def main(argv=None): r"""Routine for post-processing COCO data from two algorithms. Provided with some data, this routine outputs figure and TeX files in a folder needed for the compilation of the provided LaTeX templates for comparing two algorithms (``*cmp.tex`` or ``*2*.tex``). The used template file needs to be edited so that the command ``\bbobdatapath`` points to the output folder created by this routine. The output files will contain performance tables, performance scaling figures and empirical cumulative distribution figures. On subsequent executions, new files will be added to the output folder, overwriting existing older files in the process. Keyword arguments: *argv* -- list of strings containing options and arguments. If not given, sys.argv is accessed. *argv* must list folders containing BBOB data files. Each of these folders should correspond to the data of ONE algorithm. Furthermore, argv can begin with, in any order, facultative option flags listed below. -h, --help displays this message. -v, --verbose verbose mode, prints out operations. -o OUTPUTDIR, --output-dir=OUTPUTDIR changes the default output directory (:file:`ppdata`) to :file:`OUTPUTDIR` --noise-free, --noisy processes only part of the data. --settings=SETTING changes the style of the output figures and tables. At the moment only the only differences are in the colors of the output figures. SETTING can be either "grayscale", "color" or "black-white". The default setting is "color". --fig-only, --rld-only, --tab-only, --sca-only these options can be used to output respectively the ERT graphs figures, run length distribution figures or the comparison tables scatter plot figures only. Any combination of these options results in no output. --conv if this option is chosen, additionally convergence plots for each function and algorithm are generated. --rld-single-fcts generate also runlength distribution figures for each single function. --expensive runlength-based f-target values and fixed display limits, useful with comparatively small budgets. By default the setting is based on the budget used in the data. --not-expensive expensive setting off. --svg generate also the svg figures which are used in html files Exceptions raised: *Usage* -- Gives back a usage message. Examples: * Calling the rungeneric2.py interface from the command line:: $ python bbob_pproc/rungeneric2.py -v Alg0-baseline Alg1-of-interest will post-process the data from folders :file:`Alg0-baseline` and :file:`Alg1-of-interest`, the former containing data for the reference algorithm (zero-th) and the latter data for the algorithm of concern (first). The results will be output in the default output folder. The ``-v`` option adds verbosity. * From the python interpreter (requires that the path to this package is in python search path):: >> import bbob_pproc as bb >> bb.rungeneric2.main('-o outputfolder PSO DEPSO'.split()) This will execute the post-processing on the data found in folder :file:`PSO` and :file:`DEPSO`. The ``-o`` option changes the output folder from the default to :file:`outputfolder`. """ if argv is None: argv = sys.argv[1:] # The zero-th input argument which is the name of the calling script is # disregarded. global ftarget try: try: opts, args = getopt.getopt(argv, genericsettings.shortoptlist, genericsettings.longoptlist) except getopt.error, msg: raise Usage(msg) if not (args): usage() sys.exit() #Process options outputdir = genericsettings.outputdir for o, a in opts: if o in ("-v", "--verbose"): genericsettings.verbose = True elif o in ("-h", "--help"): usage() sys.exit() elif o in ("-o", "--output-dir"): outputdir = a elif o == "--fig-only": genericsettings.isRLDistr = False genericsettings.isTab = False genericsettings.isScatter = False elif o == "--rld-only": genericsettings.isFig = False genericsettings.isTab = False genericsettings.isScatter = False elif o == "--tab-only": genericsettings.isFig = False genericsettings.isRLDistr = False genericsettings.isScatter = False elif o == "--sca-only": genericsettings.isFig = False genericsettings.isRLDistr = False genericsettings.isTab = False elif o == "--noisy": genericsettings.isNoisy = True elif o == "--noise-free": genericsettings.isNoiseFree = True elif o == "--settings": genericsettings.inputsettings = a elif o == "--conv": genericsettings.isConv = True elif o == "--rld-single-fcts": genericsettings.isRldOnSingleFcts = True elif o == "--runlength-based": genericsettings.runlength_based_targets = True elif o == "--expensive": genericsettings.isExpensive = True # comprises runlength-based elif o == "--not-expensive": genericsettings.isExpensive = False elif o == "--svg": genericsettings.generate_svg_files = True elif o == "--los-only": warnings.warn("option --los-only will have no effect with rungeneric2.py") elif o == "--crafting-effort=": warnings.warn("option --crafting-effort will have no effect with rungeneric2.py") elif o in ("-p", "--pickle"): warnings.warn("option --pickle will have no effect with rungeneric2.py") else: assert False, "unhandled option" # from bbob_pproc import bbob2010 as inset # input settings if genericsettings.inputsettings == "color": from bbob_pproc import genericsettings as inset # input settings config.config() elif genericsettings.inputsettings == "grayscale": # probably very much obsolete from bbob_pproc import grayscalesettings as inset # input settings elif genericsettings.inputsettings == "black-white": # probably very much obsolete from bbob_pproc import bwsettings as inset # input settings else: txt = ('Settings: %s is not an appropriate ' % genericsettings.inputsettings + 'argument for input flag "--settings".') raise Usage(txt) if (not genericsettings.verbose): warnings.simplefilter('module') warnings.simplefilter('ignore') print ("Post-processing will generate comparison " + "data in folder %s" % outputdir) print " this might take several minutes." dsList, sortedAlgs, dictAlg = processInputArgs(args, verbose=genericsettings.verbose) if 1 < 3 and len(sortedAlgs) != 2: raise ValueError('rungeneric2.py needs exactly two algorithms to compare, found: ' + str(sortedAlgs) + '\n use rungeneric.py (or rungenericmany.py) to compare more algorithms. ') if not dsList: sys.exit() for i in dictAlg: if genericsettings.isNoisy and not genericsettings.isNoiseFree: dictAlg[i] = dictAlg[i].dictByNoise().get('nzall', DataSetList()) if genericsettings.isNoiseFree and not genericsettings.isNoisy: dictAlg[i] = dictAlg[i].dictByNoise().get('noiselessall', DataSetList()) for i in dsList: if i.dim not in genericsettings.dimensions_to_display: continue if (dict((j, i.instancenumbers.count(j)) for j in set(i.instancenumbers)) < inset.instancesOfInterest): warnings.warn('The data of %s do not list ' %(i) + 'the correct instances ' + 'of function F%d.' %(i.funcId)) if len(sortedAlgs) < 2: raise Usage('Expect data from two different algorithms, could ' + 'only find one.') elif len(sortedAlgs) > 2: warnings.warn('Data from folders: %s ' % (sortedAlgs) + 'were found, the first two will be processed.') # Group by algorithm dsList0 = dictAlg[sortedAlgs[0]] if not dsList0: raise Usage('Could not find data for algorithm %s.' % (sortedAlgs[0])) dsList1 = dictAlg[sortedAlgs[1]] if not dsList1: raise Usage('Could not find data for algorithm %s.' % (sortedAlgs[0])) # get the name of each algorithm from the input arguments tmppath0, alg0name = os.path.split(sortedAlgs[0].rstrip(os.sep)) tmppath1, alg1name = os.path.split(sortedAlgs[1].rstrip(os.sep)) for i in dsList0: i.algId = alg0name for i in dsList1: i.algId = alg1name # compute maxfuneval values dict_max_fun_evals1 = {} dict_max_fun_evals2 = {} for ds in dsList0: dict_max_fun_evals1[ds.dim] = np.max((dict_max_fun_evals1.setdefault(ds.dim, 0), float(np.max(ds.maxevals)))) for ds in dsList1: dict_max_fun_evals2[ds.dim] = np.max((dict_max_fun_evals2.setdefault(ds.dim, 0), float(np.max(ds.maxevals)))) config.target_values(genericsettings.isExpensive, {1: min([max([val/dim for dim, val in dict_max_fun_evals1.iteritems()]), max([val/dim for dim, val in dict_max_fun_evals2.iteritems()])] )}) config.config() ######################### Post-processing ############################# if genericsettings.isFig or genericsettings.isRLDistr or genericsettings.isTab or genericsettings.isScatter: if not os.path.exists(outputdir): os.mkdir(outputdir) if genericsettings.verbose: print 'Folder %s was created.' % (outputdir) # prepend the algorithm name command to the tex-command file abc = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' lines = [] for i, alg in enumerate(args): lines.append('\\providecommand{\\algorithm' + abc[i] + '}{' + str_to_latex(strip_pathname1(alg)) + '}') prepend_to_file(os.path.join(outputdir, 'bbob_pproc_commands.tex'), lines, 1000, 'bbob_proc_commands.tex truncated, consider removing the file before the text run' ) # Check whether both input arguments list noisy and noise-free data dictFN0 = dsList0.dictByNoise() dictFN1 = dsList1.dictByNoise() k0 = set(dictFN0.keys()) k1 = set(dictFN1.keys()) symdiff = k1 ^ k0 # symmetric difference if symdiff: tmpdict = {} for i, noisegrp in enumerate(symdiff): if noisegrp == 'nzall': tmp = 'noisy' elif noisegrp == 'noiselessall': tmp = 'noiseless' if dictFN0.has_key(noisegrp): tmp2 = sortedAlgs[0] elif dictFN1.has_key(noisegrp): tmp2 = sortedAlgs[1] tmpdict.setdefault(tmp2, []).append(tmp) txt = [] for i, j in tmpdict.iteritems(): txt.append('Only input folder %s lists %s data.' % (i, ' and '.join(j))) raise Usage('Data Mismatch: \n ' + ' '.join(txt) + '\nTry using --noise-free or --noisy flags.') if genericsettings.isFig: plt.rc("axes", **inset.rcaxeslarger) plt.rc("xtick", **inset.rcticklarger) plt.rc("ytick", **inset.rcticklarger) plt.rc("font", **inset.rcfontlarger) plt.rc("legend", **inset.rclegendlarger) plt.rc('pdf', fonttype = 42) ppfig2.main(dsList0, dsList1, ppfig2_ftarget, outputdir, genericsettings.verbose) print "log ERT1/ERT0 vs target function values done." plt.rc("axes", **inset.rcaxes) plt.rc("xtick", **inset.rctick) plt.rc("ytick", **inset.rctick) plt.rc("font", **inset.rcfont) plt.rc("legend", **inset.rclegend) plt.rc('pdf', fonttype = 42) if genericsettings.isRLDistr: if len(dictFN0) > 1 or len(dictFN1) > 1: warnings.warn('Data for functions from both the noisy and ' + 'non-noisy testbeds have been found. Their ' + 'results will be mixed in the "all functions" ' + 'ECDF figures.') dictDim0 = dsList0.dictByDim() dictDim1 = dsList1.dictByDim() # ECDFs of ERT ratios for dim in set(dictDim0.keys()) & set(dictDim1.keys()): if dim in