h$,fHddlmZddlZddlmZddlmZddl m Z dZ dZ y) )suppressN)sparse) is_scalar_nan)_object_dtype_isnancttt5ddl}||jur|j |cdddS dddt |r||jjdk(rtj|}|S|jjdvr'tj|jt}|St|}|S||k(}|S#1swYxYw)Nrf)iu)dtype)r ImportErrorAttributeErrorpandasNAisnarr kindnpisnanzerosshapeboolr)X value_to_maskrXts 3lib/python3.12/site-packages/sklearn/utils/_mask.py_get_dense_maskr s +~ ."  FII %;;q> "" & "]# 77<<3 !B IWW\\Z '!''.B I %Q'B I-  I)""s #CC"cjtj|s t||St|j|}|jdk(rtj ntj }|||jj|jjf|jt}|S)aCompute the boolean mask X == value_to_mask. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) Input data, where ``n_samples`` is the number of samples and ``n_features`` is the number of features. value_to_mask : {int, float} The value which is to be masked in X. Returns ------- X_mask : {ndarray, sparse matrix} of shape (n_samples, n_features) Missing mask. csr)rr ) spissparserdataformat csr_matrix csc_matrixindicescopyindptrrr)rrrsparse_constructor Xt_sparses r _get_maskr*"s" ;;q>q-00  /B*+((e*;" QYY^^ qxx}}/qwwdI ) contextlibrnumpyrscipyrrrfixesrrr*r+rr2s&0r+