{KgodZddlZddlZddlmZddlZddlmZddlm Z ddl m Z dd hZ d0d Z d0d Zd ZdZd efddZdZdZdZdZdZdZdZGddZdZdZGddZeZd efddZd efddd Z d efdd!Z!d1d"Z"d#Z#d$Z$d2d%Z%d3d&Z&d3d'Z' d4ddd(d)Z(d1d*Z)d+Z*d,Z+d-Z,d.Z-d3d/Z.y)5zTools to support array_api.Nwraps) get_config) parse_versionnumpyzarray_api_compat.numpyTc#8KdD]}|s |tvr|yw)a7Yield supported namespace. This is meant to be used for testing purposes only. Parameters ---------- include_numpy_namespaces : bool, default=True If True, also yield numpy namespaces. Returns ------- array_namespace : str The name of the Array API namespace. )r array_api_strictcupycupy.array_apitorchN)_NUMPY_NAMESPACE_NAMES)include_numpy_namespacesarray_namespaces \/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/sklearn/utils/_array_api.pyyield_namespacesrs( (O?U,U  sc#Kt|D];}|dk(r-tjddD] \}}|||f|ddf5|ddf=yw)aYield supported namespace, device, dtype tuples for testing. Use this to test that an estimator works with all combinations. Parameters ---------- include_numpy_namespaces : bool, default=True If True, also yield numpy namespaces. Returns ------- array_namespace : str The name of the Array API namespace. device : str The name of the device on which to allocate the arrays. Can be None to indicate that the default value should be used. dtype_name : str The name of the data type to use for arrays. Can be None to indicate that the default value should be used. )rr)cpucuda)float64float32mpsrN)r itertoolsproduct)rrdevicedtypes r)yield_namespace_device_dtype_combinationsr0sn.,!9 g %!*!2!2!7" &vu44""5)3 3!4- -sA Ac|r> ddl}ttj}d}|t|krtd|dyy#t$r tdwxYw)zCheck that array_api_compat is installed and NumPy version is compatible. array_api_compat follows NEP29, which has a higher minimum NumPy version than scikit-learn. rNzKarray_api_compat is required to dispatch arrays using the API specificationz1.21zNumPy must be z7 or newer to dispatch array using the API specification)array_api_compat ImportErrorrr __version__)array_api_dispatchr numpy_versionmin_numpy_versions r_check_array_api_dispatchr&Ts   #&e&7&78 " =):; ; !2 34))  < !  s AAct|tjtjfs t |dsy|j S)z0Hardware device where the array data resides on.rr) isinstancer ndarraygenerichasattrr)arrays r_single_array_devicer-ls5%%--78 xA|| remove_none remove_typesc t|||d}t|d}|ddD]5}t|}||k7stdt|dt||S)aDHardware device where the array data resides on. If the hardware device is not the same for all arrays, an error is raised. Parameters ---------- *array_list : arrays List of array instances from NumPy or an array API compatible library. remove_none : bool, default=True Whether to ignore None objects passed in array_list. remove_types : tuple or list, default=(str,) Types to ignore in array_list. Returns ------- out : device `device` object (see the "Device Support" section of the array API spec). r/rrNz$Input arrays use different devices: , )_remove_non_arraysr- ValueErrorstr)r0r1 array_listdevice_r, device_others rrrvsz*$ <J #:a=1G AB+E2 l "6s7|nB|$%'   Nr.c@tj|jS)zReturn the total number of elements of x. Parameters ---------- x : array Array instance from NumPy or an array API compatible library. Returns ------- out : int Total number of elements. )mathprodshape)xs rsizer?s 99QWW r.c&|jtvS)z%Return True if xp is backed by NumPy.)__name__rxps r_is_numpy_namespacerDs ;;0 00r.c*t|r%|jtj||S|j|jcxk(rdk(sJJ|j |j |j ||j |gS)Nr)rDasarrayr union1dndim unique_valuesconcat)abrCs r_union1drMsz2zz%--1-.. 