inset.rldDimsOfInterest: # ECDF for all functions altogether try: pprldistr2.main(dictDim0[dim], dictDim1[dim], dim, inset.rldValsOfInterest, outputdir, '%02dD_all' % dim, genericsettings.verbose) except KeyError: warnings.warn('Could not find some data in %d-D.' % (dim)) continue # ECDFs per function groups dictFG0 = dictDim0[dim].dictByFuncGroup() dictFG1 = dictDim1[dim].dictByFuncGroup() for fGroup in set(dictFG0.keys()) & set(dictFG1.keys()): pprldistr2.main(dictFG1[fGroup], dictFG0[fGroup], dim, inset.rldValsOfInterest, outputdir, '%02dD_%s' % (dim, fGroup), genericsettings.verbose) # ECDFs per noise groups dictFN0 = dictDim0[dim].dictByNoise() dictFN1 = dictDim1[dim].dictByNoise() for fGroup in set(dictFN0.keys()) & set(dictFN1.keys()): pprldistr2.main(dictFN1[fGroup], dictFN0[fGroup], dim, inset.rldValsOfInterest, outputdir, '%02dD_%s' % (dim, fGroup), genericsettings.verbose) prepend_to_file(os.path.join(outputdir, 'bbob_pproc_commands.tex'), ['\\providecommand{\\bbobpprldistrlegendtwo}[1]{', pprldistr.caption_two(), # depends on the config setting, should depend on maxfevals '}' ]) print "ECDF runlength ratio graphs done." for dim in set(dictDim0.keys()) & set(dictDim1.keys()): pprldistr.fmax = None #Resetting the max final value pprldistr.evalfmax = None #Resetting the max #fevalsfactor # ECDFs of all functions altogether if dim in inset.rldDimsOfInterest: try: pprldistr.comp(dictDim1[dim], dictDim0[dim], inset.rldValsOfInterest, # TODO: let rldVals... possibly be RL-based targets True, outputdir, 'all', genericsettings.verbose) except KeyError: warnings.warn('Could not find some data in %d-D.' % (dim)) continue # ECDFs per function groups dictFG0 = dictDim0[dim].dictByFuncGroup() dictFG1 = dictDim1[dim].dictByFuncGroup() for fGroup in set(dictFG0.keys()) & set(dictFG1.keys()): pprldistr.comp(dictFG1[fGroup], dictFG0[fGroup], inset.rldValsOfInterest, True, outputdir, '%s' % fGroup, genericsettings.verbose) # ECDFs per noise groups dictFN0 = dictDim0[dim].dictByNoise() dictFN1 = dictDim1[dim].dictByNoise() for fGroup in set(dictFN0.keys()) & set(dictFN1.keys()): pprldistr.comp(dictFN1[fGroup], dictFN0[fGroup], inset.rldValsOfInterest, True, outputdir, '%s' % fGroup, genericsettings.verbose) if genericsettings.isRldOnSingleFcts: # copy-paste from above, here for each function instead of function groups # ECDFs for each function pprldmany.all_single_functions(dictAlg, sortedAlgs, outputdir, genericsettings.verbose) print "ECDF runlength graphs done." if genericsettings.isConv: ppconverrorbars.main(dictAlg, outputdir, genericsettings.verbose) if genericsettings.isScatter: if genericsettings.runlength_based_targets: ppscatter.targets = ppscatter.runlength_based_targets ppscatter.main(dsList1, dsList0, outputdir, verbose=genericsettings.verbose) prepend_to_file(os.path.join(outputdir, 'bbob_pproc_commands.tex'), ['\\providecommand{\\bbobppscatterlegend}[1]{', ppscatter.figure_caption(), '}' ]) replace_in_file(os.path.join(outputdir, genericsettings.two_algorithm_file_name + '.html'), '##bbobppscatterlegend##', ppscatter.figure_caption_html()) print "Scatter plots done." if genericsettings.isTab: dictNG0 = dsList0.dictByNoise() dictNG1 = dsList1.dictByNoise() for nGroup in set(dictNG0.keys()) & set(dictNG1.keys()): # split table in as many as necessary dictFunc0 = dictNG0[nGroup].dictByFunc() dictFunc1 = dictNG1[nGroup].dictByFunc() funcs = list(set(dictFunc0.keys()) & set(dictFunc1.keys())) if len(funcs) > 24: funcs.sort() nbgroups = int(numpy.ceil(len(funcs)/24.)) def split_seq(seq, nbgroups): newseq = [] splitsize = 1.0/nbgroups*len(seq) for i in range(nbgroups): newseq.append(seq[int(round(i*splitsize)):int(round((i+1)*splitsize))]) return newseq groups = split_seq(funcs, nbgroups) # merge group0 = [] group1 = [] for i, g in enumerate(groups): tmp0 = DataSetList() tmp1 = DataSetList() for f in g: tmp0.extend(dictFunc0[f]) tmp1.extend(dictFunc1[f]) group0.append(tmp0) group1.append(tmp1) for i, g in enumerate(zip(group0, group1)): pptable2.main(g[0], g[1], inset.tabDimsOfInterest, outputdir, '%s%d' % (nGroup, i), genericsettings.verbose) else: if 11 < 3: # future handling: dictFunc0 = dsList0.dictByFunc() dictFunc1 = dsList1.dictByFunc() funcs = list(set(dictFunc0.keys()) & set(dictFunc1.keys())) funcs.sort() # nbgroups = int(numpy.ceil(len(funcs)/testbedsettings.numberOfFunctions)) # pptable2.main(dsList0, dsList1, # testbedsettings.tabDimsOfInterest, outputdir, # '%s' % (testbedsettings.testbedshortname), genericsettings.verbose) else: pptable2.main(dictNG0[nGroup], dictNG1[nGroup], inset.tabDimsOfInterest, outputdir, '%s' % (nGroup), genericsettings.verbose) if isinstance(pptable2.targetsOfInterest, pproc.RunlengthBasedTargetValues): prepend_to_file(os.path.join(outputdir, 'bbob_pproc_commands.tex'), ['\\providecommand{\\bbobpptablestwolegend}[1]{', pptable2.table_caption_expensive, '}' ]) else: prepend_to_file(os.path.join(outputdir, 'bbob_pproc_commands.tex'), ['\\providecommand{\\bbobpptablestwolegend}[1]{', pptable2.table_caption, '}' ]) htmlFileName = os.path.join(outputdir, genericsettings.two_algorithm_file_name + '.html') key = '##bbobpptablestwolegendexpensive##' if isinstance(pptable2.targetsOfInterest, pproc.RunlengthBasedTargetValues) else '##bbobpptablestwolegend##' replace_in_file(htmlFileName, '##bbobpptablestwolegend##', htmldesc.getValue(key)) alg0 = set(i[0] for i in dsList0.dictByAlg().keys()).pop().replace(genericsettings.extraction_folder_prefix, '')[0:3] alg1 = set(i[0] for i in dsList1.dictByAlg().keys()).pop().replace(genericsettings.extraction_folder_prefix, '')[0:3] replace_in_file(htmlFileName, 'algorithmAshort', alg0) replace_in_file(htmlFileName, 'algorithmBshort', alg1) for i, alg in enumerate(args): replace_in_file(htmlFileName, 'algorithm' + abc[i], str_to_latex(strip_pathname1(alg))) print "Tables done." if genericsettings.isScaleUp: plt.rc("axes", labelsize=20, titlesize=24) plt.rc("xtick", labelsize=20) plt.rc("ytick", labelsize=20) plt.rc("font", size=20) plt.rc("legend", fontsize=20) plt.rc('pdf', fonttype = 42) if genericsettings.runlength_based_targets: ftarget = RunlengthBasedTargetValues([target_runlength]) # TODO: make this more variable but also consistent ppfigs.main(dictAlg, genericsettings.two_algorithm_file_name, sortedAlgs, ftarget, outputdir, genericsettings.verbose) plt.rcdefaults() print "Scaling figures done." if genericsettings.isFig or genericsettings.isRLDistr or genericsettings.isTab or genericsettings.isScatter or genericsettings.isScaleUp: print "Output data written to folder %s" % outputdir plt.rcdefaults()
def plotLegend(handles, maxval): """Display right-side legend. :param float maxval: rightmost x boundary :returns: list of (ordered) labels and handles. The figure is stopped at maxval (upper x-bound), and the graphs in the figure are prolonged with straight lines to the right to connect with labels of the graphs (uniformly spread out vertically). The order of the graphs at the upper x-bound line give the order of the labels, in case of ties, the best is the graph for which the x-value of the first step (from the right) is smallest. The annotation string is stripped from preceeding pathnames. """ reslabels = [] reshandles = [] ys = {} lh = 0 for h in handles: x2 = [] y2 = [] for i in h: x2.append(plt.getp(i, "xdata")) y2.append(plt.getp(i, "ydata")) x2 = np.array(np.hstack(x2)) y2 = np.array(np.hstack(y2)) tmp = np.argsort(x2) x2 = x2[tmp] y2 = y2[tmp] h = h[-1] # we expect the label to be in the last element of h tmp = (x2 <= maxval) try: x2bis = x2[y2 < y2[tmp][-1]][-1] except IndexError: # there is no data with a y smaller than max(y) x2bis = 0. ys.setdefault(y2[tmp][-1], {}).setdefault(x2bis, []).append(h) lh += 1 if len(show_algorithms) > 0: lh = min(lh, len(show_algorithms)) if lh <= 1: lh = 2 fontsize = genericsettings.minmax_algorithm_fontsize[0] + np.min( (1, np.exp(9 - lh))) * (genericsettings.minmax_algorithm_fontsize[-1] - genericsettings.minmax_algorithm_fontsize[0]) i = 0 # loop over the elements of ys for j in sorted(ys.keys()): for k in reversed(sorted(ys[j].keys())): #enforce best ever comes last in case of equality tmp = [] for h in ys[j][k]: if plt.getp(h, 'label') == 'best 2009': tmp.insert(0, h) else: tmp.append(h) tmp.reverse() ys[j][k] = tmp for h in ys[j][k]: if (not plt.getp(h, 'label').startswith('_line') and (len(show_algorithms) == 0 or plt.getp(h, 'label') in show_algorithms)): y = 0.02 + i * 0.96 / (lh - 1) tmp = {} for attr in ('lw', 'ls', 'marker', 'markeredgewidth', 'markerfacecolor', 'markeredgecolor', 'markersize', 'zorder'): tmp[attr] = plt.getp(h, attr) legx = maxval**annotation_line_end_relative if 'marker' in attr: legx = maxval**annotation_line_end_relative # reshandles.extend(plt_plot((maxval, legx), (j, y), reshandles.extend( plt_plot((maxval, legx), (j, y), color=plt.getp(h, 'markeredgecolor'), **tmp)) reshandles.append( plt.text(maxval**(0.02 + annotation_line_end_relative), y, toolsdivers.str_to_latex( toolsdivers.strip_pathname1( plt.getp(h, 'label'))), horizontalalignment="left", verticalalignment="center", size=fontsize)) reslabels.append(plt.getp(h, 'label')) #set_trace() i += 1 #plt.axvline(x=maxval, color='k') # Not as efficient? reshandles.append(plt_plot((maxval, maxval), (0., 1.), color='k')) reslabels.reverse() plt.xlim(xmax=maxval**annotation_space_end_relative) return reslabels, reshandles
if 1 < 3: print("Post-processing: will generate output " + "data in folder %s" % outputdir) print " this might take several minutes." if not os.path.exists(outputdir): os.makedirs(outputdir) if genericsettings.verbose: print 'Folder %s was created.' % (outputdir) # prepend the algorithm name command to the tex-command file lines = [] for i, alg in enumerate(args): lines.append('\\providecommand{\\algorithm' + pptex.numtotext(i) + '}{' + str_to_latex(strip_pathname1(alg)) + '}') prepend_to_file( os.path.join(outputdir, 'bbob_pproc_commands.tex'), lines, 5000, 'bbob_proc_commands.tex truncated, consider removing the file before the text run' ) dsList, sortedAlgs, dictAlg = processInputArgs( args, verbose=genericsettings.verbose) if not dsList: sys.exit() for i in dictAlg: if genericsettings.isNoisy and not genericsettings.isNoiseFree: dictAlg[i] = dictAlg[i].dictByNoise().get( 'nzall', DataSetList())
