maximize and minimize should return just one result about estimagic HOT 6 CLOSED

👍 Agree.

from estimagic.

I like the idea of the named tuple. This would allow us to very flexibly return everything possibly interesting in the "internal" optimization output - no matter which optimizer library was used. Especially if info is a dictionary and we come up with a good set of keys that unifies the outputs from the different libraries. We could also easily tag on additional information collected outside the optimizer library (e.g. for the purpose of debugging).

Regarding serializability

1. I wonder if it's really necessary to stick to params as a DataFrame. I cannot think of a case at the moment where params.to_dict() would cause us to lose any information. Of course, that would mean converting it back in many cases (such as the comparison plot) but that might be worth it.
2. In case we decide to return params as a dictionary: Named tuples are not easily serializable so given that there are only two entries in the tuple we might want to skip the naming step.

Moving to the (named) tuple should also allow us to get rid of the duplication now present in what the optimization returns. Right now both res_dict and params contains the final parameter vector.

from estimagic.

Regarding 1: No loss of information, but you can't do anything with such a dictionary. Also, I really want to convince people that there is nothing better than a DataFrame to represent and manipulate parameter vectors.

Yes, it is true that we can get rid of the duplication. Good observation!

from estimagic.

The returned result should also contain the constraints.

from estimagic.

#102 implements a results dictionary which contains harmonized fields which are the same across optimizers and other fields.

We thought about implementing a nice __repr__ method which turns the dictionary into a beautiful screen output, but it is delayed for now.

from estimagic.

Done in estimagic 0.1.

from estimagic.