[SciPy-User] Least-squares fittings with bounds: why is scipy not up to the task?

[william ratcliff]

What is the license for MINUIT?

On Thu, Mar 15, 2012 at 3:50 PM, Jonathan Helmus <jjhelmus at gmail.com> wrote:

> I know I am jumping into this thread late and it has drifted into
> another topics but I have some code that others might be interested in.
> With all the discussion of bounded leastsq and variable substitution I
> recalled that I had a wrapped version of leastsq in a larger project
> that allows for min, max bound using the variable transformations that
> MINUIT uses.  I pulled out the necessary functions, refactored the code
> and made a github repo in case anyone is interested
> (https://github.com/jjhelmus/leastsqbound-scipy).  This might make a
> good jumping off point for a more complete bounded leastsq optimizer
> that David had in mind.
>
> - Jonathan Helmus
>
> David Baddeley wrote:
> > From a pure performance perspective, you're probably going to be best
> > setting your bounds by variable substitution (particularly if they're
> > only single-ended - x**2 is cheap) - you really don't want to have the
> > for loops, dictionary lookups and conditionals that lmfit introduces
> > for it's bounds checking inside your objective function.
> >
> > I think a high level wrapper that permitted bounds, an unadulterated
> > goal function, and setting which parameters to fit, but also retained
> > much of the raw speed of leastsq could be accomplished with some
> > clever on the fly code generation (maybe also using Sympy to
> > automatically derive the Jacobian). Would make an interesting project ...
> >
> > David
> >
> > ------------------------------------------------------------------------
> > *From:* Eric Emsellem <eemselle at eso.org>
> > *To:* Matthew Newville <matt.newville at gmail.com>
> > *Cc:* scipy-user at scipy.org; scipy-user at googlegroups.com
> > *Sent:* Friday, 9 March 2012 12:17 PM
> > *Subject:* Re: [SciPy-User] Least-squares fittings with bounds: why is
> > scipy not up to the task?
> >
> >
> >
> > > Yes, see https://github.com/newville/lmfit-py,  which does everything
> > > you ask for, and a bit more, with the possible exception of "being
> > > included in scipy".  For what its worth, I work with Mark Rivers
> > > (who's no longer actively developing Python), and our group is full of
> > > IDL users who are very familiar with Markwardt's implementation.
> > >
> > > The lmfit-py version uses scipy.optimize.leastsq(), which uses MINPACK
> > > directly, so has the advantage of not being implemented in pure IDL or
> > > Python. It is definitely faster than mpfit.py.
> > >
> > > With lmfit-py, one writes a python function-to-minimize that takes a
> > > list of Parameters instead of the array of floating point variables
> > > that scipy.optimize.leastsq() uses. Each Parameter can be freely
> > > varied of fixed, have upper and/or lower bounds placed on them, or be
> > > written as algebraic expressions of other Parameters.  Uncertainties
> > > in varied Parameters and correlations between Parameters are estimated
> > > using the same "scaled covariance" method as used in
> > > scipy.optimize.curve_fit().  There is limited support for
> > > optimization methods other than scipy.optimize.leastsq(), but I don't
> > > find these methods to be very useful for the kind of fitting  problems
> > > I normally see, so support for them may not be perfect.
> > >
> > > Whether this gets included into scipy is up to the scipy developers.
> > > I'd be happy to support this module within scipy or outside scipy.
> > > I have no doubt that improvements could be made to lmfit.py.  If you
> > > have suggestion, I'd be happy to hear them.
> >
> > looks great! I'll have a go at this, as mentioned in my previous post. I
> > believe that leastsq is probably the fastest anyway (according to the
> > test Adam mentioned to me today) so this could be it. I'll make a test
> > and compare it with mpfit (for the specific case I am thinking of, I am
> > optimising over ~10^5-6 points with ~90 parameters...).
> >
> > thanks again for this, and I'll try to report on this (if relevant) asap.
> >
> > Eric
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