66QVV q    BIIr'7'7':Bzisdtype..sBT?5!33TrB)r(tupleanyrR)rkindrCs` `risdtyper[s/ $BTBBBudr22r.ct|tr3|dk(rjk(S|dk(r0jjj j hvS|dk(r0jjjjhvS|dk(rtfddDS|dk(r tvS|dk(r\t}td r|jj td r|jj"|vS|d k(rtfd d DSt%d||k(S)Nboolsigned integerunsigned integerintegralc3:K|]}t|ywrPrQrSs rrVz"_isdtype_single..s%?A qR00?rW)r^r_ real floatingcomplex floating complex64 complex128numericc3:K|]}t|ywrPrQrSs rrVz"_isdtype_single..s%JA qR00JrW)r`rbrczUnrecognized data type kind: )r(r6r]int8int16int32int64uint8uint16uint32uint64rYsupported_float_dtypessetr+addrdrer5)rrZrCcomplex_dtypess` ` rrRrRsN$ 6>BGG# # % %RWWbhh"((CC C ' 'RXXryy"))RYYGG G Z ? _ $2266 6 ' ' UNr;'""2<<0r<(""2==1N* * Y J  v! 1W -v>> ?s?c(eZdZdZdZdZdZdZy)_ArrayAPIWrapperasklearn specific Array API compatibility wrapper This wrapper makes it possible for scikit-learn maintainers to deal with discrepancies between different implementations of the Python Array API standard and its evolution over time. The Python Array API standard specification: https://data-apis.org/array-api/latest/ Documentation of the NumPy implementation: https://numpy.org/neps/nep-0047-array-api-standard.html c||_yN _namespace)selfrs r__init__z_ArrayAPIWrapper.__init__%s )r.c.t|j|Sr)getattrr)rnames r __getattr__z_ArrayAPIWrapper.__getattr__(st--r.c4|j|jk(Srr)rothers r__eq__z_ArrayAPIWrapper.__eq__+s%"2"222r.c2t|||jSNrB)r[rrrrZs rr[z_ArrayAPIWrapper.isdtype.sudt77r.N)rA __module__ __qualname____doc__rrrr[r.rr~r~s *.38r.r~c(|dvrtd|y)N>NrzUnsupported device for NumPy: )r5rws r_check_device_cpur2s" ]"9&DEE#r.c.tfd}|S)NcJt|jdd|i|S)Nr)rpop)argskwargsfuncs r wrapped_funcz(_accept_device_cpu..wrapped_func8s&&**Xt45T$V$$r.r)rrs` r_accept_device_cpur7s! 4[%% r.ceZdZdZhdZhdZdZedZdddd Z d d d d d Z d Z dZ dZ d ddZd ddZdZy )_NumPyAPIWrapperaArray API compat wrapper for any numpy version NumPy < 2 does not implement the namespace. NumPy 2 and later should progressively implement more an more of the latest Array API spec but this is still work in progress at this time. This wrapper makes it possible to write code that uses the standard Array API while working with any version of NumPy supported by scikit-learn. See the `get_namespace()` public function for more details. > eyefullonesemptyzerosarangelinspace full_like ones_like empty_like zeros_like> rhrirjrkrlrmrnrorurrrdrectt|}||jvr t|S||jvrtj |S|Sr)rr _CREATION_FUNCSr_DTYPESr)rrattrs rrz_NumPyAPIWrapper.__getattr__tsKud# 4'' '%d+ + 4<< ;;t$ $ r.c"tjSr)r bool_)rs rr]z_NumPyAPIWrapper.bools {{r.Tunsafecopycastingc*|j|||S)Nr)astype)rr>rrrs rrz_NumPyAPIWrapper.astypesxxD'x::r.N)rrrc~t||durtj|d|Stj||S)NT)rrr)rr r,rF)rr>rrrs rrFz_NumPyAPIWrapper.asarrays5&! 4<;;qt59 9==%0 0r.c0tj|dS)NT)return_inverser uniquerr>s runique_inversez_NumPyAPIWrapper.unique_inverses||Ad33r.c0tj|dS)NT) return_countsrrs r unique_countsz_NumPyAPIWrapper.unique_countss||AT22r.c,tj|Srrrs rrIz_NumPyAPIWrapper.unique_valuess||Ar.axisc0tj||S)Nr)r concatenate)rrzrs rrJz_NumPyAPIWrapper.concats  d33r.)rct|tstd|dt||dur|j }t j ||S)zGives a new shape to an array without changing its data. The Array API specification requires shape to be a tuple. https://data-apis.org/array-api/latest/API_specification/generated/array_api.reshape.html zshape must be a tuple, got z of type T)r(rX TypeErrortyperr reshape)rr>r=rs rrz_NumPyAPIWrapper.reshapesT %'-eYiU }M  4<A}}Q&&r.ct|||Sr)r[rs rr[z_NumPyAPIWrapper.isdtypesudt,,r.)rArrrrrrpropertyr]rrFrrrIrJrr[rr.rrr@sp $ OG$ (,X;#'t$143&*4)- '-r.rc g}t|}|D]%}|r|t||r|j|'|sAtd|ddj d|Dddj d|Dd|S)aFilter arrays to exclude None and/or specific types. Raise ValueError if no arrays are left after filtering. Parameters ---------- *arrays : array objects Array objects. remove_none : bool, default=True Whether to ignore None objects passed in arrays. remove_types : tuple or list, default=(str,) Types to ignore in the arrays. Returns ------- filtered_arrays : list List of arrays with None and typoe zCAt least one input array expected after filtering with remove_none=z, remove_types=[r3c34K|]}|jywr)rA)rTts