Mailman 3 August 2011 - SciPy-Dev

SLSQP Constrained Optimizer Status
by Rob Falck Nov. 24, 2013

Nov. 24, 2013

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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prepending underscores to private module names
by Ralf Gommers May 16, 2012

May 16, 2012

Hi, A while ago we discussed making a clear distinction between public and private modules by using underscores consistently, see http://thread.gmane.org/gmane.comp.python.scientific.devel/14936. I've done this for a few modules now, see https://github.com/rgommers/scipy/tree/underscores. Most files could simply be underscored, some other would conflict with extension modules so I also had to append _py to the name. Also I added a file doc/source/api.rst that states which modules are public (up for discussion of course). With google code search I checked how common it is to import from modules that are supposed to be private, for example "from scipy.optimize.optimize import fmin". It's not very common but does happen, so deprecation warnings should be put in. Before going further I'd like to check that this looks okay to people. What do you think? Cheers, Ralf <https://github.com/rgommers/scipy/tree/underscores>

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studentized range approximations
by Roger Lew March 28, 2012

March 28, 2012

Hi, I have implemented some approximations for studentized range quantiles and probabilities based on John R. Gleason's (1999) "An accurate, non-iterative approximation for studentized range quantiles." Computational Statistics & Data Analysis, (31), 147-158. Probability approximations rely on scipy.optimize.fminbound. The functions accept both scalars or array-like data thanks to numpy.vectorize. A fair amount of validation and testing has been conducted on the code. More details can be found here: http://code.google.com/p/qsturng-py/ I welcome any thoughts as to whether you all think this might be useful to add to SciPy or make into a scikit. Any general comments would be helpful as well. I should mention I'm a cognitive neuroscientist by trade, my use of statistical jargon probably isn't that good. Regards, Roger Roger Lew

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installation of scipy on Mac OS X 10.7
by Ben Willmore Jan. 26, 2012

Jan. 26, 2012

I have spent a day attempting to get a version of scipy that passes unit tests on Mac OS X 10.7. I hope the results of my investigations (below) are useful to others trying to do the same thing. Ben == Introduction I have attempted to obtain a version of scipy on Mac OS X 10.7 that passes unit tests, both by downloading it, or by compiling against the system's python, using the three C compilers available, and various compile flags. == Conclusions 1. Both Enthought Python 7.1 and the scipy superpack crash during scipy unit tests. 2. Compilation with the default C compiler (llvm-gcc-4.2) also leads to a crash during unit tests. 3. Compilation using either of the other C compilers (gcc-4.2 or clang) allows scipy to complete unit tests, only if the fortran flag --ff2c is used. In both cases, scipy passes almost all tests -- producing errors on one, and failing two more. With gcc-4.2, numpy passes all tests. With clang, numpy fails one test. 4. The following recipe was most successful for me: * Remove everything in /usr/local and /Library/Python/2.7/site-packages (BEWARE! These directories may well contain stuff you care about) * Install gfortran from here: http://r.research.att.com/gfortran-lion-5666-3.pkg mkdir ~/tmp cd ~/tmp git clone git://github.com/numpy/numpy.git cd numpy export CC=gcc-4.2 export CXX=g++-4.2 export FFLAGS=-ff2c python setupegg.py build --fcompiler=gfortran sudo python setupegg.py install cd ~/tmp git clone git://github.com/scipy/scipy.git cd scipy export CC=gcc-4.2 export CXX=g++-4.2 export FFLAGS=-ff2c python setupegg.py build --fcompiler=gfortran sudo python setupegg.py install (repeat for matplotlib, ipython ...) == Details of test results = Enthought python 7.1 numpy.test(): OK scipy.test(): Crash (Segmentation fault) = Scipy superpack numpy.test(): 3 failures: FAIL: test_umath_complex.TestCsqrt.test_special_values(<ufunc 'sqrt'>, 1, inf, inf, inf) FAIL: test_umath_complex.TestCsqrt.test_special_values(<ufunc 'sqrt'>, -1, inf, inf, inf) FAIL: test_umath_complex.TestCsqrt.test_special_values(<ufunc 'sqrt'>, 0.0, inf, inf, inf) FAIL: test_umath_complex.TestCsqrt.test_special_values(<ufunc 'sqrt'>, -0.0, inf, inf, inf) FAIL: test_umath_complex.TestCsqrt.test_special_values(<ufunc 'sqrt'>, inf, inf, inf, inf) FAIL: test_umath_complex.TestCsqrt.test_special_values(<ufunc 'sqrt'>, -inf, inf, inf, inf) FAIL: test_umath_complex.TestCsqrt.test_special_values(<ufunc 'sqrt'>, nan, inf, inf, inf) FAIL: test_umath_complex.TestCsqrt.test_special_values(<ufunc 'sqrt'>, -inf, 1, 0.0, inf) scipy.test(): Crash (Abort trap) = Compilation from scratch (see above for recipe) = llvm-gcc-4.2 = (no compiler flags) numpy.test(): OK scipy.test(): Crash (Segmentation fault: 11) = gcc-4.2 = CC=gcc-4.2 CXX=g++-4.2 numpy.test(): OK scipy.test(): Hang (during ARPACK tests) = clang = CC=clang CXX=clang++ numpy.test(): 1 failure: FAIL: Test basic arithmetic function errors AssertionError: Type <type 'numpy.complex64'> did not raise fpe error ''. scipy.test(): Hang (during ARPACK tests) = llvm-gcc-4.2 and -ff2c = FFLAGS=-ff2c numpy.test(): OK scipy.test(): Crash (Segmentation fault: 11) = gcc-4.2 and -ff2c = CC=gcc-4.2 CXX=g++-4.2 FFLAGS=-ff2c numpy.test(): OK scipy.test(): 1 error, 2 failures: ERROR: test_arpack.test_hermitian_modes(True, <gen-hermitian>, 'F', 2, 'SA', None, None, <function aslinearoperator at 0x1098cd500>) FAIL: test_iterative.test_convergence(<function bicg at 0x1098cdd70>, <nonsymposdef>) FAIL: test_expon (test_morestats.TestAnderson) = clang and -ff2c = CC=clang CXX=clang++ FFLAGS=-ff2c numpy.test(): 1 failure: FAIL: Test basic arithmetic function errors AssertionError: Type <type 'numpy.complex64'> did not raise fpe error ''. scipy.test(): 1 error, 2 failures: ERROR: test_arpack.test_hermitian_modes(True, <gen-hermitian>, 'F', 2, 'SA', None, None, <function aslinearoperator at 0x1098cd500>) FAIL: test_iterative.test_convergence(<function bicg at 0x1098cdd70>, <nonsymposdef>) FAIL: test_expon (test_morestats.TestAnderson)

