Source code for jina.types.ndarray.sparse.scipy

from typing import List

import numpy as np

from . import BaseSparseNdArray
from ....proto import jina_pb2

if False:
    import scipy.sparse

__all__ = ['SparseNdArray']

[docs]class SparseNdArray(BaseSparseNdArray): """ Scipy powered sparse ndarray. .. warning:: scipy only supports ndim=2 .. seealso:: :param proto: the protobuf message, when not given then create a new one via :meth:`get_null_proto` :param sp_format: the sparse format of the scipy matrix. one of 'coo', 'bsr', 'csc', 'csr'. """ def __init__( self, proto: 'jina_pb2.SparseNdArrayProto' = None, sp_format: str = 'coo', *args, **kwargs, ): """Set constructor method.""" import scipy.sparse super().__init__(proto, *args, **kwargs) support_fmt = {'coo', 'bsr', 'csc', 'csr'} if sp_format in support_fmt: self.spmat_fn = getattr(scipy.sparse, f'{sp_format}_matrix') else: raise ValueError( f'{sp_format} sparse matrix is not supported, please choose one of those: {support_fmt}' )
[docs] def sparse_constructor( self, indices: 'np.ndarray', values: 'np.ndarray', shape: List[int] ) -> 'scipy.sparse.spmatrix': """ Sparse NdArray constructor for scipy.sparse.spmatrix. :param indices: the indices of the sparse array :param values: the values of the sparse array :param shape: the shape of the sparse array :return: SparseTensor """ if indices.shape[-1] != 2: raise ValueError( f'scipy backend only supports ndim=2 sparse matrix, given {indices.shape}' ) return self.spmat_fn((values, indices.T), shape=shape)
[docs] def sparse_parser(self, value: 'scipy.sparse.spmatrix'): """ Parse a scipy.sparse.spmatrix to indices, values and shape. :param value: the scipy.sparse.spmatrix. :return: a Dict with three entries {'indices': ..., 'values':..., 'shape':...} """ v = value.tocoo() return { 'indices': np.stack([v.row, v.col], axis=1), 'values':, 'shape': v.shape, }