rrVz%_remove_non_arrays..s&H.s)KFq$q'*:*:Fs!z].)rXr(appendr5join)r0r1rzfiltered_arraysr,s rr4r4s*O&L 5=  e\ * u%  Rk^T!YY&H<&HHIJ $ )KF)K KLB P  r.)r0r1rCctd}|s||dfStdfS||dfSt|||d}t|ddl}|j |d}}|j dvr t|}||fS)aGet namespace of arrays. Introspect `arrays` arguments and return their common Array API compatible namespace object, if any. See: https://numpy.org/neps/nep-0047-array-api-standard.html If `arrays` are regular numpy arrays, an instance of the `_NumPyAPIWrapper` compatibility wrapper is returned instead. Namespace support is not enabled by default. To enabled it call: sklearn.set_config(array_api_dispatch=True) or: with sklearn.config_context(array_api_dispatch=True): # your code here Otherwise an instance of the `_NumPyAPIWrapper` compatibility wrapper is always returned irrespective of the fact that arrays implement the `__array_namespace__` protocol or not. Parameters ---------- *arrays : array objects Array objects. remove_none : bool, default=True Whether to ignore None objects passed in arrays. remove_types : tuple or list, default=(str,) Types to ignore in the arrays. xp : module, default=None Precomputed array namespace module. When passed, typically from a caller that has already performed inspection of its own inputs, skips array namespace inspection. Returns ------- namespace : module Namespace shared by array objects. If any of the `arrays` are not arrays, the namespace defaults to NumPy. is_array_api_compliant : bool True if the arrays are containers that implement the Array API spec. Always False when array_api_dispatch=False. r#NFTr/r>r )r_NUMPY_API_WRAPPER_INSTANCEr4r&r rxrAr~)r0r1rCrzr#r namespaceis_array_api_compliants rrxrxsd$&:;  >u9 .5 5 ~4x  [|F01 (F(8(F(F(OQU%I//$Y/ , ,,r.czt|||d}tdg}t|i|\}}|r||t|i|fS|ddfS)zECombination into one single function of `get_namespace` and `device`.r/FN)r4dictrxr)r0r1r7skip_remove_kwargsrCr{s rget_namespace_and_devicer+sf# <J%bA$jG4FGB    J 5"4 5  5$r.ct||\}}t|r7|jtjt j|Sdd|j | zz S)NrBg?)rxrDrFspecialexpitr exp)XrC_s r_expitr>sU ! #EB2zz'-- a(89:: #r " ##r.c|j||j}t|rPtj|}|jdd|j ddzxx|z cc<|j|S|j dk(r0t|j dD]}|||fxx||z cc<yt|j dD]}|||fxx|z cc<y)Nrrr)rFrrDr flatr=rHrange)r,valuerCarray_npis r_add_to_diagonalrFs JJuEKKJ 0E2==' +Q!++,5,zz(## qu{{1~&A !Q$K58 #K'u{{1~&A !Q$K5 K'r.c|Dcgc]}t|ds|}}|Dcgc]+}|j|jds |j-}}|r|j|S|j djScc}wcc}w)aFind a suitable floating point dtype when computing with arrays. If any of the arrays are floating point, return the dtype with the highest precision by following official type promotion rules: https://data-apis.org/array-api/latest/API_specification/type_promotion.html If there are no floating point input arrays (all integral inputs for instance), return the default floating point dtype for the namespace. rrb)r+r[r result_typerF)rCrzrK dtyped_arraysfloating_dtypess r_find_matching_floating_dtyperWs!'>1'!W*=QM>&&A"**QWWo*Nr~~// ::c?  ?sA?A?!BBc t||\}}}t|rR|r'|jtj|||S|'|%|jtj ||S|j||}||j||}||j |j k7r|8tdt|j dt|j dt|j |j |fk7r;tdt|j dt|j d|d d g|jz}|j |||<|j||}|j|jd r td |'|j|jd r td t||| }|j!||}|"|r |j"n |j$||S|j!||}|j%|j'|||} |s| S|j%||} |j)| dk(r t+d| | z S)aPartial port of np.average to support the Array API. It does a best effort at mimicking the return dtype rule described at https://numpy.org/doc/stable/reference/generated/numpy.average.html but only for the common cases needed in scikit-learn. )rweightsrwz+Axis must be specified when the shape of a z and weights z differ.zShape of weights weights.shape=z! must be consistent with a.shape=z and axis=.rrcz;Complex floating point values are not supported by average.rBrrz(Weights sum to zero, can't be normalized)rrDrFr averagedotr=rrXr5rHrr[rNotImplementedErrorrrmeansummultiplyrYZeroDivisionError) rKrr normalizerCrr8r= output_dtypesum_scales r_averagerpsh.a9NB72 ::emmAD'JK K \g1::eii734 4 1W %A**WW*5qww'--7 <=eAGGn=MN /0:   AGGDM#3 31% 2F1GH++0>*:+waI  aff ggdmd **We, zz!''