUR/XS9$US:Xa&[ R "S US9n U R1U5$US:Xa$[ R "SUS9nUR/US9$US:Xa$[ R "SUS9nUR3XS9$US:Xa[5USUS 5$SU;a[6R8R;U5(d[R<"U5nUS;a[6R8R?XS9$US:Xa[6R8RAXS9$US:Xa[6R8RCXS9$US:Xa[6R8REXS9$ggs sn fs sn n f)awConvert a given container to a specific array-like with a dtype. Parameters ---------- container : array-like The container to convert. constructor_name : {"list", "tuple", "array", "sparse", "dataframe", "series", "index", "slice", "sparse_csr", "sparse_csc", "sparse_csr_array", "sparse_csc_array", "pyarrow", "polars", "polars_series"} The type of the returned container. columns_name : index or array-like, default=None For pandas container supporting `columns_names`, it will affect specific names. dtype : dtype, default=None Force the dtype of the container. Does not apply to `"slice"` container. minversion : str, default=None Minimum version for package to install. categorical_feature_names : list of str, default=None List of column names to cast to categorical dtype. Returns ------- converted_container rN)rutuplearray)pandas dataframer) minversionF)columnsrurr(pyarrowr:colpolarsrow)schemaorientseries pyarrow_array polars_series)rrslicerr)r sparse_csrsparse_csr_array sparse_cscsparse_csc_array)#rrsasarraytolistrpytest importorskip DataFrameastypendimrshape enumerateTable from_pydict column_names set_columncolumndictionary_encode with_columnsrcast CategoricalSeriesrIndexrr~rr atleast_2d csr_matrix csr_array csc_matrix csc_array) containerconstructor_name columns_namerurcategorical_feature_namespdrcol_nameparrrUrcol_idxpls r3_convert_containerrsD6! = ? "::i5<<> > W $ =# #I;BBDE E W $zz)11 4 4  j AiUQVW $ 05#)#3#:#::#F 6 Y &  z B 9%"'**/agu  /4U[[^/DE/D!c!I/DLE1:<1HI1HgaAqDk!1HI%%d+ $ 0%.v/B/B%C!8#..6==+B+T+T+VF&D  X %  j AiUK $ 05,,RVVH-=-B-B2>>-RS6 X %  j Ayyy00 _ ,  z Bxx "" _ ,  j Ayy y** W $  j Axx x// W $Yq\9Q<00 % %yy!!),,  i0I 7 799'' '? ? !3 399&&y&> >  -99'' '? ? !3 399&&y&> >4 &?FIs O6(O;c[XX#5$)aContext manager to ensure exceptions are raised within a code block. This is similar to and inspired from pytest.raises, but supports a few other cases. This is only intended to be used in estimator_checks.py where we don't want to use pytest. In the rest of the code base, just use pytest.raises instead. Parameters ---------- excepted_exc_type : Exception or list of Exception The exception that should be raised by the block. If a list, the block should raise one of the exceptions. match : str or list of str, default=None A regex that the exception message should match. If a list, one of the entries must match. If None, match isn't enforced. may_pass : bool, default=False If True, the block is allowed to not raise an exception. Useful in cases where some estimators may support a feature but others must fail with an appropriate error message. By default, the context manager will raise an exception if the block does not raise an exception. err_msg : str, default=None If the context manager fails (e.g. the block fails to raise the proper