cocopp.ppfigdim : API documentation

Generate performance scaling figures.

The figures show the scaling of the performance in terms of aRT w.r.t.
dimensionality on a log-log scale. On the y-axis, data is represented as
a number of function evaluations divided by dimension, this is in order
to compare at a glance with a linear scaling for which aRT is
proportional to the dimension and would therefore be represented by a
horizontal line in the figure.

Crosses (+) give the median number of function evaluations of successful
trials divided by dimension for the smallest *reached* target function
value.
Numbers indicate the number of successful runs for the smallest
*reached* target.
If the smallest target function value (1e-8) is not reached for a given
dimension, crosses (x) give the average number of overall conducted
function evaluations divided by the dimension.

Horizontal lines indicate linear scaling with the dimension, additional
grid lines show quadratic and cubic scaling.
The thick light line with diamond markers shows the results of the
specified reference algorithm for df = 1e-8 or a runlength-based
target (if in the expensive/runlength-based targets setting).

**Example**

.. plot::
    :width: 50%
    
    import urllib
    import tarfile
    import glob
    from pylab import *
    
    import cocopp
    
    # Collect and unarchive data (3.4MB)
    dataurl = 'http://coco.lri.fr/BBOB2009/pythondata/BIPOP-CMA-ES.tar.gz'
    filename, headers = urllib.urlretrieve(dataurl)
    archivefile = tarfile.open(filename)
    archivefile.extractall()
    
    # Scaling figure
    ds = cocopp.load(glob.glob('BBOB2009pythondata/BIPOP-CMA-ES/ppdata_f002_*.pickle'))
    figure()
    cocopp.ppfigdim.plot(ds)
    cocopp.ppfigdim.beautify()
    cocopp.ppfigdim.plot_previous_algorithms(2, False) # plot BBOB 2009 best algorithm on fun 2

Function	scaling_figure_caption	Provides a figure caption with the help of captions.py for replacing common texts, abbreviations, etc.
Function	beautify	Customize figure presentation.
Function	generateData	Computes an array of results to be plotted.
Function	plot_a_bar	plot/draw a notched error bar, x is the x-position, y[0,1,2] are lower, median and upper percentile respectively.
Function	plot	From a DataSetList, plot a figure of aRT/dim vs dim.
Function	plot_previous_algorithms	Add graph of the reference algorithm, specified in testbedsettings.current_testbed using the last, most difficult target in `target`.
Function	main	From a DataSetList, returns a convergence and aRT/dim figure vs dim.

def scaling_figure_caption():

Provides a figure caption with the help of captions.py for replacing common texts, abbreviations, etc.

def beautify(axesLabel=True):

Customize figure presentation.

Uses information from the appropriate benchmark short infos file for figure title.

def generateData(dataSet, targetFuncValue):

Computes an array of results to be plotted.

Returns (ert, success rate, number of success, total number of function evaluations, median of successful runs).

def plot_a_bar(x, y, plot_cmd=plt.loglog, rec_width=0.1, rec_taille_fac=0.3, styles={'color':'b'}, linewidth=1, fill_color=None, fill_transparency=0.7):

plot/draw a notched error bar, x is the x-position, y[0,1,2] are lower, median and upper percentile respectively.

hold(True) to see everything.

TODO: with linewidth=0, inf is not visible

def plot(dsList, valuesOfInterest=None, styles=styles):

From a DataSetList, plot a figure of aRT/dim vs dim.

There will be one set of graphs per function represented in the input data sets. Most usually the data sets of different functions will be represented separately.

Parameters	DataSetList dsList	data sets
	seq valuesOfInterest	target precisions via class TargetValues, there might be as many graphs as there are elements in this input. Can be different for each function (a dictionary indexed by ifun).
Returns	handles

def plot_previous_algorithms(func, target=None):

Add graph of the reference algorithm, specified in testbedsettings.current_testbed using the last, most difficult target in target.

def main(dsList, _valuesOfInterest, outputdir):

From a DataSetList, returns a convergence and aRT/dim figure vs dim.

If available, uses data of a reference algorithm as specified in :py:genericsettings.py.

Parameters	DataSetList dsList	data sets
	seq _valuesOfInterest	target precisions, either as list or as `pproc.TargetValues` class instance. There will be as many graphs as there are elements in this input.
	string outputdir	output directory

cocopp.ppfigdim module documentation

`cocopp.ppfigdim` module documentation