Mailman 3 January 2013 - SciPy-Dev

Bootstrap confidence limits code
by Constantine Evans 05 Oct '14

05 Oct '14

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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SLSQP Constrained Optimizer Status
by Rob Falck 25 Nov '13

25 Nov '13

I'm currently implementing the Sequential Least Squares Quadratic Programming (SLSQP) optimizer, by Dieter Kraft, for use in Scipy. The Fortran code being wrapped with F2PY is here: http://www.netlib.org/toms/733 (its use within Scipy has been cleared) SLSQP provides for bounds on the independent variables, as well as equality and inequality constraint functions, which is a capability that doesn't exist in scipy.optimize. Currently the code works, although the constraint normals are being generated by approximation. I'm working on a way to pass these in. I think the most elegant way will be a single function that returns the matrix of constraint normals. For a demonstration of what the code can do, here is an optimization of f(x,y) = 2xy + 2x - x**2 - 2y**2 Example 14.2 in Chapra & Canale gives the maximum as x=2.0, y=1.0. The unbounded optimization tests find this solution. As expected, its faster when derivatives are provided rather than approximated. Unbounded optimization. Derivatives approximated. Elapsed time: 1.45792961121 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] Unbounded optimization. Derivatives provided. Elapsed time: 1.03211402893 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] The following example uses an equality constraint to find the optimal when x=y. Bound optimization. Derivatives approximated. Elapsed time: 1.384973526 ms Results [[0.99999996845920858, 0.99999996845920858], -0.99999999999999889, 4, 0, 'Optimization terminated successfully.'] I've tried to conform to the syntax used by the other optimizers in scipy.optimize. The function definition and doc string are below. If anyone is interested in testing it out, let me know. def fmin_slsqp( func, x0 , eqcons=[], f_eqcons=None, ieqcons=[], f_ieqcons=None, bounds = [], fprime = None, fprime_cons=None,args = (), iter = 100, acc = 1.0E-6, iprint = 1, full_output = 0, epsilon = _epsilon ): """ Minimize a function using Sequential Least SQuares Programming Description: Python interface function for the SLSQP Optimization subroutine originally implemented by Dieter Kraft. Inputs: func - Objective function (in the form func(x, *args)) x0 - Initial guess for the independent variable(s). eqcons - A list of functions of length n such that eqcons[j](x0,*args) == 0.0 in a successfully optimized problem f_eqcons - A function of the form f_eqcons(x, *args) that returns an array in which each element must equal 0.0 in a successfully optimized problem. If f_eqcons is specified, eqcons is ignored. ieqcons - A list of functions of length n such that ieqcons[j](x0,*args) >= 0.0 in a successfully optimized problem f_ieqcons - A function of the form f_ieqcons(x0, *args) that returns an array in which each element must be greater or equal to 0.0 in a successfully optimized problem. If f_ieqcons is specified, ieqcons is ignored. bounds - A list of tuples specifying the lower and upper bound for each independent variable [ (xl0, xu0), (xl1, xu1), ...] fprime - A function that evaluates the partial derivatives of func fprime_cons - A function of the form f(x, *args) that returns the m by n array of constraint normals. If not provided, the normals will be approximated. Equality constraint normals precede inequality constraint normals. args - A sequence of additional arguments passed to func and fprime iter - The maximum number of iterations (int) acc - Requested accuracy (float) iprint - The verbosity of fmin_slsqp. iprint <= 0 : Silent operation iprint == 1 : Print summary upon completion (default) iprint >= 2 : Print status of each iterate and summary full_output - If 0, return only the minimizer of func (default). Otherwise, output final objective function and summary information. epsilon - The step size for finite-difference derivative estimates. Outputs: ( x, { fx, gnorm, its, imode, smode }) x - The final minimizer of func. fx - The final value of the objective function. its - The number of iterations. imode - The exit mode from the optimizer, as an integer. smode - A string describing the exit mode from the optimizer. Exit modes are defined as follows: -1 : Gradient evaluation required (g & a) 0 : Optimization terminated successfully. 1 : Function evaluation required (f & c) 2 : Number of equality constraints larger than number of independent variables 3 : More than 3*n iterations in LSQ subproblem 4 : Inequality constraints incompatible 5 : Singular matrix E in LSQ subproblem 6 : Singular matrix C in LSQ subproblem 7 : Rank-deficient equality constraint subproblem HFTI 8 : Positive directional derivative for linesearch 9 : Iteration limit exceeded -- - Rob Falck