exception, or fails to match), then an AssertionError is raised with this message. By default, an AssertionError is raised with a default error message (depends on the kind of failure). Use this to indicate how users should fix their estimators to pass the checks. Attributes ---------- raised_and_matched : bool True if an exception was raised and a match was found, False otherwise. )_Raises)expected_exc_typematchmay_passrqs r3raisesr4sJ $X ??r5c \rSrSrSrSrSrg)ri\c[U[5(aUOU/Ul[U[5(aU/OUUlX0lX@lSUlg)NF)r)rexpected_exc_typesrmatchesrrqraised_and_matched)rArrrrqs r3rB_Raises.__init__^sP+X66 #$  #-UC"8"8we   "'r5c2^^Tc?UR(agUR=(d SUR3n[U5e[ U4SjUR55(d$URb[UR5TegUR b~UR=(d4 SR SRUR 5[T55n[ U4SjUR 55(d [U5TeSUl g)NTzDid not raise: c3<># UHn[TU5v M g7fr)r+)r expected_typers r3r#_Raises.__exit__..ts" !8  x / /!8sFzIThe error message should contain one of the following patterns: {} Got {}rc3d># UH%n[R"U[T55v M' g7fr)r>r?r)rr exc_values r3rrs#R\EryyI77\s-0) rrqrranyrr/rTrr)rArr_rqs `` r3rc_Raises.__exit__is  }},,UOD.WarningInfoiKzwarnings._ActionKindactionrkrr(cURRS:XaURRnO/URRSURR3nURSURSU3$)Nbuiltinsrr)r(rer-rlrr@s r3to_filterwarning_strI_get_warnings_filters_info_list..WarningInfo.to_filterwarning_strQsh}}'':5==11"mm667q9O9O8PQkk]!DLL>8*= =r5rjN) r-rerfrg__annotations__rrr,r(r*rorirjr5r3 WarningInforkKs%&&")$w-) >r5rrrr'rFz%pkg_resources is deprecated as an APIrr(z!Deprecated call to `pkg_resourceszQThe --rsyncdir command line argument and rsyncdirs config variable are deprecatedz,\s*Pyarrow will become a required dependencyz"datetime.datetime.utcfromtimestampzast.Num is deprecatedzAttribute n is deprecatedzast.Str is deprecatedzAttribute s is deprecatedz0.+scattermapbox.+deprecated.+scattermap.+instead)rDeprecationWarning FutureWarningr)rrs r3_get_warnings_filters_info_listrvJs  > > > G&89Gm4G&?@  ;'  7'   (    C'   8'   5@R   9DV  5@R   9DV  F' }C Cr5cb[5nUVs/sHnUR5PM sn$s snfr)rvro)warning_filters_info_list warning_infos r3get_pytest_filterwarning_linesrzs; ? A6 5L ))+5  s,c[5nUH7n[R"URURUR S9 M9 g)Nrs)rvr9filterwarningsrlrr()warnings_filters_info_listrys r3turn_warnings_into_errorsr~s>!@!B2     ((!** 3r5)NgTrkT)rmg& .>rk)r)F)rF)NN)r<)NNNN)NFN)urhr contextlibrr[rros.pathrrr>rr;rr@unittestr9 collectionsrrcollections.abcr dataclassesrr rr r itertoolsr r subprocessr rrrrnumpyrsscipyr~ numpy.testingrrxrrrrr sklearn.utilsrrrrrsklearn.utils._array_apirsklearn.utils.fixesrrrsklearn.utils.multiclassrsklearn.utils.validationr r!r"__all__caser#r,r4r1rrrARRAY_API_COMPAT_FUNCTIONALrrYrmarkskipif skip_if_32bitxfailfails_if_unstable_openblasparallelmpskip_if_no_parallel'skip_if_array_api_compat_not_configuredplatform!if_safe_multiprocessing_with_blasskip_if_no_numpydocrrrrrrrr"r$r6rrNrxr$rrAbstractContextManagerrrr(r@rhrvrzr~rjr5r3rs   /$! $OO ?? B   == ! !w)2X66tOSFR' T 8 (d#"&+ KK&&y9U&VM!'!2!2+-<"3"!++,, OO  'A-/5kk.@.@ ''M/A/+0)/ (:(:  )T);)%!++,, N<-H P)),,44Pf !R J ^J Z;B " i?X%@P*j//*Z5 5 p, , ^* * Z*ZRj S# \"("'(Z  s% J CJ JJJ#"J#