Mailman 3 March 2014 - SciPy-Dev

Re: [SciPy-Dev] removing umfpack wrapper
by Robert Cimrman Nov. 23, 2014

Nov. 23, 2014

On 11/12/2013 11:22 PM, Ralf Gommers wrote: > On Tue, Nov 12, 2013 at 10:20 AM, Robert Cimrman <cimrman3(a)ntc.zcu.cz>wrote: > >> Hi Ralf, >> >> On 11/10/2013 04:36 PM, Ralf Gommers wrote: >>> Hi all, >>> >>> This is long overdue, but I think it's time to remove the UMFPACK wrapper >>> before the next release. I'd like some feedback on how to do that and to >>> what package (if any) to point existing users. >>> >>> As for why to remove the wrapper, see: >>> https://github.com/scipy/scipy/issues/3002 >>> http://permalink.gmane.org/gmane.comp.python.scientific.user/20451 >>> Short summary: UMFPACK changed from a BSD-compatible license to GPL at >> some >>> point. >>> >>> The deprecation warning in sparse.linalg has been referring people to >>> scikits.umfpack until now, however that package has disappeared >> completely >>> as far as I can tell. I suspect it was in the old scikits svn repo and >> was >>> never moved before that was killed. The alternatives seems to be >> Pysparse ( >> >> I missed that the scikit was lost in transition... >> >> How much work would be to move it to the new site - move from SVN to git, >> and? >> I would be willing to do it, if there is interest. >> > > Hi Robert, I think the moving wouldn't be a lot of work (assuming svn > access can still be arranged). If you'd revive the scikit it's not a > one-time effort though - it's only useful if the code is being maintained > imho. That can still be low effort perhaps, but with reviewing some PRs, > maintenance releases, putting it on pypi, etc. I'd expect at least an hour > a week or so. > > Are there scikits.umfpack users now? Would it make sense to salvage > whatever is useful from the scikit and contribute it to pysparse instead? We are using it in sfepy as the default direct solver. It seems to me that the pysparse interface requires the input matrix to be in the LL (linked list) format, which is unfortunate for us, as we use CSR. The scipy (and former scikit) umfpack wrappers have used CSR, so no copies were necessary. (After all, I have created the original scipy wrappers to be used from sfepy in the first place...) So in case others are not interested in having the scikit, I can see two possible solutions from my perspective: either enhance pysparse interface to allow CSR as well, or move the wrappers to sfepy (which I maintain anyway, but swig -> cython conversion would be needed). Not sure yet which solution I prefer. Cheers, r. > Cheers, > Ralf >> >> Cheers, >> r. >> >>> http://pysparse.sourceforge.net) and PyUBlasExt ( >>> https://pypi.python.org/pypi/PyUblasExt). Scikits.sparse is dead(-ish) >> and >>> doesn't wrap umfpack it looks like. PyUBlasExt has something that looks >>> like a wrapper but for C++ (?). So can we refer people to pysparse? >>> >>> Other question: do we call Pysparse instead of scikits.umfpack when >>> available? And if so, by default or not? Or do we rip out everything that >>> sits under useUmfpack=True? >>> >>> Cheers, >>> Ralf

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Linalg: function for diagonalize hermitian commuting matrices simultaneously
by Lars-Hendrik Frahm Oct. 7, 2014

Oct. 7, 2014

Dear SciPy Developers, I like to contribute a function for simultaneously diagonalize a set of hermitian commuting matrices. Especially for matrices with degenerate eigenvalues. Is there need for something like that in SciPy ? I think it could be helpful for someone else and I didn't find it in recent SciPy versions or on the mailing list. Hope to hear from you.

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Bootstrap confidence limits code
by Constantine Evans Oct. 4, 2014

