[SciPy-User] Least-squares fittings with bounds: why is scipy not up to the task?
Jonathan Helmus
jjhelmus@gmail....
Thu Mar 15 16:08:27 CDT 2012
MINUIT , or more precisely Minuet2 is part of the ROOT which is GPL
version 2 licensed. There already exists a python wrapper for the
package (http://code.google.com/p/pyminuit/), which is also GPL
licensed. I expect the licensing would cause problems if one wanted to
including the package in scipy.
The code in my repo on the other hand is BSD licensed and isn't based
off MINUIT. I merely used the same mathematical functions (sin, sqrt,
arcsin, etc) for the variable transforms which are mentioned in MINUIT's
User Manual.
- Jonathan Helmus
william ratcliff wrote:
> What is the license for MINUIT?
>
> On Thu, Mar 15, 2012 at 3:50 PM, Jonathan Helmus <jjhelmus@gmail.com
> <mailto:jjhelmus@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@eso.org <mailto:eemselle@eso.org>>
> > *To:* Matthew Newville <matt.newville@gmail.com
> <mailto:matt.newville@gmail.com>>
> > *Cc:* scipy-user@scipy.org <mailto:scipy-user@scipy.org>;
> scipy-user@googlegroups.com <mailto:scipy-user@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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