U 3dL@sndZddlZddlmZddlmZGdddZGdddZd d Zd d Z dd dZ ddZ ddZ dS)zTools to support array_api.N) get_configc@s(eZdZdZddZddZddZdS) _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 cCs ||_dSN) _namespace)selfZarray_namespacertd|jdkrXtdj|dkrjdkr~fd d |D}qfd d |D}nfd d |D}|jj||dS) Nnumpy.array_apiaxis>rz&Only axis in (0, 1) is supported. Got >rrz(Only X.ndim in (1, 2) is supported. Got rrcsg|] }|qSrr.0iXrr ,sz)_ArrayAPIWrapper.take..csg|]}|ddfqSrrrrrr r.scsg|]}dd|fqSrrrrrr r0s)r__name__numpytakeasarray ValueErrorndimstack)rrindicesrZX_npselectedrrr rs    z_ArrayAPIWrapper.takeN)r __module__ __qualname____doc__r rrrrrr rs rc@s`eZdZdZddZdddddZd d d d d d Zd dZddZddZ d dddZ d S)_NumPyApiWrapperaRArray API compat wrapper for any numpy version NumPy < 1.22 does not expose the numpy.array_api namespace. 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. cCs tt|Sr)r rr rrr r?sz_NumPyApiWrapper.__getattr__TZunsafecopycastingcCs|j|||dS)Nr&)astype)rxdtyper'r(rrr r)Bsz_NumPyApiWrapper.astypeN)r+devicer'cCs*|dkrtj|d|dStj||dSdS)NT)r'r+)r+)rarrayr)rr*r+r,r'rrr rFsz_NumPyApiWrapper.asarraycCstj|ddS)NT)Zreturn_inverseruniquerr*rrr unique_inverseMsz_NumPyApiWrapper.unique_inversecCstj|ddS)NT)Z return_countsr.r0rrr unique_countsPsz_NumPyApiWrapper.unique_countscCs t|Srr.r0rrr unique_valuesSsz_NumPyApiWrapper.unique_valuesrcCstj||dS)Nr)rZ concatenate)rarraysrrrr concatVsz_NumPyApiWrapper.concat) rr"r#r$rr)rr1r2r3r5rrrr r%4s r%cGsltdstdfSdd|D}|s.tdt|dkrHtd||\}|dkr`tdfSt|d fS) aqGet namespace of arrays. Introspect `arrays` arguments and return their common Array API compatible namespace object, if any. NumPy 1.22 and later can construct such containers using the `numpy.array_api` namespace for instance. 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. Returns ------- namespace : module Namespace shared by array objects. is_array_api : bool True of the arrays are containers that implement the Array API spec. Zarray_api_dispatchFcSs4h|],}t|ttttfst|dr,|ndqS)__array_namespace__N) isinstanceboolintfloatcomplexhasattrr6)rr*rrr sz get_namespace..zUnrecognized array inputrz&Multiple namespaces for array inputs: NT)rr%rlenr)r4 namespacesxprrr get_namespaceZs-    rAcCs@t|\}}|jdkr,|tt|Sdd|| S)N>rrg?)rArrspecialZexpitrexp)rr@_rrr _expits  rEcCsP|dkrt|\}}|jdkrrr)orderr+)r')r+r')rArrr)r-r+rFr'r@rDrrr _asarray_with_orders   rGcCsLddg}|j|kr*d|}td||jdkr>|jSt|SdS)zsConvert X into a NumPy ndarray. Only works on cupy.array_api and numpy.array_api and is used for testing. rzcupy.array_apiz, zSupported namespaces are: N)rjoinrZ_arraygetrr)r-r@Zsupported_array_apiZsupport_array_api_strrrr _convert_to_numpys    rJcCsXddlm}||}t|D]2\}}t|ds>t|tjrF||}t|||q |S)a[Create new estimator which converting all attributes that are arrays. 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)cloner6) Z sklearn.baserKvarsitemsr<r7rZndarraysetattr)Z estimator converterrKZ new_estimatorkey attributerrr _estimator_with_converted_arrayss  rR)NNNN) r$r_configrZ scipy.specialrBrr%rArErGrJrRrrrr s  -&F