Oct. 4, 2014

Hello everyone, A few years ago I implemented a scikit for bootstrap confidence limits (https://github.com/cgevans/scikits-bootstrap). I didn’t think much about it after that until recently, when I realized that some people are actually using it, and that there’s apparently been some talk about implementing this functionality in either scipy.stats or statsmodels (I should thank Randal Olson for discussing this and bringing it to my attention). As such I’ve rewritten most of the code, and written up some docstrings. The current code can do confidence intervals with basic percentile interval, bias-corrected accelerated, and approximate bootstrap confidence methods, and can also provide bootstrap and jackknife indexes. Most of it is implemented from the descriptions in Efron and Tibshirani’s Introduction to the Bootstrap, but the ABC code at the moment is a port from the modified-BSD-licensed bootstrap package for R (not the boot package) as I’m not entirely confident in my understanding of the method. And so, I have a few questions for everyone: * Is there any interest in including this sort of code in either scipy.stats or statsmodels? If so, where do people think would be the better place? The code is relatively small; at the moment it is less than 200 lines, with docstrings probably making up 100 of those lines. * Also, if so, what would need to be changed, added, and improved beyond what is mentioned in the Contributing to Scipy part of the reference guide? I’m never a fan of my own code, and imagine quite a bit would need to be fixed; I know tests will need to be added too. In addition, I have a few questions about what would be better practice for the API, and I haven’t really found a guide on best practices for Scipy: * When I started writing the code, I wrote a single function ci for confidence intervals, with a method argument to choose the method. This is easy for users, especially so that they don’t have to look through documentation to realize that BCA is the most generally useful method (at least from everything I’ve read) and that there really isn’t any reason to use many of the simpler methods. However, ABC takes different paramenters, and needs a statistic function that takes weights, which makes this single-function organization trickier. At the moment, I have a separate function for ABC. Would it be better to split up all the methods to their own functions? * ABC requires a statistic function that takes weights. I’ve noticed that things like np.average takes a weights= argument. Would it be better to require input of a stat(data,weights) function, or input of a stat(data,weights=) with weights as a named argument? The latter would be nice in terms of allowing the same function to be used for all methods, but would make it impossible to use a lambda for the function. Is there some other method of doing this entirely? * Are there any missing features that anyone thinks should be added? I apologize if much of this is answered elsewhere, I just haven’t found any of it; I also apologize if this is far too long-winded and confusing! Regards, Constantine Evans

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Levenberg-Marquardt Implementation
by Nico Del Piano June 18, 2014

June 18, 2014

Hi all, I am Nicolas Del Piano, and currently studying the last year of computer science career. I was searching for a GSoC project that involves mathematical concepts, and I found one very interesting. I have experience on python programming and I studied calculus, so I have a background of the problem context. I am interested on the implementation and testing of Levenberg-Marquardt / trust region nonlinear least squares algorithm. Here are some useful links, that I have searched to having a reference about the problem: http://ananth.in/docs/lmtut.pdf (Introduction) www.cs.nyu.edu/~roweis/notes/lm.pdf (Optimization) http://scribblethink.org/Computer/Javanumeric/index.html (java implementation) I would be glad if there is an interested mentor to discuss the issues, talk about the problem and its possible implementations, and provide me some support and guide. Thanks! Regards.

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Re: [SciPy-Dev] Consideration of differential evolution minimizer being added to scipy.optimize.
by Andrew Nelson May 12, 2014

May 12, 2014

Dear all, I've put some more work into the differential evolution (another global optimizer) code I would like to contribute to scipy.optimize. Since this is the first time I'm contributing to scipy I'd would like a code review, with a view to putting in a pull request in the near future. The changes/additions I've made are at (under files changed): https://github.com/andyfaff/scipy/compare/scipy:master...master Some of the comments when I first discussed this were for benchmarking DE to see if it had comparable performance to basinhopping. To that end I've added some code and extra test functions (http://en.wikipedia.org/wiki/Test_functions_for_optimization) for benchmarking global optimizers. You can see the output of the benchmarking at: https://gist.github.com/andyfaff/9439229 >From the test functions differential_evolution doesn't do too badly, especially when no gradient is available for the function to be minimized. Having said that, I do recognise that you sometimes need to tune the optimizer for a specific problem. My specific motivation for adding differential_evolution is for curve_fitting. For most of my problems function expense isn't enormous, so I don't regard number of function evaluations to be all that important. In those cases finding the global minimum is the key. I've tried comparing basinhopping to DE for harder curvefitting problems and in the example I tried the number of function evaluations was a lot smaller for DE (http://www.itl.nist.gov/div898/strd/index.html - Thurber). (One can always use Levenberg Marquardt to 'polish' the end result) Of course, there are problems where function expense is enormous. However, if that is truly horrendous one can always use the callback function to terminate if you think one is getting towards a solution. cheers, Andrew. -- _____________________________________ Dr. Andrew Nelson _____________________________________

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conv2 and normxcorr2
by awebster＠falsecolour.com May 12, 2014

May 12, 2014

I noticed a couple of popular matlab functions - conv2 and normxcorr2 were not present in the scipy.signal packages. I would like to submit them for addition. Can anyone point me on instructions on how to write such a thing? Below are examples. # 2D convolution using the convolution's FFT property def conv2(a,b): ma,na = a.shape mb,nb = b.shape return fft.ifft2(fft.fft2(a,[2*ma-1,2*na-1])*fft.fft2(b,[2*mb-1,2*nb-1])) # compute a normalized 2D cross correlation using convolutions # this will give the same output as matlab, albeit in row-major order def normxcorr2(b,a): c = conv2(a,flipud(fliplr(b))) a = conv2(a**2, ones(b.shape)) b = sum(b.flatten()**2) c = c/sqrt(a*b) return c -- Aaron Webster

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1.0 roadmap: weave
by Arnd Baecker May 6, 2014