def plotLegend(handles, maxval): """Display right-side legend. :param float maxval: rightmost x boundary :returns: list of (ordered) labels and handles. The figure is stopped at maxval (upper x-bound), and the graphs in the figure are prolonged with straight lines to the right to connect with labels of the graphs (uniformly spread out vertically). The order of the graphs at the upper x-bound line give the order of the labels, in case of ties, the best is the graph for which the x-value of the first step (from the right) is smallest. The annotation string is stripped from preceeding pathnames. """ reslabels = [] reshandles = [] ys = {} lh = 0 for h in handles: x2 = [] y2 = [] for i in h: x2.append(plt.getp(i, "xdata")) y2.append(plt.getp(i, "ydata")) x2 = np.array(np.hstack(x2)) y2 = np.array(np.hstack(y2)) tmp = np.argsort(x2) x2 = x2[tmp] y2 = y2[tmp] h = h[-1] # we expect the label to be in the last element of h tmp = (x2 <= maxval) try: x2bis = x2[y2 < y2[tmp][-1]][-1] except IndexError: # there is no data with a y smaller than max(y) x2bis = 0. ys.setdefault(y2[tmp][-1], {}).setdefault(x2bis, []).append(h) lh += 1 if len(show_algorithms) > 0: lh = min(lh, len(show_algorithms)) if lh <= 1: lh = 2 fontsize = genericsettings.minmax_algorithm_fontsize[0] + np.min((1, np.exp(9-lh))) * ( genericsettings.minmax_algorithm_fontsize[-1] - genericsettings.minmax_algorithm_fontsize[0]) i = 0 # loop over the elements of ys for j in sorted(ys.keys()): for k in reversed(sorted(ys[j].keys())): #enforce best ever comes last in case of equality tmp = [] for h in ys[j][k]: if plt.getp(h, 'label') == 'best 2009': tmp.insert(0, h) else: tmp.append(h) tmp.reverse() ys[j][k] = tmp for h in ys[j][k]: if (not plt.getp(h, 'label').startswith('_line') and (len(show_algorithms) == 0 or plt.getp(h, 'label') in show_algorithms)): y = 0.02 + i * 0.96/(lh-1) tmp = {} for attr in ('lw', 'ls', 'marker', 'markeredgewidth', 'markerfacecolor', 'markeredgecolor', 'markersize', 'zorder'): tmp[attr] = plt.getp(h, attr) legx = maxval**annotation_line_end_relative if 'marker' in attr: legx = maxval**annotation_line_end_relative # reshandles.extend(plt_plot((maxval, legx), (j, y), reshandles.extend(plt_plot((maxval, legx), (j, y), color=plt.getp(h, 'markeredgecolor'), **tmp)) reshandles.append( plt.text(maxval**(0.02 + annotation_line_end_relative), y, toolsdivers.str_to_latex(toolsdivers.strip_pathname1(plt.getp(h, 'label'))), horizontalalignment="left", verticalalignment="center", size=fontsize)) reslabels.append(plt.getp(h, 'label')) #set_trace() i += 1 #plt.axvline(x=maxval, color='k') # Not as efficient? reshandles.append(plt_plot((maxval, maxval), (0., 1.), color='k')) reslabels.reverse() plt.xlim(xmax=maxval**annotation_space_end_relative) return reslabels, reshandles
def main(dictAlg, sortedAlgs, outputdir='.', verbose=True, function_targets_line=True): # [1, 13, 101] """Generate one table per func with results of multiple algorithms.""" """Difference with the first version: * numbers aligned using the decimal separator * premices for dispersion measure * significance test against best algorithm * table width... Takes ``targetsOfInterest`` from this file as "input argument" to compute the desired target values. ``targetsOfInterest`` might be configured via config. """ # TODO: method is long, terrible to read, split if possible if not bestalg.bestalgentries2009: bestalg.loadBBOB2009() # Sort data per dimension and function dictData = {} dsListperAlg = list(dictAlg[i] for i in sortedAlgs) for n, entries in enumerate(dsListperAlg): tmpdictdim = entries.dictByDim() for d in tmpdictdim: tmpdictfun = tmpdictdim[d].dictByFunc() for f in tmpdictfun: dictData.setdefault((d, f), {})[n] = tmpdictfun[f] nbtests = len(dictData) for df in dictData: # Generate one table per df # first update targets for each dimension-function pair if needed: targets = targetsOfInterest((df[1], df[0])) targetf = targets[-1] # best 2009 refalgentry = bestalg.bestalgentries2009[df] refalgert = refalgentry.detERT(targets) refalgevals = (refalgentry.detEvals((targetf, ))[0][0]) refalgnbruns = len(refalgevals) refalgnbsucc = numpy.sum(numpy.isnan(refalgevals) == False) # Process the data # The following variables will be lists of elements each corresponding # to an algorithm algnames = [] #algdata = [] algerts = [] algevals = [] algdisp = [] algnbsucc = [] algnbruns = [] algmedmaxevals = [] algmedfinalfunvals = [] algtestres = [] algentries = [] for n in sorted(dictData[df].keys()): entries = dictData[df][n] # the number of datasets for a given dimension and function (df) # should be strictly 1. TODO: find a way to warn # TODO: do this checking before... why wasn't it triggered by ppperprof? if len(entries) > 1: print entries txt = ("There is more than a single entry associated with " "folder %s on %d-D f%d." % (sortedAlgs[n], df[0], df[1])) raise Exception(txt) entry = entries[0] algentries.append(entry) algnames.append(sortedAlgs[n]) evals = entry.detEvals(targets) #tmpdata = [] tmpdisp = [] tmpert = [] for i, e in enumerate(evals): succ = (numpy.isnan(e) == False) ec = e.copy() # note: here was the previous bug (changes made in e also appeared in evals !) ec[succ == False] = entry.maxevals[succ == False] ert = toolsstats.sp(ec, issuccessful=succ)[0] #tmpdata.append(ert/refalgert[i]) if succ.any(): tmp = toolsstats.drawSP(ec[succ], entry.maxevals[succ == False], [10, 50, 90], samplesize=samplesize)[0] tmpdisp.append((tmp[-1] - tmp[0])/2.) else: tmpdisp.append(numpy.nan) tmpert.append(ert) algerts.append(tmpert) algevals.append(evals) #algdata.append(tmpdata) algdisp.append(tmpdisp) algmedmaxevals.append(numpy.median(entry.maxevals)) algmedfinalfunvals.append(numpy.median(entry.finalfunvals)) #algmedmaxevals.append(numpy.median(entry.maxevals)/df[0]) #algmedfinalfunvals.append(numpy.median(entry.finalfunvals)) algtestres.append(significancetest(refalgentry, entry, targets)) # determine success probability for Df = 1e-8 e = entry.detEvals((targetf ,))[0] algnbsucc.append(numpy.sum(numpy.isnan(e) == False)) algnbruns.append(len(e)) # Process over all data # find best values... nalgs = len(dictData[df]) maxRank = 1 + numpy.floor(0.14 * nalgs) # number of algs to be displayed in bold isBoldArray = [] # Point out the best values algfinaldata = [] # Store median function values/median number of function evaluations tmptop = getTopIndicesOfColumns(algerts, maxRank=maxRank) for i, erts in enumerate(algerts): tmp = [] for j, ert in enumerate(erts): # algi targetj tmp.append(i in tmptop[j] or (nalgs > 7 and algerts[i][j] <= 3. * refalgert[j])) isBoldArray.append(tmp) algfinaldata.append((algmedfinalfunvals[i], algmedmaxevals[i])) # significance test of best given algorithm against all others best_alg_idx = numpy.array(algerts).argsort(0)[0, :] # indexed by target index significance_versus_others = significance_all_best_vs_other(algentries, targets, best_alg_idx)[0] # Create the table table = [] tableHtml = [] spec = r'@{}c@{}|*{%d}{@{\,}r@{}X@{\,}}|@{}r@{}@{}l@{}' % (len(targets)) # in case StrLeft not working: replaced c@{} with l@{ } spec = r'@{}c@{}|*{%d}{@{}r@{}X@{}}|@{}r@{}@{}l@{}' % (len(targets)) # in case StrLeft not working: replaced c@{} with l@{ } extraeol = [] # Generate header lines if with_table_heading: header = funInfos[df[1]] if funInfos else 'f%d' % df[1] table.append([r'\multicolumn{%d}{@{\,}c@{\,}}{{\textbf{%s}}}' % (2 * len(targets) + 2, header)]) extraeol.append('') if function_targets_line is True or (function_targets_line and df[1] in function_targets_line): if isinstance(targetsOfInterest, pproc.RunlengthBasedTargetValues): curline = [r'\#FEs/D'] curlineHtml = ['<thead>\n<tr>\n<th>#FEs/D<br>REPLACEH</th>\n'] counter = 1 for i in targetsOfInterest.labels(): curline.append(r'\multicolumn{2}{@{}c@{}}{%s}' % i) curlineHtml.append('<td>%s<br>REPLACE%d</td>\n' % (i, counter)) counter += 1 else: curline = [r'$\Delta f_\mathrm{opt}$'] curlineHtml = ['<thead>\n<tr>\n<th>Δ f<sub>opt</sub><br>REPLACEH</th>\n'] counter = 1 for t in targets: curline.append(r'\multicolumn{2}{@{\,}X@{\,}}{%s}' % writeFEvals2(t, precision=1, isscientific=True)) curlineHtml.append('<td>%s<br>REPLACE%d</td>\n' % (writeFEvals2(t, precision=1, isscientific=True), counter)) counter += 1 # curline.append(r'\multicolumn{2}{@{\,}X@{}|}{%s}' # % writeFEvals2(targets[-1], precision=1, isscientific=True)) curline.append(r'\multicolumn{2}{@{}l@{}}{\#succ}') curlineHtml.append('<td>#succ<br>REPLACEF</td>\n</tr>\n</thead>\n') table.append(curline) extraeol.append(r'\hline') # extraeol.append(r'\hline\arrayrulecolor{tableShade}') curline = [r'ERT$_{\text{best}}$'] if with_table_heading else [r'\textbf{f%d}' % df[1]] replaceValue = 'ERT<sub>best</sub>' if with_table_heading else ('<b>f%d</b>' % df[1]) curlineHtml = [item.replace('REPLACEH', replaceValue) for item in curlineHtml] if isinstance(targetsOfInterest, pproc.RunlengthBasedTargetValues): # write ftarget:fevals counter = 1 for i in xrange(len(refalgert[:-1])): temp="%.1e" %targetsOfInterest((df[1], df[0]))[i] if temp[-2]=="0": temp=temp[:-2]+temp[-1] curline.append(r'\multicolumn{2}{@{}c@{}}{\textit{%s}:%s \quad}' % (temp, writeFEvalsMaxPrec(refalgert[i], 2))) replaceValue = '<i>%s</i>:%s' % (temp, writeFEvalsMaxPrec(refalgert[i], 2)) curlineHtml = [item.replace('REPLACE%d' % counter, replaceValue) for item in curlineHtml] counter += 1 temp="%.1e" %targetsOfInterest((df[1], df[0]))[-1] if temp[-2]=="0": temp=temp[:-2]+temp[-1] curline.append(r'\multicolumn{2}{@{}c@{}|}{\textit{%s}:%s }' % (temp ,writeFEvalsMaxPrec(refalgert[-1], 2))) replaceValue = '<i>%s</i>:%s' % (temp, writeFEvalsMaxPrec(refalgert[-1], 2)) curlineHtml = [item.replace('REPLACE%d' % counter, replaceValue) for item in curlineHtml] else: # write #fevals of the reference alg counter = 1 for i in refalgert[:-1]: curline.append(r'\multicolumn{2}{@{}c@{}}{%s \quad}' % writeFEvalsMaxPrec(i, 2)) curlineHtml = [item.replace('REPLACE%d' % counter, writeFEvalsMaxPrec(i, 2)) for item in curlineHtml] counter += 1 curline.append(r'\multicolumn{2}{@{}c@{}|}{%s}' % writeFEvalsMaxPrec(refalgert[-1], 2)) curlineHtml = [item.replace('REPLACE%d' % counter, writeFEvalsMaxPrec(refalgert[-1], 2)) for item in curlineHtml] # write the success ratio for the reference alg tmp2 = numpy.sum(numpy.isnan(refalgevals) == False) # count the nb of success curline.append('%d' % (tmp2)) if tmp2 > 0: curline.append('/%d' % len(refalgevals)) replaceValue = '%d/%d' % (tmp2, len(refalgevals)) else: replaceValue = '%d' % tmp2 curlineHtml = [item.replace('REPLACEF', replaceValue) for item in curlineHtml] table.append(curline[:]) tableHtml.extend(curlineHtml[:]) tableHtml.append('<tbody>\n') extraeol.append('') #for i, gna in enumerate(zip((1, 2, 3), ('bla', 'blo', 'bli'))): #print i, gna, gno #set_trace() # Format data #if df == (5, 