-.! I  rzz'--9KL! I  1GCL !\"A09"&&!$??ii.G 66"++a)6 5D  FF7F &E vvesl JKK %<r.c t||\}}t|r&|jtj||S|j |}|j |j||j|jt|||}|j||}|j|r,|j||j|j|}|SNrBrrw) rxrDrFr nanminisnanminwhereinfrallrYnanrrrCrmasks r_nanminr  ! #EB2zz%,,qt455xx{ FF288D"**bffWVAY*"GKRVF Wvvdv& 66$<rzz"&&115Ar.c t||\}}t|r&|jtj||S|j |}|j |j||j|j t|||}|j||}|j|r,|j||j|j|}|Sr) rxrDrFr nanmaxrmaxrrrrrYrrs r_nanmaxrr r.)rCrct||\}}t|rF|durtj|||}ntj|||}|j |S|j ||||S)aHelper to support the order kwarg only for NumPy-backed arrays Memory layout parameter `order` is not exposed in the Array API standard, however some input validation code in scikit-learn needs to work both for classes and functions that will leverage Array API only operations and for code that inherently relies on NumPy backed data containers with specific memory layout constraints (e.g. our own Cython code). The purpose of this helper is to make it possible to share code for data container validation without memory copies for both downstream use cases: the `order` parameter is only enforced if the input array implementation is NumPy based, otherwise `order` is just silently ignored. rBT)orderr)rrr)rxrDr r,rF)r,rrrrCrrs r_asarray_with_orderrso %B 'EB2 4<KKU%@EMM%uEBEzz%  zz%u4zGGr.ct||\}}t|r;tj|}|jtj|dS|j |dS)zArray API compliant version of np.ravel. For non numpy namespaces, it just returns a flattened array, that might be or not be a copy. rBC)r))r=)rxrDr rFravelr)r,rCrs r_ravelrsW %B 'EB2 e$zz%++e3788 ::e5: ))r.c|j}|dvr|jjS|dk(r|jj S|dvr|j Stj |S)z*Convert X into a NumPy ndarray on the CPU.>array_api_compat.torchrr >array_api_compat.cupyr )rArr _arraygetrF)r,rCxp_names r_convert_to_numpyrsikkG55yy{  "" $ $||!! 5 5yy{ == r.cddlm}||}t|jD]@\}}t |dst |t jr||}t|||B|S)aCreate new estimator which converting all attributes that are arrays. The converter is called on all NumPy arrays and arrays that support the `DLPack interface `__. Parameters ---------- estimator : Estimator Estimator to convert converter : callable Callable that takes an array attribute and returns the converted array. Returns ------- new_estimator : Estimator Convert estimator r)clone __dlpack__) sklearn.baservarsitemsr+r(r r)setattr) estimator converterr new_estimatorkey attributes r _estimator_with_converted_arraysr*sa&#)$My///1Y 9l +z)U]]/S!),I sI.2 r.cj|tj}tj|jdzS)z6Return the absolute tolerance for a given numpy dtype.d)r rfinfoeps)dtype_or_dtype_names r_atol_for_typer0+s/"$mm ;;* + / /# 55r.c8|jdjS)aMReturn a platform-specific integer dtype suitable for indexing. On 32-bit platforms, this will typically return int32 and int64 otherwise. Note: using dtype is recommended for indexing transient array datastructures. For long-lived arrays, such as the fitted attributes of estimators, it is instead recommended to use platform-independent int32 if we do not expect to index more 2B elements. Using fixed dtypes simplifies the handling of serialized models, e.g. to deploy a model fit on a 64-bit platform to a target 32-bit platform such as WASM/pyodide. r)rFrrBs rindexing_dtyper25s* ::a=  r.c~|jdk(sJ|j||}|S|j||}|j||jddf}|j ||j |z}|jd||j }|j|j|dk7|||S)zoA variant of `sklearn.utils.sparsefuncs.count_nonzero` for the Array API. It only supports 2D arrays. rrwrr)rrr) rHrrFrr=rrrr)rrCrr sample_weightr zero_scalars r_count_nonzeror6Ms 66Q;;ll1Vl,G  = @  ==3F3Fq3I12MN ))G]%8%89MI**QvW]]*CK 66"((167K8t6 DDr.)Tr)NNTN)NN)NNN)/rrr; functoolsrr scipy.specialr_configrfixesrrrrr&r-r6rr?rDrMr[rRrpr|r~rrrrr4rxrrrrrr rrrrr*r0r2r6rr.rr;s#!   !#;<@!.H0%)v(V  1 S 3D (:886F l-l-^/0-1v$N(,3&TO-d7;#&$!"!2?D"$)-H48H< *  :60Er.