Hi,
I really need to add some type checking to minfx. Maybe x0 is being
sent in as a Python list rather than a numpy array? Or maybe you need
to use the specific_analyses.relax_fit.optimisation wrapper functions
that do the type conversions correctly for you. These are the only
functions that should be sent into minfx as the C module does not
handle numpy arrays. I originally used numpy arrays but removed it
due to Python version incompatibilities causing huge headaches with
the distribution files (r12625,
http://article.gmane.org/gmane.science.nmr.relax.scm/12064).
Regards,
Edward
On 27 August 2014 08:45, Troels Emtekær Linnet <tlin...@nmr-relax.com> wrote:
> Hi Edward.
>
> I get:
>
> Verify, without constraints, C code BFGS
> Traceback (most recent call last):
> File "profiling_relax_fit.py", line 401, in <module>
> main()
> File "profiling_relax_fit.py", line 177, in main
> cProfile.run('verify(min_algor="BFGS",
> constraints=%s)'%(constraints), filename)
> File
> "/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/cProfile.py",
> line 29, in run
> prof = prof.run(statement)
> File
> "/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/cProfile.py",
> line 135, in run
> return self.runctx(cmd, dict, dict)
> File
> "/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/cProfile.py",
> line 140, in runctx
> exec cmd in globals, locals
> File "<string>", line 1, in <module>
> File "profiling_relax_fit.py", line 323, in verify
> results = generic_minimise(func=func, dfunc=dfunc, d2func=d2func,
> args=(), x0=x0, min_algor=E.min_algor, min_options=E.min_options,
> func_tol=E.func_tol, grad_tol=E.grad_tol, maxiter=E.max_iterations,
> A=E.A, b=E.b, full_output=True, print_flag=E.verbosity)
> File
> "/Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/minfx/generic.py",
> line 322, in generic_minimise
> results = bfgs(func=func, dfunc=dfunc, args=args, x0=x0,
> min_options=min_options, func_tol=func_tol, grad_tol=grad_tol,
> maxiter=maxiter, full_output=full_output, print_flag=print_flag,
> print_prefix=print_prefix)
> File
> "/Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/minfx/bfgs.py",
> line 49, in bfgs
> results = min.minimise()
> File
> "/Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/minfx/base_classes.py",
> line 299, in minimise
> self.update()
> File
> "/Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/minfx/bfgs.py",
> line 151, in update_bfgs
> yk = self.dfk_new - self.dfk
> TypeError: unsupported operand type(s) for -: 'list' and 'list'
>
> Best
> Troels
>
> 2014-08-26 19:52 GMT+02:00 Edward d'Auvergne <edw...@nmr-relax.com>:
>> The exponential curve-fitting Hessian from the
>> target_functions.relax_fit.d2func() function is bug free and fully
>> functional.
>>
>> Regards,
>>
>> Edward
>>
>>
>> On 26 August 2014 19:24, Edward d'Auvergne <edw...@nmr-relax.com> wrote:
>>> Oh well, I had some time between frame order calculations and decided
>>> to practice my C coding a little. The full exponential curve Hessian
>>> is now implemented. It may still need debugging. But you can now use
>>> the full Newton algorithm in your timing tests and comparisons to
>>> Scipy.
>>>
>>> Regards,
>>>
>>> Edward
>>>
>>> On 26 August 2014 13:32, Edward d'Auvergne <edw...@nmr-relax.com> wrote:
>>>> Test it and see ;) And if you are interested in the speed up in this
>>>> part of the analysis, you can take it one more step and implement the
>>>> Hessian in the C modules and then time the Newton algorithm!
>>>>
>>>> Regards,
>>>>
>>>> Edward
>>>>
>>>> On 26 August 2014 13:26, Troels Emtekær Linnet <tlin...@nmr-relax.com>
>>>> wrote:
>>>>> Should this be used instead then in R2eff minimisation?
>>>>>
>>>>> Is it faster than simplex?
>>>>>
>>>>> Best
>>>>> Troels
>>>>>
>>>>> 2014-08-26 13:04 GMT+02:00 Edward d'Auvergne <edw...@nmr-relax.com>:
>>>>>> Oh, I forgot to mention, but I also converted the
>>>>>> Relax_fit.test_curve_fitting_height and
>>>>>> Relax_fit.test_curve_fitting_volume system tests to use BFGS
>>>>>> optimisation. This is one of the best optimisation techniques when
>>>>>> only the function and gradient are present, as it tries to numerically
>>>>>> approximate the Hessian matrix, updating it as the algorithm moves
>>>>>> along. It is fast and performs incredibly well, so it is a widely
>>>>>> used algorithm. The system tests using BFGS demonstrate that the
>>>>>> gradient works very well for optimisation. It isn't as fast as Newton
>>>>>> optimisation however.
>>>>>>
>>>>>> Regards,
>>>>>>
>>>>>> Edward
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> On 26 August 2014 13:00, Edward d'Auvergne <edw...@nmr-relax.com> wrote:
>>>>>>> Hi Troels,
>>>>>>>
>>>>>>> I've now implemented the exponential curve-fitting dfunc() function
>>>>>>> for calculating the gradient. This includes:
>>>>>>>
>>>>>>> - The Python wrapper function
>>>>>>> specific_analyses.relax_fit.optimisation.dfunc_wrapper(),
>>>>>>> - The target_functions/c_chi2.c function dchi2(),
>>>>>>> - The target_functions/exponential.c functions exponential_dI0() and
>>>>>>> exponential_dR(),
>>>>>>> - The target_functions.relax_fit C module dfunc() Python function.
