Difference in sparse.einsum and np.einsum behaviour about sparse HOT 10 CLOSED

I think there are maybe two questions here:

• implicit output: einsum('ab', x) - i.e. the output is not given but automatically computed, this is handled already
• interleaved format: einsum(array_0, indices_0, array_1, indices_1, ...[, output_indices]), this is not supported yet, but is just a matter of parsing that could be lifted from e.g. opt_einsum.

Note that einsum('ab->ab, v1, v2) is not actually valid! There should only be one term, and that last call should probably error, as it does with numpy...

from sparse.

Actually, since #579 your case is working and the interleaved format is supported. However, you need to update sparse to the last development version from github.

from sparse.

By the way, you could use opt_einsum.contract as a drop in here and it should work with sparse, handle the input and also perform contraction order (for 3+ terms) optimization.

from sparse.

Cc @HadrienNU @jcmgray

from sparse.

I believe it isn't possible, as of now. Is it required for your use-case?

from sparse.

@hameerabbasi
Yes, I'm trying to use it for the sparse factor product in MRF, and most convenient way is to provide only operands and indices, here is the example in numpy:
https://github.com/pgmpy/pgmpy/blob/d1131144af2a2a4b3ef341613170538c808194aa/pgmpy/factors/discrete/DiscreteFactor.py#L704

I trying to implement the same, but with sparse matrices.

from sparse.

Yeah, it seems that convert_interleaved_input function from opt_einsum may help to solve the problem.
@jcmgray @hameerabbasi big thanks to you guys for a quick response!

from sparse.

@HadrienNU thanks for a reply, already manage to do it with parsing from opt_einsum, but using it from the lib will be more convenient.

from sparse.

Actually, since #579 your case is working and the interleaved format is supported. However, you need to update sparse to the last development version from github.

Ah sorry I missed this PR, thanks for adding the additional parsing!

from sparse.

Since the question has been answered and the required feature is in master, I'm going to close this issue.

from sparse.