17): #set_trace() header = r'\providecommand{\ntables}{7}' for i, alg in enumerate(algnames): tableHtml.append('<tr>\n') #algname, entries, irs, line, line2, succ, runs, testres1alg in zip(algnames, #data, dispersion, isBoldArray, isItalArray, nbsucc, nbruns, testres): commandname = r'\alg%stables' % numtotext(i) # header += r'\providecommand{%s}{{%s}{}}' % (commandname, str_to_latex(strip_pathname(alg))) header += r'\providecommand{%s}{\StrLeft{%s}{\ntables}}' % (commandname, str_to_latex(strip_pathname1(alg))) curline = [commandname + r'\hspace*{\fill}'] # each list element becomes a &-separated table entry? curlineHtml = ['<th>%s</th>\n' % str_to_latex(strip_pathname1(alg))] for j, tmp in enumerate(zip(algerts[i], algdisp[i], # j is target index isBoldArray[i], algtestres[i])): ert, dispersion, isBold, testres = tmp alignment = '@{\,}X@{\,}' if j == len(algerts[i]) - 1: alignment = '@{\,}X@{\,}|' data = ert/refalgert[j] # write star for significance against all other algorithms str_significance_subsup = '' str_significance_subsup_html = '' if (len(best_alg_idx) > 0 and len(significance_versus_others) > 0 and i == best_alg_idx[j] and nbtests * significance_versus_others[j][1] < 0.05): logp = -numpy.ceil(numpy.log10(nbtests * significance_versus_others[j][1])) logp = numpy.min((9, logp)) # not messing up the format and handling inf str_significance_subsup = r"^{%s%s}" % (significance_vs_others_symbol, str(int(logp)) if logp > 1 else '') str_significance_subsup_html = '<sup>%s%s</sup>' % (significance_vs_others_symbol_html, str(int(logp)) if logp > 1 else '') # moved out of the above else: this was a bug!? z, p = testres if (nbtests * p) < 0.05 and data < 1. and z < 0.: if not numpy.isinf(refalgert[j]): tmpevals = algevals[i][j].copy() tmpevals[numpy.isnan(tmpevals)] = algentries[i].maxevals[numpy.isnan(tmpevals)] bestevals = refalgentry.detEvals(targets) bestevals, bestalgalg = (bestevals[0][0], bestevals[1][0]) bestevals[numpy.isnan(bestevals)] = refalgentry.maxevals[bestalgalg][numpy.isnan(bestevals)] tmpevals = numpy.array(sorted(tmpevals))[0:min(len(tmpevals), len(bestevals))] bestevals = numpy.array(sorted(bestevals))[0:min(len(tmpevals), len(bestevals))] #The conditions are now that ERT < ERT_best and # all(sorted(FEvals_best) > sorted(FEvals_current)). if numpy.isinf(refalgert[j]) or all(tmpevals < bestevals): nbstars = -numpy.ceil(numpy.log10(nbtests * p)) # tmp2[-1] += r'$^{%s}$' % superscript str_significance_subsup += r'_{%s%s}' % (significance_vs_ref_symbol, str(int(nbstars)) if nbstars > 1 else '') str_significance_subsup_html = '<sub>%s%s</sub>' % (significance_vs_ref_symbol_html, str(int(nbstars)) if nbstars > 1 else '') if str_significance_subsup: str_significance_subsup = '$%s$' % str_significance_subsup # format number in variable data if numpy.isnan(data): curline.append(r'\multicolumn{2}{%s}{.}' % alignment) else: if numpy.isinf(refalgert[j]): curline.append(r'\multicolumn{2}{%s}{\textbf{%s}\mbox{\tiny (%s)}%s}' % (alignment, writeFEvalsMaxPrec(algerts[i][j], 2), writeFEvalsMaxPrec(dispersion, precdispersion), str_significance_subsup)) curlineHtml.append('<td sorttable_customkey=\"%f\"><b>%s</b> (%s)%s</td>\n' % (algerts[i][j], writeFEvalsMaxPrec(algerts[i][j], 2), writeFEvalsMaxPrec(dispersion, precdispersion), str_significance_subsup_html)) continue tmp = writeFEvalsMaxPrec(data, precfloat, maxfloatrepr=maxfloatrepr) tmpHtml = writeFEvalsMaxPrec(data, precfloat, maxfloatrepr=maxfloatrepr) sortKey = data if data >= maxfloatrepr or data < 0.01: # either inf or scientific notation if numpy.isinf(data) and j == len(algerts[i]) - 1: tmp += r'\,\textit{%s}' % writeFEvalsMaxPrec(algfinaldata[i][1], 0, maxfloatrepr=maxfloatrepr) tmpHtml += '<i>%s</i>' % writeFEvalsMaxPrec(algfinaldata[i][1], 0, maxfloatrepr=maxfloatrepr) sortKey = algfinaldata[i][1] else: tmp = writeFEvalsMaxPrec(data, precscien, maxfloatrepr=data) if isBold: tmpHtml = '<b>%s</b>' % tmp tmp = r'\textbf{%s}' % tmp if not numpy.isnan(dispersion): tmpdisp = dispersion/refalgert[j] if tmpdisp >= maxfloatrepr or tmpdisp < 0.005: # TODO: hack tmpdisp = writeFEvalsMaxPrec(tmpdisp, precdispersion, maxfloatrepr=tmpdisp) else: tmpdisp = writeFEvalsMaxPrec(tmpdisp, precdispersion, maxfloatrepr=maxfloatrepr) tmp += r'\mbox{\tiny (%s)}' % tmpdisp tmpHtml += ' (%s)' % tmpdisp curline.append(r'\multicolumn{2}{%s}{%s%s}' % (alignment, tmp, str_significance_subsup)) tmpHtml = tmpHtml.replace('$\infty$', '∞') if (numpy.isinf(sortKey)): sortKey = sys.maxint curlineHtml.append('<td sorttable_customkey=\"%f\">%s%s</td>' % (sortKey, tmpHtml, str_significance_subsup_html)) else: tmp2 = tmp.split('.', 1) if len(tmp2) < 2: tmp2.append('') else: tmp2[-1] = '.' + tmp2[-1] if isBold: tmp3 = [] tmp3html = [] for k in tmp2: tmp3.append(r'\textbf{%s}' % k) tmp3html.append('<b>%s</b>' % k) tmp2 = tmp3 tmp2html = tmp3html else: tmp2html = [] tmp2html.extend(tmp2) if not numpy.isnan(dispersion): tmpdisp = dispersion/refalgert[j] if tmpdisp >= maxfloatrepr or tmpdisp < 0.01: tmpdisp = writeFEvalsMaxPrec(tmpdisp, precdispersion, maxfloatrepr=tmpdisp) else: tmpdisp = writeFEvalsMaxPrec(tmpdisp, precdispersion, maxfloatrepr=maxfloatrepr) tmp2[-1] += (r'\mbox{\tiny (%s)}' % (tmpdisp)) tmp2html[-1] += ' (%s)' % tmpdisp tmp2[-1] += str_significance_subsup tmp2html[-1] += str_significance_subsup_html curline.extend(tmp2) tmp2html = ("").join(str(item) for item in tmp2html) tmp2html = tmp2html.replace('$\infty$', '∞') curlineHtml.append('<td sorttable_customkey=\"%f\">%s</td>' % (data, tmp2html)) curline.append('%d' % algnbsucc[i]) curline.append('/%d' % algnbruns[i]) table.append(curline) curlineHtml.append('<td sorttable_customkey=\"%d\">%d/%d</td>\n' % (algnbsucc[i], algnbsucc[i], algnbruns[i])) tableHtml.extend(curlineHtml[:]) extraeol.append('') # Write table res = tableXLaTeX(table, spec=spec, extraeol=extraeol) try: filename = os.path.join(outputdir, 'pptables_f%03d_%02dD.tex' % (df[1], df[0])) f = open(filename, 'w') f.write(header + '\n') f.write(res) res = ("").join(str(item) for item in tableHtml) res = '\n<table class=\"sortable\" style=\"width:800px \">\n%s</table>\n<p/>\n' % res if df[0] in (5, 20): filename = os.path.join(outputdir, genericsettings.many_algorithm_file_name + '.html') lines = [] with open(filename) as infile: for line in infile: if '<!--' + 'pptablesf%03d%02dDHtml' % (df[1], df[0]) + '-->' in line: lines.append(res) lines.append(line) with open(filename, 'w') as outfile: for line in lines: outfile.write(line) if verbose: print 'Wrote table in %s' % filename except: raise else: f.close()
return 2 if 1 < 3: print ("Post-processing: will generate output " + "data in folder %s" % outputdir) print " this might take several minutes." if not os.path.exists(outputdir): os.makedirs(outputdir) if genericsettings.verbose: print "Folder %s was created." % (outputdir) # prepend the algorithm name command to the tex-command file lines = [] for i, alg in enumerate(args): lines.append( "\\providecommand{\\algorithm" + pptex.numtotext(i) + "}{" + str_to_latex(strip_pathname1(alg)) + "}" ) prepend_to_file( os.path.join(outputdir, "bbob_pproc_commands.tex"), lines, 5000, "bbob_proc_commands.tex truncated, consider removing the file before the text run", ) dsList, sortedAlgs, dictAlg = processInputArgs(args, verbose=genericsettings.verbose) if not dsList: sys.exit() for i in dictAlg: if genericsettings.isNoisy and not genericsettings.isNoiseFree:
def main(dictAlg, sortedAlgs, outputdir='.', verbose=True, function_targets_line=True): # [1, 13, 101] """Generate one table per func with results of multiple algorithms.""" """Difference with the first version: * numbers aligned using the decimal separator * premices for dispersion measure * significance test against best algorithm * table width... Takes ``targetsOfInterest`` from this file as "input argument" to compute the desired target values. ``targetsOfInterest`` might be configured via config. """ # TODO: method is long, terrible to read, split if possible if not bestalg.bestalgentries2009: bestalg.loadBBOB2009() # Sort data per dimension and function dictData = {} dsListperAlg = list(dictAlg[i] for i in sortedAlgs) for n, entries in enumerate(dsListperAlg): tmpdictdim = entries.dictByDim() for d in tmpdictdim: tmpdictfun = tmpdictdim[d].dictByFunc() for f in tmpdictfun: dictData.setdefault((d, f), {})[n] = tmpdictfun[f] nbtests = len(dictData) for df in dictData: # Generate one table per df # first update targets for each dimension-function pair if needed: targets = targetsOfInterest((df[1], df[0])) targetf = targets[-1] # best 2009 refalgentry = bestalg.bestalgentries2009[df] refalgert = refalgentry.detERT(targets) refalgevals = (refalgentry.detEvals((targetf, ))[0][0]) refalgnbruns = len(refalgevals) refalgnbsucc = numpy.sum(numpy.isnan(refalgevals) == False) # Process the data # The following variables will be lists of elements each corresponding # to an algorithm algnames = [] #algdata = [] algerts = [] algevals = [] algdisp = [] algnbsucc = [] algnbruns = [] algmedmaxevals = [] algmedfinalfunvals = [] algtestres = [] algentries = [] for n in sorted(dictData[df].keys()): entries = dictData[df][n] # the number of datasets for a given dimension and function (df) # should be strictly 1. TODO: find a way to warn # TODO: do this checking before... why wasn't it triggered by ppperprof? if len(entries) > 1: print entries txt = ("There is more than a single entry associated with " "folder %s on %d-D f%d." % (sortedAlgs[n], df[0], df[1])) raise Exception(txt) entry = entries[0] algentries.append(entry) algnames.append(sortedAlgs[n]) evals = entry.detEvals(targets) #tmpdata = [] tmpdisp = [] tmpert = [] for i, e in enumerate(evals): succ = (numpy.isnan(e) == False) ec = e.copy( ) # note: here was the previous bug (changes made in e also appeared in evals !) ec[succ == False] = entry.maxevals[succ == False] ert = toolsstats.sp(ec, issuccessful=succ)[0] #tmpdata.append(ert/refalgert[i]) if succ.any(): tmp = toolsstats.drawSP(ec[succ], entry.maxevals[succ == False], [10, 50, 90], samplesize=samplesize)[0] tmpdisp.append((tmp[-1] - tmp[0]) / 2.) else: tmpdisp.append(numpy.nan) tmpert.append(ert) algerts.append(tmpert) algevals.append(evals) #algdata.append(tmpdata) algdisp.append(tmpdisp) algmedmaxevals.append(numpy.median(entry.maxevals)) algmedfinalfunvals.append(numpy.median(entry.finalfunvals)) #algmedmaxevals.append(numpy.median(entry.maxevals)/df[0]) #algmedfinalfunvals.append(numpy.median(entry.finalfunvals)) algtestres.append(significancetest(refalgentry, entry, targets)) # determine