optimagic is a Python package for numerical optimization. It is a unified interface to optimizers from SciPy, NlOpt and other packages. optimagic's minimize function works just like SciPy's, so you don't have to adjust your code. You simply get more optimizers for free. On top you get diagnostic tools, parallel numerical derivatives and more.
[ ] Explain what n_samples does in LikelihoodResult.se() (It says to look at the implementation of constraints, but glancing over it I cannot find anything there.
[ ] Make the warning about a missing seed more constructive in LikelihoodResult.se(). E.g., a core estimagic dev wrote some code that produces:
estimagic/estimation/estimate_ml.py:429:
UserWarning: seed is set to None and constraints are transforming.
This leads to randomness in the result. To avoid random behavior, choose a
non-None seed.
and it took me quite some time to find out where it is coming from (pytask does not show more and it wasn't obvious to me that LikelihoodResult lives in estimate_ml.py. So I had initially thought this happens during the estimation phase... Would be enough to add that this needs to be done in .se() or .summary().
n_samplesdoes inLikelihoodResult.se()(It says to look at the implementation of constraints, but glancing over it I cannot find anything there.[ ] Make the warning about a missing seed more constructive in
LikelihoodResult.se(). E.g., a core estimagic dev wrote some code that produces:and it took me quite some time to find out where it is coming from (pytask does not show more and it wasn't obvious to me that
LikelihoodResultlives inestimate_ml.py. So I had initially thought this happens during the estimation phase... Would be enough to add that this needs to be done in.se()or.summary().