U 3d@sLddlZddlmZddlmZddlmZddl m Z ddZ d d Z dS) N)sparse)suppress) is_scalar_nan)_object_dtype_isnanc Csttt.ddl}||jkr4||W5QRSW5QRXt|r|jjdkr^t |}q|jjdkr|t j |j t d}qt|}n||k}|S)Nrf)iu)dtype)r ImportErrorAttributeErrorpandasZNAZisnarr kindnpisnanzerosshapeboolr)X value_to_maskr Xtr7/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/utils/_mask.py_get_dense_mask s       rcCs\t|st||St|j|}|jdkr0tjntj}|||j|j f|j t d}|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. Zcsr)rr ) spissparserdataformatZ csr_matrixZ csc_matrixindicescopyZindptrrr)rrrZsparse_constructorZ Xt_sparserrr _get_mask!s   r ) ZnumpyrZscipyrr contextlibrrfixesrrr rrrrs