success probability for Df = 1e-8 e = entry.detEvals((targetf, ))[0] algnbsucc.append(numpy.sum(numpy.isnan(e) == False)) algnbruns.append(len(e)) # Process over all data # find best values... nalgs = len(dictData[df]) maxRank = 1 + numpy.floor( 0.14 * nalgs) # number of algs to be displayed in bold isBoldArray = [] # Point out the best values algfinaldata = [ ] # Store median function values/median number of function evaluations tmptop = getTopIndicesOfColumns(algerts, maxRank=maxRank) for i, erts in enumerate(algerts): tmp = [] for j, ert in enumerate(erts): # algi targetj tmp.append(i in tmptop[j] or (nalgs > 7 and algerts[i][j] <= 3. * refalgert[j])) isBoldArray.append(tmp) algfinaldata.append((algmedfinalfunvals[i], algmedmaxevals[i])) # significance test of best given algorithm against all others best_alg_idx = numpy.array(algerts).argsort(0)[ 0, :] # indexed by target index significance_versus_others = significance_all_best_vs_other( algentries, targets, best_alg_idx)[0] # Create the table table = [] tableHtml = [] spec = r'@{}c@{}|*{%d}{@{\,}r@{}X@{\,}}|@{}r@{}@{}l@{}' % ( len(targets) ) # in case StrLeft not working: replaced c@{} with l@{ } spec = r'@{}c@{}|*{%d}{@{}r@{}X@{}}|@{}r@{}@{}l@{}' % ( len(targets) ) # in case StrLeft not working: replaced c@{} with l@{ } extraeol = [] # Generate header lines if with_table_heading: header = funInfos[df[1]] if funInfos else 'f%d' % df[1] table.append([ r'\multicolumn{%d}{@{\,}c@{\,}}{{\textbf{%s}}}' % (2 * len(targets) + 2, header) ]) extraeol.append('') if function_targets_line is True or (function_targets_line and df[1] in function_targets_line): if isinstance(targetsOfInterest, pproc.RunlengthBasedTargetValues): curline = [r'\#FEs/D'] curlineHtml = ['<thead>\n<tr>\n<th>#FEs/D<br>REPLACEH</th>\n'] counter = 1 for i in targetsOfInterest.labels(): curline.append(r'\multicolumn{2}{@{}c@{}}{%s}' % i) curlineHtml.append('<td>%s<br>REPLACE%d</td>\n' % (i, counter)) counter += 1 else: curline = [r'$\Delta f_\mathrm{opt}$'] curlineHtml = [ '<thead>\n<tr>\n<th>Δ f<sub>opt</sub><br>REPLACEH</th>\n' ] counter = 1 for t in targets: curline.append( r'\multicolumn{2}{@{\,}X@{\,}}{%s}' % writeFEvals2(t, precision=1, isscientific=True)) curlineHtml.append( '<td>%s<br>REPLACE%d</td>\n' % (writeFEvals2( t, precision=1, isscientific=True), counter)) counter += 1 # curline.append(r'\multicolumn{2}{@{\,}X@{}|}{%s}' # % writeFEvals2(targets[-1], precision=1, isscientific=True)) curline.append(r'\multicolumn{2}{@{}l@{}}{\#succ}') curlineHtml.append('<td>#succ<br>REPLACEF</td>\n</tr>\n</thead>\n') table.append(curline) extraeol.append(r'\hline') # extraeol.append(r'\hline\arrayrulecolor{tableShade}') curline = [r'ERT$_{\text{best}}$' ] if with_table_heading else [r'\textbf{f%d}' % df[1]] replaceValue = 'ERT<sub>best</sub>' if with_table_heading else ( '<b>f%d</b>' % df[1]) curlineHtml = [ item.replace('REPLACEH', replaceValue) for item in curlineHtml ] if isinstance(targetsOfInterest, pproc.RunlengthBasedTargetValues): # write ftarget:fevals counter = 1 for i in xrange(len(refalgert[:-1])): temp = "%.1e" % targetsOfInterest((df[1], df[0]))[i] if temp[-2] == "0": temp = temp[:-2] + temp[-1] curline.append( r'\multicolumn{2}{@{}c@{}}{\textit{%s}:%s \quad}' % (temp, writeFEvalsMaxPrec(refalgert[i], 2))) replaceValue = '<i>%s</i>:%s' % ( temp, writeFEvalsMaxPrec(refalgert[i], 2)) curlineHtml = [ item.replace('REPLACE%d' % counter, replaceValue) for item in curlineHtml ] counter += 1 temp = "%.1e" % targetsOfInterest((df[1], df[0]))[-1] if temp[-2] == "0": temp = temp[:-2] + temp[-1] curline.append(r'\multicolumn{2}{@{}c@{}|}{\textit{%s}:%s }' % (temp, writeFEvalsMaxPrec(refalgert[-1], 2))) replaceValue = '<i>%s</i>:%s' % ( temp, writeFEvalsMaxPrec(refalgert[-1], 2)) curlineHtml = [ item.replace('REPLACE%d' % counter, replaceValue) for item in curlineHtml ] else: # write #fevals of the reference alg counter = 1 for i in refalgert[:-1]: curline.append(r'\multicolumn{2}{@{}c@{}}{%s \quad}' % writeFEvalsMaxPrec(i, 2)) curlineHtml = [ item.replace('REPLACE%d' % counter, writeFEvalsMaxPrec(i, 2)) for item in curlineHtml ] counter += 1 curline.append(r'\multicolumn{2}{@{}c@{}|}{%s}' % writeFEvalsMaxPrec(refalgert[-1], 2)) curlineHtml = [ item.replace('REPLACE%d' % counter, writeFEvalsMaxPrec(refalgert[-1], 2)) for item in curlineHtml ] # write the success ratio for the reference alg tmp2 = numpy.sum( numpy.isnan(refalgevals) == False) # count the nb of success curline.append('%d' % (tmp2)) if tmp2 > 0: curline.append('/%d' % len(refalgevals)) replaceValue = '%d/%d' % (tmp2, len(refalgevals)) else: replaceValue = '%d' % tmp2 curlineHtml = [ item.replace('REPLACEF', replaceValue) for item in curlineHtml ] table.append(curline[:]) tableHtml.extend(curlineHtml[:]) tableHtml.append('<tbody>\n') extraeol.append('') #for i, gna in enumerate(zip((1, 2, 3), ('bla', 'blo', 'bli'))): #print i, gna, gno #set_trace() # Format data #if df == (5, 17): #set_trace() header = r'\providecommand{\ntables}{7}' for i, alg in enumerate(algnames): tableHtml.append('<tr>\n') #algname, entries, irs, line, line2, succ, runs, testres1alg in zip(algnames, #data, dispersion, isBoldArray, isItalArray, nbsucc, nbruns, testres): commandname = r'\alg%stables' % numtotext(i) # header += r'\providecommand{%s}{{%s}{}}' % (commandname, str_to_latex(strip_pathname(alg))) header += r'\providecommand{%s}{\StrLeft{%s}{\ntables}}' % ( commandname, str_to_latex(strip_pathname1(alg))) curline = [ commandname + r'\hspace*{\fill}' ] # each list element becomes a &-separated table entry? curlineHtml = [ '<th>%s</th>\n' % str_to_latex(strip_pathname1(alg)) ] for j, tmp in enumerate( zip( algerts[i], algdisp[i], # j is target index isBoldArray[i], algtestres[i])): ert, dispersion, isBold, testres = tmp alignment = '@{\,}X@{\,}' if j == len(algerts[i]) - 1: alignment = '@{\,}X@{\,}|' data = ert / refalgert[j] # write star for significance against all other algorithms str_significance_subsup = '' str_significance_subsup_html = '' if (len(best_alg_idx) > 0 and len(significance_versus_others) > 0 and i == best_alg_idx[j] and nbtests * significance_versus_others[j][1] < 0.05): logp = -numpy.ceil( numpy.log10( nbtests * significance_versus_others[j][1])) logp = numpy.min( (9, logp)) # not messing up the format and handling inf str_significance_subsup = r"^{%s%s}" % ( significance_vs_others_symbol, str(int(logp)) if logp > 1 else '') str_significance_subsup_html = '<sup>%s%s</sup>' % ( significance_vs_others_symbol_html, str(int(logp)) if logp > 1 else '') # moved out of the above else: this was a bug!? z, p = testres if (nbtests * p) < 0.05 and data < 1. and z < 0.: if not numpy.isinf(refalgert[j]): tmpevals = algevals[i][j].copy() tmpevals[numpy.isnan(tmpevals)] = algentries[ i].maxevals[numpy.isnan(tmpevals)] bestevals = refalgentry.detEvals(targets) bestevals, bestalgalg = (bestevals[0][0], bestevals[1][0]) bestevals[numpy.isnan( bestevals)] = refalgentry.maxevals[bestalgalg][ numpy.isnan(bestevals)] tmpevals = numpy.array(sorted( tmpevals))[0:min(len(tmpevals), len(bestevals))] bestevals = numpy.array(sorted( bestevals))[0:min(len(tmpevals), len(bestevals))] #The conditions are now that ERT < ERT_best and # all(sorted(FEvals_best) > sorted(FEvals_current)). if numpy.isinf(refalgert[j]) or all(tmpevals < bestevals): nbstars = -numpy.ceil(numpy.log10(nbtests * p)) # tmp2[-1] += r'$^{%s}$' % superscript str_significance_subsup += r'_{%s%s}' % ( significance_vs_ref_symbol, str(int(nbstars)) if nbstars > 1 else '') str_significance_subsup_html = '<sub>%s%s</sub>' % ( significance_vs_ref_symbol_html, str(int(nbstars)) if nbstars > 1 else '') if str_significance_subsup: str_significance_subsup = '$%s$' % str_significance_subsup # format number in variable data if numpy.isnan(data): curline.append(r'\multicolumn{2}{%s}{.}' % alignment) else: if numpy.isinf(refalgert[j]): curline.append( r'\multicolumn{2}{%s}{\textbf{%s}\mbox{\tiny (%s)}%s}' % (alignment, writeFEvalsMaxPrec(algerts[i][j], 2), writeFEvalsMaxPrec(dispersion, precdispersion), str_significance_subsup)) curlineHtml.append( '<td sorttable_customkey=\"%f\"><b>%s</b> (%s)%s</td>\n' % (algerts[i][j], writeFEvalsMaxPrec(algerts[i][j], 2), writeFEvalsMaxPrec(dispersion, precdispersion), str_significance_subsup_html)) continue tmp = writeFEvalsMaxPrec(data, precfloat, maxfloatrepr=maxfloatrepr) tmpHtml = writeFEvalsMaxPrec(data, precfloat, maxfloatrepr=maxfloatrepr) sortKey = data if data >= maxfloatrepr or data < 0.01: # either inf or scientific notation if numpy.isinf(data) and j == len(algerts[i]) - 1: tmp += r'\,\textit{%s}' % writeFEvalsMaxPrec( algfinaldata[i][1], 0, maxfloatrepr=maxfloatrepr) tmpHtml += '<i>%s</i>' % writeFEvalsMaxPrec( algfinaldata[i][1], 0, maxfloatrepr=maxfloatrepr) sortKey = algfinaldata[i][1] else: tmp = writeFEvalsMaxPrec(data, precscien, maxfloatrepr=data) if isBold: tmpHtml = '<b>%s</b>' % tmp tmp = r'\textbf{%s}' % tmp if not numpy.isnan(dispersion): tmpdisp = dispersion / refalgert[j] if tmpdisp >= maxfloatrepr or tmpdisp < 0.005: # TODO: hack tmpdisp = writeFEvalsMaxPrec( tmpdisp, precdispersion, maxfloatrepr=tmpdisp) else: tmpdisp = writeFEvalsMaxPrec( tmpdisp, precdispersion, maxfloatrepr=maxfloatrepr) tmp += r'\mbox{\tiny (%s)}' % tmpdisp tmpHtml += ' (%s)' % tmpdisp curline.append( r'\multicolumn{2}{%s}{%s%s}' % (alignment, tmp, str_significance_subsup)) tmpHtml = tmpHtml.replace('$\infty$', '∞') if (numpy.isinf(sortKey)): sortKey = sys.maxint curlineHtml.append( '<td sorttable_customkey=\"%f\">%s%s</td>' % (sortKey, tmpHtml, str_significance_subsup_html)) else: tmp2 = tmp.split('.', 1) if len(tmp2) < 2: tmp2.append('') else: tmp2[-1] = '.' + tmp2[-1] if isBold: tmp3 = [] tmp3html = [] for k in tmp2: tmp3.append(r'\textbf{%s}' % k) tmp3html.append('<b>%s</b>' % k) tmp2 = tmp3 tmp2html = tmp3html else: tmp2html = [] tmp2html.extend(tmp2) if not numpy.isnan(dispersion): tmpdisp = dispersion / refalgert[j] if tmpdisp >= maxfloatrepr or tmpdisp < 0.01: tmpdisp = writeFEvalsMaxPrec( tmpdisp, precdispersion, maxfloatrepr=tmpdisp) else: tmpdisp = writeFEvalsMaxPrec( tmpdisp, precdispersion, maxfloatrepr=maxfloatrepr) tmp2[-1] += (r'\mbox{\tiny (%s)}' % (tmpdisp)) tmp2html[-1] += ' (%s)' % tmpdisp tmp2[-1] += str_significance_subsup tmp2html[-1] += str_significance_subsup_html curline.extend(tmp2) tmp2html = ("").join(str(item) for item in tmp2html) tmp2html = tmp2html.replace('$\infty$', '∞') curlineHtml.append( '<td sorttable_customkey=\"%f\">%s</td>' % (data, tmp2html)) curline.append('%d' % algnbsucc[i]) curline.append('/%d' % algnbruns[i]) table.append(curline) curlineHtml.append('<td sorttable_customkey=\"%d\">%d/%d</td>\n' % (algnbsucc[i], algnbsucc[i], algnbruns[i])) tableHtml.extend(curlineHtml[:]) extraeol.append('') # Write table res = tableXLaTeX(table, spec=spec, extraeol=extraeol) try: filename = os.path.join( outputdir, 'pptables_f%03d_%02dD.tex' % (df[1], df[0])) f = open(filename, 'w') f.write(header + '\n') f.write(res) res = ("").join(str(item) for item in tableHtml) res = '\n<table class=\"sortable\" style=\"width:800px \">\n%s</table>\n<p/>\n' % res if df[0] in (5, 20): filename = os.path.join( outputdir, genericsettings.many_algorithm_file_name + '.html') lines = [] with open(filename) as infile: for line in infile: if '<!--' + 'pptablesf%03d%02dDHtml' % ( df[1], df[0]) + '-->' in line: lines.append(res) lines.append(line) with open(filename, 'w') as outfile: for line in lines: outfile.write(line) if verbose: print 'Wrote table in %s' % filename except: raise else: f.close()