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Vector Strength function
by Todd 22 Nov '13

22 Nov '13

I am interested in implementing a function for scipy. The function is called "vector strength". It is basically a measure of how reliably a set of events occur at a particular phase. It was originally developed for neuroscience research, to determine how well a set of neural events sync up with a periodic stimulus like a sound waveform. However, it is useful for determining how periodic a supposedly periodic set of events really are, for example: 1. Determining whether crime is really more common during a full moon and by how much 2. Determining how concentrated visitors to a coffee shop are during rush hour 3. Determining exactly how concentrated hurricanes are during hurricane season My thinking is that this could be implemented in stages: First would be a Numpy function that would add a set of vectors in polar coordinates. Given a number of magnitude/angle pairs it would provide a summed magnitude/angle pair. This would probably be combined with a cartesian<->polar conversion functions. Making use of this function would be a scipy function that would actually implement the vector strength calculation. This is done by treating each event as a unit vector with a phase, then taking the average of the vectors. If all events have the same phase, the result will have an amplitude of 1. If they all have a different phases, the result will have an amplitude of 0. It may even be worth having a dedicated polar dtype, although that may be too much. What does everyone think of this proposal?

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2D histogram: request for plotting variable bin size
by Frank Breitling 28 Mar '13

28 Mar '13

Hello, http://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram2d.html explains the usage of 2D histograms. Although it is possible to construct 2D histograms with a variable bin size, there is no simple way to plot them. Therefore I would like to request an implementation to imshow that allows the visualization of 2D histograms with variable bin size. I imagine a syntax like imshow(historgram2d, xedges=xedges, yedges=yedges) where xedges and yedges are the bin edges along the x- and y-axis. I have attached a short program and its output which constructs a numpy.historgram2d with variable bin width (xedges=[0,1,3]) and shows its bin content with imshow. I would highly appreciate if imshow would be extended to represent the histogram correctly. If there is an alternative solution, I would be interested to learn about it as well. Kind regards Frank

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The New Scipy Central Home page
by Surya Kasturi 05 Mar '13

05 Mar '13

Hey, As discussed before, I want to start my first contribution to SciPy by make Scipy Central new design. So, my idea is to make the site look "clean and simple". However, lots of things needed to be done, I just want to share this small piece. http://ksurya.github.com/scipycentral/ The whole point of sharing at this early is that I need some suggestions regarding some major design-changes in the code. 1. Remove "Tag cloud" from its current position and place on the background of "Heading: Scipy Central" 2. Submit Code, Submit Lib files, Submit Link.. part, I don't want to comment on it now. May be we don't need to change but can change some design! However, we can as well put them on on nav-bar Fundamentally, I want to hear some suggestions regarding these things.. Hope you like the page. Also, What about the "color". Is black okay? How about regular "scipy blue"? Not sure? Need some suggestion on it?

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64-bit sparse matrix indices
by Pauli Virtanen 19 Feb '13

19 Feb '13

Hi, I've been looking a bit at making sparse matrices work with 64-bit indices: https://github.com/pv/scipy-work/commits/ticket/1307 The motivation is that 32-bit indices on 64-bit machines don't allow representing sparse matrices with large nnz. One option A (currently there) is to allow both int32 and int64 as indices, and use the larger one only when required by nnz. The second option B would be to just use intp for everything. The problem with A is that I'm far from certain that I found all the corner cases yet, and I'm fairly certain there are some undiscovered bugs still somewhere. The test suite doesn't yet have the level of coverage on this issue I'd be comfortable with. The problem with B is that on 64-bit systems, it it increases the memory needs of sparse matrices by about 50%. However, as a solution it's more robust and elegant. Opinions on how it should work? Pauli

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0.12.0 release schedule
by Ralf Gommers 15 Feb '13

15 Feb '13

Hi all, It's time to start thinking about the 0.12.0 release. There's a number of cool new features, and a huge amount of maintenance work (thanks in large part to Pauli). I don't think there's any real blockers at the moment. We should merge as many open PRs as possible though. Does anyone still have important fixes or other things that should go in? I propose the following release schedule: Feb 9 : beta 1 Feb 23 : rc 1 Mar 9 : rc 2 (if needed) Mar 16 : final release Cheers, Ralf