May 6, 2014

Hi, > On Sat, Sep 21, 2013 at 8:54 PM, Ralf Gommers <ralf.gommers(a)gmail.com> wrote: [...] > weave > ````` > This is the only module that was not ported to Python 3. Effectively it's > deprecated (not recommended to use for new code). In the future it should be > removed from scipy (can be made into a separate module). Let me just mention that I am worried about the removal of weave: a substantial amount of code developed in our group over the years uses weave. Porting this to cython seems not trivial, in particular, because many of the original authors (diploma students, PhD students, ...) have left academia by now. Well, as porting weave to Python 3 is way out of my capabilities, this is just a comment in the hope that someone could give this a try (if at some point testing would be needed, I could try to help, of course). Best, Arnd

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Re: [SciPy-Dev] Multivariate ctypes integration PR
by Brian Lee Newsom April 29, 2014

April 29, 2014

Thank you to everyone involved in assisting to move this forward. It is definitely a confusing project for me but something I am committed to finishing and getting in the library as soon as possible. I appreciate the help and I will do my best to address the issues Pauli presented. Thanks, Brian

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GSoC Draft Proposal: Rewrite and improve cluster package in Cython
by Richard Tsai April 22, 2014

April 22, 2014

Hi all, I wrote a draft proposal for my GSoC about the cluster package. I post to the list hoping for advice. However, as Ralf said, cluster is not well maintained now. And I am still not be able to find someone who know about cluster analysis to mentor me. If you have any suggestions for my proposal, or are willing to mentor me, please let me know and I will be really grateful. Regards, Richard Proposal Title: SciPy: Rewrite and improve cluster package in Cython Proposal Abstract According to the roadmap to SciPy 1.0, the cluster package needs a Cython rewrite the make it more maintainable and efficient. Besides, there's room for improvement in cluster.vq module. Some useful features can be added and the performance can be improved when dealing with large datasets. Proposal Detailed Description/Timeline There's an experimental Cython implementation of the vq module in the source tree. However, it has not been maintained for about 2 years and it only supports single precision datasets, and it's also slower than the original implementation. I plan to start with some cleanup job, then finish the double precision support. After some optimizations and tuning it should be mature enough the replace the original implementation. After that, I'm going to implementation a mini-batch optimization for kmeans/kmeans2 function based on a paper ("Web-Scale K-Means Clustering<http://dl.acm.org/citation.cfm?id=1772862>") and it should greatly improve the performance for large datasets. In addition, I think the support for automatically determine the number of clusters via some methods (e.g. gap statistics<http://www.stanford.edu/~hastie/Papers/gap.pdf>) can be included in this module. As for the hierarchy module, it is rather full-featured now, but the Cython rewrite has yet begun. I'll rewrite the high level part in Cython first since it it convenient to call the original C underlying functions in Cython code. I'll migrate the underlying part from C to Cython gradually at last. My detailed timeline is as follows. - Week 1: Do some cleanup for the existing experimental Cython version of vq (bugs, docs, etc.), unit tests, performance benchmarks for datasets of various sizes and distributions. - Week 2: Finish the double precision support in the Cython version of vq, try to migrate some Python code to Cython to gain performance improvement. - Week 3: Do some performance profiling, continue to optimize the performance of vq, try to replace the original C implementation with the new Cython implementation. - Week 4: Implement the mini-batch K-means algorithm. - Week 5: Add support for automatically determine the number of clusters. - Week 6: Maneuver time. Finish the work that is behind schedule, and try some potential optimizations. - Week 7: Build a framework for the Cython implementation of the hierarchy module. The work should be just translate the wrapper functions in hierarchy_wrap.c into Cython so there may be no performance gains by then. - Week 8-9: Rewrite the underlying implementation of the hierarchy module in Cython. The major work is to translate hierarchy.c into Cython. - Week 10: Optimize the Cython implementation of the hierarchy module, replace the original implementation if possible. - Remaining time (if there is): Improve the documents, add some sample code especially for the hierarchy module. Code Sample My previous patches to SciPy can be found in https://github.com/scipy/scipy/pulls/richardtsai?state=closed I haven't submitted code to the cluster package but I'll probably make a related PR soon.

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ANN: NumPy 1.8.1 release
by Julian Taylor March 28, 2014

March 28, 2014

Hello, I'm happy to announce the of Numpy 1.8.1. This is a bugfix only release supporting Python 2.6 - 2.7 and 3.2 - 3.4. More than 48 issues have been fixed, the most important issues are listed in the release notes: https://github.com/numpy/numpy/blob/maintenance/1.8.x/doc/release/1.8.1-not… Compared to the last release candidate we have fixed a regression of the 1.8 series that prevented using some gufunc based linalg functions on larger matrices on 32 bit systems. This implied a few changes in the NDIter C-API which might expose insufficient checks for error conditions in third party applications. Please check the release notes for details. Source tarballs, windows installers and release notes can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.8.1 Cheers, Julian Taylor

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