:iSrSSKrSSKrSSKrSSKrSSKrSSKrSSKJr SSK r SSK r SSK r SSK r SSKrSSKrSSKJr SSKJr SSKJr SSKJr SSKJrJrJrJr SSKJr SSKrSSKrSSKr SS K!J"r# SS K!J$r$J%r%J&r&J'r'J(r(J)r) SSK*r*SS K+J,r, SS K-J.r.J/r/J0r0J1r1J2r2J3r3 SS K4J5r5 SSK6J7r7J8r8J9r9 /SQr:\"S5r;\;Rxr=\R|R~r?\;RrA\;RrC\CrDS\E4SjrF"SS5rGSrHS9Sjr"S:SjrIS;SjrJ\,"S5 SrKSSKMrM\MRR\.SS9rP\MRR\/SS9rR\MRR\1"5SS9rS\MRR\RR(+SS9rV\MRR\K(+SS9rW\MRR\ RS :HS!S9rYS"rZSS)jr`S?S*jraS@S+jrbSAS,jrc"S-S.\R5re"S/S05rf"S1S25rg"S3S45rhS5riS6rjS7rkS8rlg!\La SrKGNSf=f!\La Nf=f)BzTesting utilities.N)Iterable) dataclasswraps) signature)STDOUTCalledProcessErrorTimeoutExpired check_output)TestCase)assert_allclose)assert_almost_equalassert_approx_equalassert_array_almost_equalassert_array_equalassert_array_lessassert_no_warnings)_check_array_api_dispatch) _IS_32BIT_IS_PYPYVisibleDeprecationWarning#_in_unstable_openblas_configuration parse_version sp_version)check_classification_targets) check_arraycheck_is_fitted check_X_y) assert_raisesassert_raises_regexprrrrrr 'assert_run_python_script_without_outputrSkipTest__init__c[U[5(a9[U[5(a$URn[ SR US95e[U5(a[US9"U5$[US9$)aContext manager and decorator to ignore warnings. Note: Using this (in both variants) will clear all warnings from all python modules loaded. In case you need to test cross-module-warning-logging, this is not your tool of choice. Parameters ---------- obj : callable, default=None callable where you want to ignore the warnings. category : warning class, default=Warning The category to filter. If Warning, all categories will be muted. Examples -------- >>> import warnings >>> from sklearn.utils._testing import ignore_warnings >>> with ignore_warnings(): ... warnings.warn('buhuhuhu') >>> def nasty_warn(): ... warnings.warn('buhuhuhu') ... print(42) >>> ignore_warnings(nasty_warn)() 42 z'obj' should be a callable where you want to ignore warnings. You passed a warning class instead: 'obj={warning_name}'. 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Given an exception, a callable to raise the exception, and a message string, tests that the correct exception is raised and that the message is a substring of the error thrown. Used to test that the specific message thrown during an exception is correct. Parameters ---------- exceptions : exception or tuple of exception An Exception object. message : str The error message or a substring of the error message. function : callable Callable object to raise error. *args : the positional arguments to `function`. **kwargs : the keyword arguments to `function`. z or c38# UHoRv M g7fN)r,).0es r2 'assert_raise_message..s?Jq Jsz not raised by z4Error message does not include the expected string: z. Observed error message: N)r(tuplerRr,AssertionErrorstr) exceptionsmessagefunctionrGrHnamesrl error_messages r2assert_raise_messagerws.Q$!&! j% ( (KK?J??E''EeX=N=NOPP A  ' =DmU  (sA B&#BBTc  /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) actualdesiredrzr{r|r}r~dtypesrrtolss r2r r sjFmmF+R]]7-CGllGMM *F |DJKF5"**,$6FK5z  Ls !B c[RRU5(a[RRU5(aUR5nUR5nUR 5 UR 5 [ UR UR US9 [ URURUS9 [URURX#US9 g[RRU5(d0[RRU5(d [XX#US9 g[S5e)a@Assert allclose for sparse and dense data. Both x and y need to be either sparse or dense, they can't be mixed. Parameters ---------- x : {array-like, sparse matrix} First array to compare. y : {array-like, sparse matrix} Second array to compare. rtol : float, default=1e-07 relative tolerance; see numpy.allclose. atol : float, default=1e-9 absolute tolerance; see numpy.allclose. 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(JC ::e22E4E4EF F (     (zzU111 EJJ(  w<1 G} %     s(C,CC<CC CCcJ/n[R"U5nU(aURUR5 URnX0R:wa#URUSUR S55 URUR5 SR U5$)zGet function full name. Parameters ---------- func : callable The function object. Returns ------- name : str The function name. N.)inspect getmodulerQr,rcfindrR)funcpartsmodulequalnames r2_get_func_namers| E   t $F  V__%  H==  X2 c 234 LL 88E?r4c ^SSKJn /nTc/OTm[U5nURS5(aURS5(aU$[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. 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J J L ~G NNMM";=QR W!9,- 9I  )73s8C"#hnnT22A6GM)"=!>!HHI$$2 1s3x(( !*<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$US :Xa[ R "S US9n [R"U5n Uc+[U RS 5V s/sHn SU 3PM nn [U5V V s0sHupXSS2U 4_M n n n U RRU 5nUbT[UR5H;upX;dM URXUR!U5R#55nM= U$US:Xal[ R "SUS9nURXSS9nUbBUH<nUR%UR'U5R)UR*55nM> U$US:Xa$[ R "SUS9nUR-XS9$US:Xa$[ R "SUS9nUR-US9$US:Xa$[ R "SUS9nUR/XS9$US:Xa[1USUS 5$SU;a[2R4R7U5(d[R8"U5nSU;a'[:[=S5:a[?US[:35eUS;a[2R4RAXS9$US:Xa[2R4RCXS9$US:Xa[2R4REXS9$US:Xa[2R4RGXS9$ggs sn fs sn n f)awConvert a given container to a specific array-like with a dtype. 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