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Expanding Scipy's KDE functionality
by Daniel Smith 07 Feb '13

07 Feb '13

Hello, This was started on a different thread, but I thought I would post a new thread focused on this. Currently, I have some existing code that implements the bandwidth selection algorithm from: Z. I. Botev, J. F. Grotowski, and D. P. Kroese. Kernel density estimation via diffusion. The Annals of Statistics, 38(5):2916-2957, 2010. Zdravko Botev implemented the code in MatLab which can be found here: http://www.mathworks.com/matlabcentral/fileexchange/14034-kernel-density-es… My code for that is here: https://github.com/Daniel-B-Smith/KDE-for-SciPy I assume I probably need to find a workaround to avoid the float128 in the function fixed_point before I can add it to SciPy. I wrote the code a couple of years ago, so it will take me a moment to map out the best workaround (there is a very large number being multiplied by a very small number). I can also add the 2d-version once I start integrating with SciPy. I have a couple of questions remaining. First, should I implement this in SciPy? StatsModels? Both? Secondly, can I use Cython to generate C code for the function fixed_point? Or do I need to write it up in the Numpy C API? If there is somewhere else I should post this and/or someone I should directly contact, I would greatly appreciate it. Thanks, Daniel

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Re: [SciPy-Dev] docstring standard: parameter shape description
by Joe Harrington 31 Jan '13

31 Jan '13

On Tue, 29 Jan 2013 10:09:54 -0500, Jonathan Guyer <guyer(a)nist.gov> wrote: > On Jan 28, 2013, at 7:35 PM, Joe Harrington wrote: > > > On Mon, 28 Jan 2013 13:47:26 -0800 Nathaniel Smith <njs(a)pobox.com> wrote: > > > >> "any size" should mean 0+. "absent" is not a size. If a function does > >> accept an optional final dimension, can we write that like 'shape (N, > >> D) or shape (N,)'? > > > > An array dimension cannot have 0 elements (the total size is the product > > of the shape tuple's elements). > > That's not true. An array dimension certainly can have 0 elements: > > >>> import numpy as np > >>> a = np.zeros((0,)) > >>> b = np.zeros((1, 0)) > >>> c = np.zeros((1, 0, 3)) > >>> a.shape, b.shape, c.shape > ((0,), (1, 0), (1, 0, 3)) > > For most applications, there is little utility in being able to > declare an "empty" array, but you can certainly do it (and for us, > it's handy for FiPy to partition PDE solutions across multiple > processors without having to special-case any processors that don't > get any solution nodes). > > As we reported in http://projects.scipy.org/numpy/ticket/1171, the > treatment of 0-length dimensions is buggy, but NumPy nonetheless > allows for it. I did the following test before I posted: >>> import numpy as np >>> a=np.array(((2,3),(3,4))) >>> a array([[2, 3], [3, 4]]) >>> a.shape (2, 2) >>> a.shape=(2,0,2) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: total size of new array must be unchanged So, you can't have a 0-element dimension in an array that is not already 0-size. I didn't think about 0-size arrays having some non-zero dimensions. I guess that makes mathematical sense, but then what is the point? Between this and your bug report, I wonder whether a 0-element array dimension is a feature you can rely on. Can a developer let us know if it's an accident that it works at all? I'll leave it to you to start a new thread on this and get the inconsistencies resolved, since you're the one using the feature. The report was last touched 3 years ago... --jh--

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Re: [SciPy-Dev] SciPy-Dev Digest, Vol 111, Issue 22
by The Helmbolds 30 Jan '13

30 Jan '13

Re: use of 1-D to mean "one-dimensional" See---> D:\SciPy Editing--Master Copy\How to Document ScipY\Questions+Answers.mht Bob H >> param1 : array_like >> Can be any shape... >> param2 : array_like, shape(N,) >> A 1D array, specified because ``N`` is used. >> I tried to find a grammar site to determine what the standard was for >> dimensionality, but didn't find anything. Is it 1-D, 1D, or 1-d? Seems >> that most people are going with 1D though, but that determination should >> also be part of the standard if we accept Ralf's initial proposal. > >I'm pretty sure we decided on 1-D, but it's not written down either. > >Ralf

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