cocopp.compall.ppperfprof : API documentation

For generating performance profiles. This module is not in use (anymore or not yet?).

**Example**

.. plot::
   :width: 75%

   import urllib
   import tarfile
   import glob
   from pylab import *
   import pickle
   import cocopp
   import cocopp.compall.ppperfprof
   import cocopp.bestalg

   # Collect and unarchive data
   dsets = {}
   for alg in cocopp.compall.ppperfprof.best:
       for date in ('2010', '2009'):
           try:
               dataurl = 'http://coco.lri.fr/BBOB'+date+'/pythondata/' + alg + '.tar.gz'
               filename, headers = urllib.urlretrieve(dataurl)
               archivefile = tarfile.open(filename)
               archivefile.extractall()  # write to disc
               dsets[alg] = cocopp.load(glob.glob('BBOB'+date+'pythondata/' + alg + '/ppdata_f0*_20.pickle'))
           except:
               pass

   # plot the profiles
   figure()
   # cocopp.compall.ppperfprof.plotmultiple(dsets, dsref=cocopp.bestalg.bestAlgorithmEntries)

def beautify():

Customize figure presentation.

def plotmultiple(dictAlg, dsref=None, order=None, targets=defaulttargets, istoolsstats=False, rhleg=True):

Generate performance profile figure.

:param dict dictAlg: dictionary of :py:class:`DataSetList` instances
                     one instance = one algorithm
:param DataSetList dsref: reference data set
:param seq targets: target function values
:param bool istoolsstats: if True, uses bootstrapped distribution
:param bool rhleg: if True, displays the right-hand legend

def plot(dsList, dsref, targets=defaulttargets, istoolsstats=False, **kwargs):

Generates a graph showing the performance profile of an algorithm.

We display the empirical cumulative distribution function ECDF of
the bootstrapped distribution of the average running time (aRT)
for an algorithm to reach the function value :py:data:`targets`
normalized by the aRT of the reference algorithm for these
targets.

:param DataSetList dsList: data set for one algorithm
:param DataSetList dsref: reference data set for normalization
:param seq targets: target function values
:param dict kwargs: additional parameters provided to plot function.

:returns: handles

def main(dictAlg, dsref=None, order=None, targets=defaulttargets, outputdir='', info='default', verbose=True):

Generates image files of the performance profiles of algorithms

From a dictionary of :py:class:`DataSetList` sorted by algorithms,
generates the performance profile (Moré:2008) on multiple functions
for multiple targets altogether.

:param dict dictAlg: dictionary of :py:class:`DataSetList` instances, one
                     dataSetList

:param list targets: target function values
:param list order: sorted list of keys to dictAlg for plotting order

Function	beautify	Customize figure presentation.
Function	plotmultiple	Generate performance profile figure.
Function	plot	Generates a graph showing the performance profile of an algorithm.
Function	main	Generates image files of the performance profiles of algorithms

cocopp.compall.ppperfprof module documentation

`cocopp.compall.ppperfprof` module documentation