在 HDF5 (PyTables) 中存储 numpy 稀疏矩阵

我在使用 PyTables 存储 numpy csr_matrix 时遇到问题。我收到此错误：

TypeError: objects of type ``csr_matrix`` are not supported in this context, sorry; supported objects are: NumPy array, record or scalar; homogeneous list or tuple, integer, float, complex or string

My code:

f = tables.openFile(path,'w')

atom = tables.Atom.from_dtype(self.count_vector.dtype)
ds = f.createCArray(f.root, 'count', atom, self.count_vector.shape)
ds[:] = self.count_vector
f.close()

有任何想法吗？

Thanks

DaveP 的答案是almost是的...但是对于非常稀疏的矩阵可能会导致问题：如果最后一列或最后一行为空，它们就会被删除。因此，为了确保一切正常，“shape”属性也必须被存储。

这是我经常使用的代码：

import tables as tb
from numpy import array
from scipy import sparse

def store_sparse_mat(m, name, store='store.h5'):
    msg = "This code only works for csr matrices"
    assert(m.__class__ == sparse.csr.csr_matrix), msg
    with tb.openFile(store,'a') as f:
        for par in ('data', 'indices', 'indptr', 'shape'):
            full_name = '%s_%s' % (name, par)
            try:
                n = getattr(f.root, full_name)
                n._f_remove()
            except AttributeError:
                pass

            arr = array(getattr(m, par))
            atom = tb.Atom.from_dtype(arr.dtype)
            ds = f.createCArray(f.root, full_name, atom, arr.shape)
            ds[:] = arr

def load_sparse_mat(name, store='store.h5'):
    with tb.openFile(store) as f:
        pars = []
        for par in ('data', 'indices', 'indptr', 'shape'):
            pars.append(getattr(f.root, '%s_%s' % (name, par)).read())
    m = sparse.csr_matrix(tuple(pars[:3]), shape=pars[3])
    return m

使其适应 csc 矩阵很简单。