On Thu, Jan 24, 2019 at 1:46 PM Stefan van der Walt <stefanv@berkeley.edu> wrote:
Hi Josef,
On Thu, 24 Jan 2019 11:26:09 -0500, josef.pktd@gmail.com wrote:
I think making initial values compulsory is too much of a break with tradition. IMO, a warning and better documentation would be more appropriate.
https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fi...
does not show an example with starting values. curve_fit could issue a warning if p0 is not specified, or warn if convergence fails and p0 was not specified.
Isn't the greater danger that convergence succeeds, with p0 unspecified, and the resulting model not being at all what the user had in mind?
Unless the optimization problem is globally convex, the user always needs to check the results.
I think it should also be possible to improve the default starting values, e.g. if the function fails or if bounds are provided.
This is the type of magic I hope we can avoid. Having different execution paths based on some vaguely defined notion of perceived failure seems dangerous at best.
I there is no guarantee for a global optimum, it's still what either the program or the user has to do. E.g. for statsmodels (very rough guess on numbers) 90% of the cases work fine 10% of the cases the data is not appropriate, singular, ill conditioned or otherwise "not nice" 10% of the cases the optimizer has problems and does not converge. In this last case either the program or the user needs to work more: We can try different optimizers, e.g. start with nelder-mead before switching to a gradient optimizer. Or, switch to global optimizer from scipy, if the underlying model is complex and might not be well behaved. or pure man's global optimizer: try out many different random or semi-random starting values. (and if all fails go back to the drawing board and try to find a parameterization that is better behaved.) statsmodels is switching optimizers in some cases, but in most cases it is up to the user to change the optimizers after convergence failure. However, we did select default optimizers by which scipy optimizer seems to work well for the various cases. Stata is also switching optimizers in some cases, and AFAIR has in some cases and option to "try harder". statsmodels is still missing an automatic "try harder" option, that automatically switches optimizers on convergence failure.
I'm not a user of curve_fit, but I guess there might be a strong selection bias in use cases when helping out users that run into problems.
I agree; and I think this can be accomplished by better documentation, helpful warnings, and assisting the user in choosing correct parameters.
The main question for me is whether the warnings and improved documentation are enough, or whether curve_fit needs to force every user to specify the starting values. i.e. I think Try automatic first, and if that does not succeed, then the user has to think again, is more convenient, than "you have to think about your problem first, don't just hit the button". Josef
Best regards, Stéfan _______________________________________________ SciPy-Dev mailing list SciPy-Dev@python.org https://mail.python.org/mailman/listinfo/scipy-dev