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adding chkfinite flags to linalg functions
by Ralf Gommers Sept. 19, 2011

Sept. 19, 2011

Hi, https://github.com/scipy/scipy/pull/48 adds a chkfinite flag to scipy.linalg functions that allows for disabling a check on whether there are infs/nans in the array, which can be a speed gain if you already know that there's no infs/nans in your input array. Is anyone opposed to merging this? Ralf

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scipy.spatial.distance.pdist - unnecessarily limited functionality and a suggestion of a possible solution
by Tomasz J. Kotarba Sept. 15, 2011

Sept. 15, 2011

Hello, I would like to suggest a small improvement to pdist(). I have just implemented some new similarity/distance metrics which work with heterogeneous arrays containing both numerical, categorical and set-valued data and tried to use pdist() for calculating the condensed distance matrix by passing the new metric functions as intended (i.e. using the 'metric' argument). Unfortunately, it turned out that I had to implement my own pdist function as the original pdist's functionality is unnecessarily limited to numerical data because of a conversion attempt in line 1089 (shown below): 1088 # The C code doesn't do striding. 1089 [X] = _copy_arrays_if_base_present([_convert_to_double(X)]) I understand the need for that conversion but propose that it either be moved down (to be applied only in case of distance measures which require it) or made possible to switch off (e.g. using a new function call parameter with a default value so that the change does not break the backward compatibility). The rationale behind this is that the intended purpose of pdist() does not require it to access individual values stored in the array - it is the job of a metric function - so decoupling pdist from the data would improve its design (and increase the number of its potential usage scenarios). Regards, T

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removing netcdf_variable from netcdf.__all__
by Ralf Gommers Sept. 13, 2011

Sept. 13, 2011

Hi, Question for users of scipy.io.netcdf: did you ever use netcdf_variable directly, or only via netcdf_file.createVariable? The documentation says that the latter is the only intended use, which means that this class shouldn't be in __all__. https://github.com/scipy/scipy/pull/67 removes it (and changes its __init__ in a non-backwards compatible way) - the patch looks correct but I want to double check that no one is using this class. Thanks, Ralf

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life expectancy of scipy.stats nan statistics
by josef.pktd＠gmail.com Sept. 11, 2011

Sept. 11, 2011

I'm just looking at http://projects.scipy.org/scipy/ticket/1200 I agree with Ralf that the bias keyword should be changed to ddof as in the numpy functions. For functions in scipy.stats, and statistics in general, I prefer the usual axis=0 default. However, I think these functions, like scipy.stats.nanstd, should be replaced by corresponding numpy functions, which might happen relatively soon. But how soon? Is it worth deprecating bias in scipy 0.10, and then deprecate again for removal in 0.11 or 0.12? Josef

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Target date for 0.10 release
by Warren Weckesser Sept. 10, 2011

Sept. 10, 2011

Just checking: the roadmap (http://projects.scipy.org/scipy/roadmap) says Sept. 30 for the release of 0.10. Is that still the plan? Warren

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import scipy.stats hangs in mod_python application
by fork Sept. 7, 2011

Sept. 7, 2011

Hi all, This may be really tricky to diagnose, given all the rather weird parameters, but here goes: I have a mod_python application that takes a bunch of mortality rates via a form, and processes them with scipy, numpy, and matplotlib. It is running on a Gentoo box. (Gentoo + mod_python seemed like a good idea at the time, but so did the Edsel... anyway, I still have to maintain this beast...) In the multitude of files that get loaded when a mod_python based page is called, there is a line "import scipy.stats as ST". That line works just great if I am running python at the command line, but it hangs forever when in the modpython application; I know this because I have run it both with an exception before it (barfs on the exception) and after it (just sits there, no errors to the apache log, etc). scipy.__version__ 0.9.0 python is 2.7.1 Anybody have any ideas how to fix this or even diagnose it?

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