c50597926f69286fce5c416d23271a302b2da962,sklearn/utils/_mask.py,,_get_mask,#Any#Any#,7

Before Change

            return np.zeros(X.shape, dtype=bool)
        else:
            // np.isnan does not work on object dtypes.
            return _object_dtype_isnan(X)
    else:
        return X == value_to_mask

After Change

    X_mask : {ndarray, sparse matrix} of shape (n_samples, n_features)
        Missing mask.
    
    if not sp.issparse(X):
        // For all cases apart of a sparse input where we need to reconstruct
        // a sparse output
        return _get_dense_mask(X, value_to_mask)

    Xt = _get_dense_mask(X.data, value_to_mask)

    sparse_constructor = (sp.csr_matrix if X.format == "csr"
                          else sp.csc_matrix)
    Xt_sparse = sparse_constructor(
        (Xt, X.indices.copy(), X.indptr.copy()), shape=X.shape, dtype=bool
    )

Instances