def main(argv=None): r"""Routine for post-processing COCO data from two algorithms. Provided with some data, this routine outputs figure and TeX files in a folder needed for the compilation of the provided LaTeX templates for comparing two algorithms (``*cmp.tex`` or ``*2*.tex``). The used template file needs to be edited so that the command ``\bbobdatapath`` points to the output folder created by this routine. The output files will contain performance tables, performance scaling figures and empirical cumulative distribution figures. On subsequent executions, new files will be added to the output folder, overwriting existing older files in the process. Keyword arguments: *argv* -- list of strings containing options and arguments. If not given, sys.argv is accessed. *argv* must list folders containing BBOB data files. Each of these folders should correspond to the data of ONE algorithm. Furthermore, argv can begin with, in any order, facultative option flags listed below. -h, --help displays this message. -v, --verbose verbose mode, prints out operations. -o OUTPUTDIR, --output-dir=OUTPUTDIR changes the default output directory (:file:`ppdata`) to :file:`OUTPUTDIR` --noise-free, --noisy processes only part of the data. --settings=SETTING changes the style of the output figures and tables. At the moment only the only differences are in the colors of the output figures. SETTING can be either "grayscale", "color" or "black-white". The default setting is "color". --fig-only, --rld-only, --tab-only, --sca-only these options can be used to output respectively the ERT graphs figures, run length distribution figures or the comparison tables scatter plot figures only. Any combination of these options results in no output. --conv if this option is chosen, additionally convergence plots for each function and algorithm are generated. --rld-single-fcts generate also runlength distribution figures for each single function. --expensive runlength-based f-target values and fixed display limits, useful with comparatively small budgets. By default the setting is based on the budget used in the data. --not-expensive expensive setting off. --svg generate also the svg figures which are used in html files Exceptions raised: *Usage* -- Gives back a usage message. Examples: * Calling the rungeneric2.py interface from the command line:: $ python bbob_pproc/rungeneric2.py -v Alg0-baseline Alg1-of-interest will post-process the data from folders :file:`Alg0-baseline` and :file:`Alg1-of-interest`, the former containing data for the reference algorithm (zero-th) and the latter data for the algorithm of concern (first). The results will be output in the default output folder. The ``-v`` option adds verbosity. * From the python interpreter (requires that the path to this package is in python search path):: >> import bbob_pproc as bb >> bb.rungeneric2.main('-o outputfolder PSO DEPSO'.split()) This will execute the post-processing on the data found in folder :file:`PSO` and :file:`DEPSO`. The ``-o`` option changes the output folder from the default to :file:`outputfolder`. """ if argv is None: argv = sys.argv[1:] # The zero-th input argument which is the name of the calling script is # disregarded. global ftarget try: try: opts, args = getopt.getopt(argv, genericsettings.shortoptlist, genericsettings.longoptlist) except getopt.error, msg: raise Usage(msg) if not (args): usage() sys.exit() #Process options outputdir = genericsettings.outputdir for o, a in opts: if o in ("-v", "--verbose"): genericsettings.verbose = True elif o in ("-h", "--help"): usage() sys.exit() elif o in ("-o", "--output-dir"): outputdir = a elif o == "--fig-only": genericsettings.isRLDistr = False genericsettings.isTab = False genericsettings.isScatter = False elif o == "--rld-only": genericsettings.isFig = False genericsettings.isTab = False genericsettings.isScatter = False elif o == "--tab-only": genericsettings.isFig = False genericsettings.isRLDistr = False genericsettings.isScatter = False elif o == "--sca-only": genericsettings.isFig = False genericsettings.isRLDistr = False genericsettings.isTab = False elif o == "--noisy": genericsettings.isNoisy = True elif o == "--noise-free": genericsettings.isNoiseFree = True elif o == "--settings": genericsettings.inputsettings = a elif o == "--conv": genericsettings.isConv = True elif o == "--rld-single-fcts": genericsettings.isRldOnSingleFcts = True elif o == "--runlength-based": genericsettings.runlength_based_targets = True elif o == "--expensive": genericsettings.isExpensive = True # comprises runlength-based elif o == "--not-expensive": genericsettings.isExpensive = False elif o == "--svg": genericsettings.generate_svg_files = True elif o == "--los-only": warnings.warn( "option --los-only will have no effect with rungeneric2.py" ) elif o == "--crafting-effort=": warnings.warn( "option --crafting-effort will have no effect with rungeneric2.py" ) elif o in ("-p", "--pickle"): warnings.warn( "option --pickle will have no effect with rungeneric2.py") else: assert False, "unhandled option" # from bbob_pproc import bbob2010 as inset # input settings if genericsettings.inputsettings == "color": from bbob_pproc import genericsettings as inset # input settings config.config() elif genericsettings.inputsettings == "grayscale": # probably very much obsolete from bbob_pproc import grayscalesettings as inset # input settings elif genericsettings.inputsettings == "black-white": # probably very much obsolete from bbob_pproc import bwsettings as inset # input settings else: txt = ('Settings: %s is not an appropriate ' % genericsettings.inputsettings + 'argument for input flag "--settings".') raise Usage(txt) if (not genericsettings.verbose): warnings.simplefilter('module') warnings.simplefilter('ignore') print("Post-processing will generate comparison " + "data in folder %s" % outputdir) print " this might take several minutes." dsList, sortedAlgs, dictAlg = processInputArgs( args, verbose=genericsettings.verbose) if 1 < 3 and len(sortedAlgs) != 2: raise ValueError( 'rungeneric2.py needs exactly two algorithms to compare, found: ' + str(sortedAlgs) + '\n use rungeneric.py (or rungenericmany.py) to compare more algorithms. ' ) if not dsList: sys.exit() for i in dictAlg: if genericsettings.isNoisy and not genericsettings.isNoiseFree: dictAlg[i] = dictAlg[i].dictByNoise().get( 'nzall', DataSetList()) if genericsettings.isNoiseFree and not genericsettings.isNoisy: dictAlg[i] = dictAlg[i].dictByNoise().get( 'noiselessall', DataSetList()) for i in dsList: if i.dim not in genericsettings.dimensions_to_display: continue if (dict((j, i.instancenumbers.count(j)) for j in set(i.instancenumbers)) < inset.instancesOfInterest): warnings.warn('The data of %s do not list ' % (i) + 'the correct instances ' + 'of function F%d.' % (i.funcId)) if len(sortedAlgs) < 2: raise Usage('Expect data from two different algorithms, could ' + 'only find one.') elif len(sortedAlgs) > 2: warnings.warn('Data from folders: %s ' % (sortedAlgs) + 'were found, the first two will be processed.') # Group by algorithm dsList0 = dictAlg[sortedAlgs[0]] if not dsList0: raise Usage('Could not find data for algorithm %s.' % (sortedAlgs[0])) dsList1 = dictAlg[sortedAlgs[1]] if not dsList1: raise Usage('Could not find data for algorithm %s.' % (sortedAlgs[0])) # get the name of each algorithm from the input arguments tmppath0, alg0name = os.path.split(sortedAlgs[0].rstrip(os.sep)) tmppath1, alg1name = os.path.split(sortedAlgs[1].rstrip(os.sep)) for i in dsList0: i.algId = alg0name for i in dsList1: i.algId = alg1name # compute maxfuneval values dict_max_fun_evals1 = {} dict_max_fun_evals2 = {} for ds in dsList0: dict_max_fun_evals1[ds.dim] = np.max( (dict_max_fun_evals1.setdefault(ds.dim, 0), float(np.max(ds.maxevals)))) for ds in dsList1: dict_max_fun_evals2[ds.dim] = np.max( (dict_max_fun_evals2.setdefault(ds.dim, 0), float(np.max(ds.maxevals)))) config.target_values( genericsettings.isExpensive, { 1: min([ max([ val / dim for dim, val in dict_max_fun_evals1.iteritems() ]), max([ val / dim for dim, val in dict_max_fun_evals2.iteritems() ]) ]) }) config.config() ######################### Post-processing ############################# if genericsettings.isFig or genericsettings.isRLDistr or genericsettings.isTab or genericsettings.isScatter: if not os.path.exists(outputdir): os.mkdir(outputdir) if genericsettings.verbose: print 'Folder %s was created.' % (outputdir) # prepend the algorithm name command to the tex-command file abc = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' lines = [] for i, alg in enumerate(args): lines.append('\\providecommand{\\algorithm' + abc[i] + '}{' + str_to_latex(strip_pathname1(alg)) + '}') prepend_to_file( os.path.join(outputdir, 'bbob_pproc_commands.tex'), lines, 1000, 'bbob_proc_commands.tex truncated, consider removing the file before the text run' ) # Check whether both input arguments list noisy and noise-free data dictFN0 = dsList0.dictByNoise() dictFN1 = dsList1.dictByNoise() k0 = set(dictFN0.keys()) k1 = set(dictFN1.keys()) symdiff = k1 ^ k0 # symmetric difference if symdiff: tmpdict = {} for i, noisegrp in enumerate(symdiff): if noisegrp == 'nzall': tmp = 'noisy' elif noisegrp == 'noiselessall': tmp = 'noiseless' if dictFN0.has_key(noisegrp): tmp2 = sortedAlgs[0] elif dictFN1.has_key(noisegrp): tmp2 = sortedAlgs[1] tmpdict.setdefault(tmp2, []).append(tmp) txt = [] for i, j in tmpdict.iteritems(): txt.append('Only input folder %s lists %s data.' % (i, ' and '.join(j))) raise Usage('Data Mismatch: \n ' + ' '.join(txt) + '\nTry using --noise-free or --noisy flags.') if genericsettings.isFig: plt.rc("axes", **inset.rcaxeslarger) plt.rc("xtick", **inset.rcticklarger) plt.rc("ytick", **inset.rcticklarger) plt.rc("font", **inset.rcfontlarger) plt.rc("legend", **inset.rclegendlarger) plt.rc('pdf', fonttype=42) ppfig2.main(dsList0, dsList1, ppfig2_ftarget, outputdir, genericsettings.verbose) print "log ERT1/ERT0 vs target function values done." plt.rc("axes", **inset.rcaxes) plt.rc("xtick", **inset.rctick) plt.rc("ytick", **inset.rctick) plt.rc("font", **inset.rcfont) plt.rc("legend", **inset.rclegend) plt.rc('pdf', fonttype=42) if genericsettings.isRLDistr: if len(dictFN0) > 1 or len(dictFN1) > 1: warnings.warn('Data for functions from both the noisy and ' + 'non-noisy testbeds have been found. Their ' + 'results will be mixed in the "all functions" ' + 'ECDF figures.') dictDim0 = dsList0.dictByDim() dictDim1 = dsList1.dictByDim() # ECDFs of ERT ratios for dim in set(dictDim0.keys()) & set(dictDim1.keys()): if dim in inset.rldDimsOfInterest: # ECDF for all functions altogether try: pprldistr2.main(dictDim0[dim], dictDim1[dim], dim, inset.rldValsOfInterest, outputdir, '%02dD_all' % dim, genericsettings.verbose) except KeyError: warnings.warn('Could not find some data in %d-D.' % (dim)) continue # ECDFs per function groups dictFG0 = dictDim0[dim].dictByFuncGroup() dictFG1 = dictDim1[dim].dictByFuncGroup() for fGroup in set(dictFG0.keys()) & set(dictFG1.keys()): pprldistr2.main(dictFG1[fGroup], dictFG0[fGroup], dim, inset.rldValsOfInterest, outputdir, '%02dD_%s' % (dim, fGroup), genericsettings.verbose) # ECDFs per noise groups dictFN0 = dictDim0[dim].dictByNoise() dictFN1 = dictDim1[dim].dictByNoise() for fGroup in set(dictFN0.keys()) & set(dictFN1.keys()): pprldistr2.main(dictFN1[fGroup], dictFN0[fGroup], dim, inset.rldValsOfInterest, outputdir, '%02dD_%s' % (dim, fGroup), genericsettings.verbose) prepend_to_file( os.path.join(outputdir, 'bbob_pproc_commands.tex'), [ '\\providecommand{\\bbobpprldistrlegendtwo}[1]{', pprldistr.caption_two( ), # depends on the config setting, should depend on maxfevals '}' ]) print "ECDF runlength ratio graphs done." for dim in set(dictDim0.keys()) & set(dictDim1.keys()): pprldistr.fmax = None #Resetting the max final value pprldistr.evalfmax = None #Resetting the max #fevalsfactor # ECDFs of all functions altogether if dim in inset.rldDimsOfInterest: try: pprldistr.comp( dictDim1[dim], dictDim0[dim], inset. rldValsOfInterest, # TODO: let rldVals... possibly be RL-based targets True, outputdir, 'all', genericsettings.verbose) except KeyError: warnings.warn('Could not find some data in %d-D.' % (dim)) continue # ECDFs per function groups dictFG0 = dictDim0[dim].dictByFuncGroup() dictFG1 = dictDim1[dim].dictByFuncGroup() for fGroup in set(dictFG0.keys()) & set(dictFG1.keys()): pprldistr.comp(dictFG1[fGroup], dictFG0[fGroup], inset.rldValsOfInterest, True, outputdir, '%s' % fGroup, genericsettings.verbose) # ECDFs per noise groups dictFN0 = dictDim0[dim].dictByNoise() dictFN1 = dictDim1[dim].dictByNoise() for fGroup in set(dictFN0.keys()) & set(dictFN1.keys()): pprldistr.comp(dictFN1[fGroup], dictFN0[fGroup], inset.rldValsOfInterest, True, outputdir, '%s' % fGroup, genericsettings.verbose) if genericsettings.isRldOnSingleFcts: # copy-paste from above, here for each function instead of function groups # ECDFs for each function pprldmany.all_single_functions(dictAlg, sortedAlgs, outputdir, genericsettings.verbose) print "ECDF runlength graphs done." if genericsettings.isConv: ppconverrorbars.main(dictAlg, outputdir, genericsettings.verbose) if genericsettings.isScatter: if genericsettings.runlength_based_targets: ppscatter.targets = ppscatter.runlength_based_targets ppscatter.main(dsList1, dsList0, outputdir, verbose=genericsettings.verbose) prepend_to_file(os.path.join(outputdir, 'bbob_pproc_commands.tex'), [ '\\providecommand{\\bbobppscatterlegend}[1]{', ppscatter.figure_caption(), '}' ]) replace_in_file( os.path.join(outputdir, genericsettings.two_algorithm_file_name + '.html'), '##bbobppscatterlegend##', ppscatter.figure_caption_html()) print "Scatter plots done." if genericsettings.isTab: dictNG0 = dsList0.dictByNoise() dictNG1 = dsList1.dictByNoise() for nGroup in set(dictNG0.keys()) & set(dictNG1.keys()): # split table in as many as necessary dictFunc0 = dictNG0[nGroup].dictByFunc() dictFunc1 = dictNG1[nGroup].dictByFunc() funcs = list(set(dictFunc0.keys()) & set(dictFunc1.keys())) if len(funcs) > 24: funcs.sort() nbgroups = int(numpy.ceil(len(funcs) / 24.)) def split_seq(seq, nbgroups): newseq = [] splitsize = 1.0 / nbgroups * len(seq) for i in range(nbgroups): newseq.append( seq[int(round(i * splitsize) ):int(round((i + 1) * splitsize))]) return newseq groups = split_seq(funcs, nbgroups) # merge group0 = [] group1 = [] for i, g in enumerate(groups): tmp0 = DataSetList() tmp1 = DataSetList() for f in g: tmp0.extend(dictFunc0[f]) tmp1.extend(dictFunc1[f]) group0.append(tmp0) group1.append(tmp1) for i, g in enumerate(zip(group0, group1)): pptable2.main(g[0], g[1], inset.tabDimsOfInterest, outputdir, '%s%d' % (nGroup, i), genericsettings.verbose) else: if 11 < 3: # future handling: dictFunc0 = dsList0.dictByFunc() dictFunc1 = dsList1.dictByFunc() funcs = list( set(dictFunc0.keys()) & set(dictFunc1.keys())) funcs.sort() # nbgroups = int(numpy.ceil(len(funcs)/testbedsettings.numberOfFunctions)) # pptable2.main(dsList0, dsList1, # testbedsettings.tabDimsOfInterest, outputdir, # '%s' % (testbedsettings.testbedshortname), genericsettings.verbose) else: pptable2.main(dictNG0[nGroup], dictNG1[nGroup], inset.tabDimsOfInterest, outputdir, '%s' % (nGroup), genericsettings.verbose) if isinstance(pptable2.targetsOfInterest, pproc.RunlengthBasedTargetValues): prepend_to_file( os.path.join(outputdir, 'bbob_pproc_commands.tex'), [ '\\providecommand{\\bbobpptablestwolegend}[1]{', pptable2.table_caption_expensive, '}' ]) else: prepend_to_file( os.path.join(outputdir, 'bbob_pproc_commands.tex'), [ '\\providecommand{\\bbobpptablestwolegend}[1]{', pptable2.table_caption, '}' ]) htmlFileName = os.path.join( outputdir, genericsettings.two_algorithm_file_name + '.html') key = '##bbobpptablestwolegendexpensive##' if isinstance( pptable2.targetsOfInterest, pproc.RunlengthBasedTargetValues ) else '##bbobpptablestwolegend##' replace_in_file(htmlFileName, '##bbobpptablestwolegend##', htmldesc.getValue(key)) alg0 = set(i[0] for i in dsList0.dictByAlg().keys()).pop().replace( genericsettings.extraction_folder_prefix, '')[0:3] alg1 = set(i[0] for i in dsList1.dictByAlg().keys()).pop().replace( genericsettings.extraction_folder_prefix, '')[0:3] replace_in_file(htmlFileName, 'algorithmAshort', alg0) replace_in_file(htmlFileName, 'algorithmBshort', alg1) for i, alg in enumerate(args): replace_in_file(htmlFileName, 'algorithm' + abc[i], str_to_latex(strip_pathname1(alg))) print "Tables done." if genericsettings.isScaleUp: plt.rc("axes", labelsize=20, titlesize=24) plt.rc("xtick", labelsize=20) plt.rc("ytick", labelsize=20) plt.rc("font", size=20) plt.rc("legend", fontsize=20) plt.rc('pdf', fonttype=42) if genericsettings.runlength_based_targets: ftarget = RunlengthBasedTargetValues([ target_runlength ]) # TODO: make this more variable but also consistent ppfigs.main(dictAlg, genericsettings.two_algorithm_file_name, sortedAlgs, ftarget, outputdir, genericsettings.verbose) plt.rcdefaults() print "Scaling figures done." if genericsettings.isFig or genericsettings.isRLDistr or genericsettings.isTab or genericsettings.isScatter or genericsettings.isScaleUp: print "Output data written to folder %s" % outputdir plt.rcdefaults()
if 1 < 3: print ("Post-processing: will generate output " + "data in folder %s" % outputdir) print " this might take several minutes." if not os.path.exists(outputdir): os.makedirs(outputdir) if genericsettings.verbose: print 'Folder %s was created.' % (outputdir) # prepend the algorithm name command to the tex-command file lines = [] for i, alg in enumerate(args): lines.append('\\providecommand{\\algorithm' + pptex.numtotext(i) + '}{' + str_to_latex(strip_pathname1(alg)) + '}') prepend_to_file(os.path.join(outputdir, 'bbob_pproc_commands.tex'), lines, 5000, 'bbob_proc_commands.tex truncated, consider removing the file before the text run' ) dsList, sortedAlgs, dictAlg = processInputArgs(args, verbose=genericsettings.verbose) if not dsList: sys.exit() if (any(ds.isBiobjective() for ds in dsList) and any(not ds.isBiobjective() for ds in dsList)): sys.exit() for i in dictAlg:
def main(argv=None): r"""Post-processing COCO data of a single algorithm. Provided with some data, this routine outputs figure and TeX files in a folder needed for the compilation of the provided LaTeX templates for one algorithm (``*article.tex`` or ``*1*.tex``). The used template file needs to be edited so that the commands ``\bbobdatapath`` and ``\algfolder`` point to the output folder created by this routine. These output files will contain performance tables, performance scaling figures and empirical cumulative distribution figures. On subsequent executions, new files will be added to the output folder, overwriting existing older files in the process. Keyword arguments: *argv* -- list of strings containing options and arguments. If not given, sys.argv is accessed. *argv* should list either names of :file:`info` files or folders containing :file:`info` files. argv can also contain post-processed :file:`pickle` files generated by this routine. Furthermore, *argv* can begin with, in any order, facultative option flags listed below. -h, --help displays this message. -v, --verbose verbose mode, prints out all operations. -p, --pickle generates pickle post processed data files. -o OUTPUTDIR, --output-dir=OUTPUTDIR changes the default output directory (:file:`ppdata`) to :file:`OUTPUTDIR`. --crafting-effort=VALUE sets the crafting effort to VALUE (float). Otherwise the default value of 0. will be used. --noise-free, --noisy processes only part of the data. --settings=SETTINGS changes the style of the output figures and tables. At the moment the only differences are in the colors of the output figures. SETTINGS can be either "grayscale", "color" or "black-white". The default setting is "color". --tab-only, --fig-only, --rld-only, --los-only these options can be used to output respectively the TeX tables, convergence and ERTs graphs figures, run length distribution figures, ERT loss ratio figures only. A combination of any two of these options results in no output. --conv if this