>>>>>>>
>>>>>>> I have tested the gradient using the numerical integration in the
>>>>>>> test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py
>>>>>>> file to determine what the chi-squared gradient should be at different
>>>>>>> parameter combinations. And this has been converted into a few unit
>>>>>>> tests. As this works, that means that the jacobian() function of the
>>>>>>> C module should also be correct and bug-free, hence you should be able
>>>>>>> to use it to obtain the covariance matrix.
>>>>>>>
>>>>>>> This is all I will do for now. All that is left is to do for the
>>>>>>> target_functions.relax_fit C module is simply the same thing, but for
>>>>>>> the Hessian. Feel free to give this a go if you are interested. If I
>>>>>>> have time in the future, I might add this too.
>>>>>>>
>>>>>>> Regards,
>>>>>>>
>>>>>>> Edward
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On 24 August 2014 17:56, Troels E. Linnet
>>>>>>> <no-reply.invalid-addr...@gna.org> wrote:
>>>>>>>> URL:
>>>>>>>> <http://gna.org/task/?7822>
>>>>>>>>
>>>>>>>> Summary: Implement user function to estimate R2eff and
>>>>>>>> associated errors for exponential curve fitting.
>>>>>>>> Project: relax
>>>>>>>> Submitted by: tlinnet
>>>>>>>> Submitted on: Sun 24 Aug 2014 03:56:36 PM UTC
>>>>>>>> Should Start On: Sun 24 Aug 2014 12:00:00 AM UTC
>>>>>>>> Should be Finished on: Sun 24 Aug 2014 12:00:00 AM UTC
>>>>>>>> Category: relax's source code
>>>>>>>> Priority: 5 - Normal
>>>>>>>> Status: In Progress
>>>>>>>> Percent Complete: 0%
>>>>>>>> Assigned to: tlinnet
>>>>>>>> Open/Closed: Open
>>>>>>>> Discussion Lock: Any
>>>>>>>> Effort: 0.00
>>>>>>>>
>>>>>>>> _______________________________________________________
>>>>>>>>
>>>>>>>> Details:
>>>>>>>>
>>>>>>>> A verification script, showed that using scipy.optimize.leastsq
>>>>>>>> reaches the
>>>>>>>> exact same parameters as minfx for exponential curve fitting.
>>>>>>>>
>>>>>>>> The verification script is in:
>>>>>>>> test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py
>>>>>>>> test_suite/shared_data/curve_fitting/profiling/verify_error.py
>>>>>>>>
>>>>>>>> The profiling script shows that a 10 X increase in speed can be
>>>>>>>> reached by
>>>>>>>> removing
>>>>>>>> the linear constraints when using minfx.
>>>>>>>>
>>>>>>>> The profiling also shows that scipy.optimize.leastsq is 10X as fast as
>>>>>>>> using
>>>>>>>> minfx, even without linear constraints.
>>>>>>>>
>>>>>>>> scipy.optimize.leastsq is a wrapper around wrapper around MINPACK's
>>>>>>>> lmdif and
>>>>>>>> lmder algorithms.
>>>>>>>>
>>>>>>>> MINPACK is a FORTRAN90 library which solves systems of nonlinear
>>>>>>>> equations, or
>>>>>>>> carries out the least squares minimization of the residual of a set of
>>>>>>>> linear
>>>>>>>> or nonlinear equations.
>>>>>>>>
>>>>>>>> The verification script also shows, that a very heavy and time
>>>>>>>> consuming
>>>>>>>> monte carlo simulation of 2000 steps, reaches the same errors as the
>>>>>>>> errors
>>>>>>>> reported by scipy.optimize.leastsq.
>>>>>>>>
>>>>>>>> The return from scipy.optimize.leastsq, gives the estimated
>>>>>>>> co-variance.
>>>>>>>> Taking the square root of the co-variance corresponds with 2X error
>>>>>>>> reported
>>>>>>>> by minfx after 2000 Monte-Carlo simulations.
>>>>>>>>
>>>>>>>> This could be an extremely time saving step, when performing model
>>>>>>>> fitting in
>>>>>>>> R1rho, where the errors of the R2eff values, are estimated by
>>>>>>>> Monte-Carlo
>>>>>>>> simulations.
>>>>>>>>
>>>>>>>> The following setup illustrates the problem.
>>>>>>>> This was analysed on a: MacBook Pro, 13-inch, Late 2011.
>>>>>>>> With no multi-core setup.
>>>>>>>>
>>>>>>>> Script running is:
>>>>>>>> test_suite/shared_data/dispersion/Kjaergaard_et_al_2013/2_pre_run_r2eff.py
>>>>>>>>
>>>>>>>> This script analyses just the R2eff values for 15 residues.
>>>>>>>> It estimates the errors of R2eff based on 2000 Monte Carlo simulations.
>>>>>>>> For each residues, there is 14 exponential graphs.
>>>>>>>>
>>>>>>>> The script was broken after 35 simulations.
>>>>>>>> This was measured to 20 minutes.
>>>>>>>> So 500 simulations would take about 4.8 Hours.
>>>>>>>>
>>>>>>>> The R2eff values and errors can by scipy.optimize.leastsq can instead
>>>>>>>> be
>>>>>>>> calculated in: 15 residues * 0.02 seconds = 0.3 seconds.
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> _______________________________________________________
>>>>>>>>
>>>>>>>> Reply to this item at:
>>>>>>>>
>>>>>>>> <http://gna.org/task/?7822>
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> Message sent via/by Gna!
>>>>>>>> http://gna.org/
>>>>>>>>
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> relax (http://www.nmr-relax.com)
>>>>>>>>
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