option is chosen, additionally convergence plots for each function and algorithm are generated. --rld-single-fcts generate also runlength distribution figures for each single function. --expensive runlength-based f-target values and fixed display limits, useful with comparatively small budgets. By default the setting is based on the budget used in the data. --not-expensive expensive setting off. --svg generate also the svg figures which are used in html files --runlength-based runlength-based f-target values, such that the "level of difficulty" is similar for all functions. Exceptions raised: *Usage* -- Gives back a usage message. Examples: * Calling the rungeneric1.py interface from the command line:: $ python bbob_pproc/rungeneric1.py -v experiment1 will post-process the folder experiment1 and all its containing data, base on the .info files found in the folder. The result will appear in the default output folder. The -v option adds verbosity. :: $ python bbob_pproc/rungeneric1.py -o exp2 experiment2/*.info This will execute the post-processing on the info files found in :file:`experiment2`. The result will be located in the alternative location :file:`exp2`. * Loading this package and calling the main from the command line (requires that the path to this package is in python search path):: $ python -m bbob_pproc.rungeneric1 -h This will print out this help message. * From the python interpreter (requires that the path to this package is in python search path):: >> import bbob_pproc as bb >> bb.rungeneric1.main('-o outputfolder folder1'.split()) This will execute the post-processing on the index files found in :file:`folder1`. The ``-o`` option changes the output folder from the default to :file:`outputfolder`. """ if argv is None: argv = sys.argv[1:] # The zero-th input argument which is the name of the calling script is # disregarded. if 1 < 3: opts, args = getopt.getopt(argv, genericsettings.shortoptlist, genericsettings.longoptlist) if 11 < 3: try: opts, args = getopt.getopt(argv, genericsettings.shortoptlist, genericsettings.longoptlist) except getopt.error, msg: raise Usage(msg) if not (args) and not "--help" in argv and not "h" in argv: print "not enough input arguments given" print "cave: the following options also need an argument:" print [o for o in genericsettings.longoptlist if o[-1] == "="] print "options given:" print opts print "try --help for help" sys.exit() # Process options outputdir = genericsettings.outputdir for o, a in opts: if o in ("-v", "--verbose"): genericsettings.verbose = True elif o in ("-h", "--help"): usage() sys.exit() elif o in ("-p", "--pickle"): genericsettings.isPickled = True elif o in ("-o", "--output-dir"): outputdir = a elif o == "--noisy": genericsettings.isNoisy = True elif o == "--noise-free": genericsettings.isNoiseFree = True # The next 4 are for testing purpose elif o == "--tab-only": genericsettings.isFig = False genericsettings.isRLDistr = False genericsettings.isLogLoss = False elif o == "--fig-only": genericsettings.isTab = False genericsettings.isRLDistr = False genericsettings.isLogLoss = False elif o == "--rld-only": genericsettings.isTab = False genericsettings.isFig = False genericsettings.isLogLoss = False elif o == "--los-only": genericsettings.isTab = False genericsettings.isFig = False genericsettings.isRLDistr = False elif o == "--crafting-effort": try: genericsettings.inputCrE = float(a) except ValueError: raise Usage("Expect a valid float for flag crafting-effort.") elif o == "--settings": genericsettings.inputsettings = a elif o == "--conv": genericsettings.isConv = True elif o == "--rld-single-fcts": genericsettings.isRldOnSingleFcts = True elif o == "--runlength-based": genericsettings.runlength_based_targets = True elif o == "--expensive": genericsettings.isExpensive = True # comprises runlength-based elif o == "--not-expensive": genericsettings.isExpensive = False elif o == "--svg": genericsettings.generate_svg_files = True elif o == "--sca-only": warnings.warn("option --sca-only will have no effect with rungeneric1.py") else: assert False, "unhandled option" # from bbob_pproc import bbob2010 as inset # input settings if genericsettings.inputsettings == "color": from bbob_pproc import genericsettings as inset # input settings elif genericsettings.inputsettings == "grayscale": from bbob_pproc import grayscalesettings as inset # input settings elif genericsettings.inputsettings == "black-white": from bbob_pproc import bwsettings as inset # input settings else: txt = ( "Settings: %s is not an appropriate " % genericsettings.inputsettings + 'argument for input flag "--settings".' ) raise Usage(txt) if 11 < 3: from bbob_pproc import config # input settings config.config(False) import imp # import testbedsettings as testbedsettings # input settings try: fp, pathname, description = imp.find_module("testbedsettings") testbedsettings = imp.load_module("testbedsettings", fp, pathname, description) finally: fp.close() if not genericsettings.verbose: warnings.simplefilter("module") # warnings.simplefilter('ignore') # get directory name if outputdir is a archive file algfolder = findfiles.get_output_directory_subfolder(args[0]) outputdir = os.path.join(outputdir, algfolder) print ("Post-processing (1): will generate output " + "data in folder %s" % outputdir) print " this might take several minutes." filelist = list() for i in args: i = i.strip() if os.path.isdir(i): filelist.extend(findfiles.main(i, genericsettings.verbose)) elif os.path.isfile(i): filelist.append(i) else: txt = "Input file or folder %s could not be found." % i print txt raise Usage(txt) dsList = DataSetList(filelist, genericsettings.verbose) if not dsList: raise Usage("Nothing to do: post-processing stopped.") if genericsettings.isNoisy and not genericsettings.isNoiseFree: dsList = dsList.dictByNoise().get("nzall", DataSetList()) if genericsettings.isNoiseFree and not genericsettings.isNoisy: dsList = dsList.dictByNoise().get("noiselessall", DataSetList()) # compute maxfuneval values dict_max_fun_evals = {} for ds in dsList: dict_max_fun_evals[ds.dim] = np.max((dict_max_fun_evals.setdefault(ds.dim, 0), float(np.max(ds.maxevals)))) from bbob_pproc import config config.target_values(genericsettings.isExpensive, dict_max_fun_evals) config.config(dsList.isBiobjective()) if genericsettings.verbose: for i in dsList: if dict((j, i.instancenumbers.count(j)) for j in set(i.instancenumbers)) != inset.instancesOfInterest: warnings.warn( "The data of %s do not list " % (i) + "the correct instances " + "of function F%d." % (i.funcId) ) dictAlg = dsList.dictByAlg() if len(dictAlg) > 1: warnings.warn("Data with multiple algId %s " % str(dictAlg.keys()) + "will be processed together.") # TODO: in this case, all is well as long as for a given problem # (given dimension and function) there is a single instance of # DataSet associated. If there are more than one, the first one only # will be considered... which is probably not what one would expect. # TODO: put some errors where this case would be a problem. # raise Usage? if genericsettings.isFig or genericsettings.isTab or genericsettings.isRLDistr or genericsettings.isLogLoss: if not os.path.exists(outputdir): os.makedirs(outputdir) if genericsettings.verbose: print "Folder %s was created." % (outputdir) if genericsettings.isPickled: dsList.pickle(verbose=genericsettings.verbose) if genericsettings.isConv: ppconverrorbars.main(dictAlg, outputdir, genericsettings.verbose) if genericsettings.isFig: print "Scaling figures...", sys.stdout.flush() # ERT/dim vs dim. plt.rc("axes", **inset.rcaxeslarger) plt.rc("xtick", **inset.rcticklarger) plt.rc("ytick", **inset.rcticklarger) plt.rc("font", **inset.rcfontlarger) plt.rc("legend", **inset.rclegendlarger) plt.rc("pdf", fonttype=42) ppfigdim.main(dsList, ppfigdim.values_of_interest, outputdir, genericsettings.verbose) plt.rcdefaults() print_done() plt.rc("axes", **inset.rcaxes) plt.rc("xtick", **inset.rctick) plt.rc("ytick", **inset.rctick) plt.rc("font", **inset.rcfont) plt.rc("legend", **inset.rclegend) plt.rc("pdf", fonttype=42) if genericsettings.isTab: print "TeX tables...", sys.stdout.flush() dictNoise = dsList.dictByNoise() for noise, sliceNoise in dictNoise.iteritems(): pptable.main(sliceNoise, inset.tabDimsOfInterest, outputdir, noise, genericsettings.verbose) print_done() if genericsettings.isRLDistr: print "ECDF graphs...", sys.stdout.flush() dictNoise = dsList.dictByNoise() if len(dictNoise) > 1: warnings.warn( "Data for functions from both the noisy and " "non-noisy testbeds have been found. Their " 'results will be mixed in the "all functions" ' "ECDF figures." ) dictDim = dsList.dictByDim() for dim in inset.rldDimsOfInterest: try: sliceDim = dictDim[dim] except KeyError: continue pprldistr.main(sliceDim, True, outputdir, "all", genericsettings.verbose) dictNoise = sliceDim.dictByNoise() for noise, sliceNoise in dictNoise.iteritems(): pprldistr.main(sliceNoise, True, outputdir, "%s" % noise, genericsettings.verbose) dictFG = sliceDim.dictByFuncGroup() for fGroup, sliceFuncGroup in dictFG.items(): pprldistr.main(sliceFuncGroup, True, outputdir, "%s" % fGroup, genericsettings.verbose) pprldistr.fmax = None # Resetting the max final value pprldistr.evalfmax = None # Resetting the max #fevalsfactor if ( genericsettings.isRldOnSingleFcts ): # copy-paste from above, here for each function instead of function groups # ECDFs for each function pprldmany.all_single_functions(dictAlg, None, outputdir, genericsettings.verbose) print_done() if genericsettings.isLogLoss: print "ERT loss ratio figures and tables...", sys.stdout.flush() for ng, sliceNoise in dsList.dictByNoise().iteritems(): if ng == "noiselessall": testbed = "noiseless" elif ng == "nzall": testbed = "noisy" txt = "Please input crafting effort value " + "for %s testbed:\n CrE = " % testbed CrE = genericsettings.inputCrE while CrE is None: try: CrE = float(raw_input(txt)) except (SyntaxError, NameError, ValueError): print "Float value required." dictDim = sliceNoise.dictByDim() for d in inset.rldDimsOfInterest: try: sliceDim = dictDim[d] except KeyError: continue info = "%s" % ng pplogloss.main(sliceDim, CrE, True, outputdir, info, verbose=genericsettings.verbose) pplogloss.generateTable(sliceDim, CrE, outputdir, info, verbose=genericsettings.verbose) for fGroup, sliceFuncGroup in sliceDim.dictByFuncGroup().iteritems(): info = "%s" % fGroup pplogloss.main(sliceFuncGroup, CrE, True, outputdir, info, verbose=genericsettings.verbose) pplogloss.evalfmax = None # Resetting the max #fevalsfactor print_done() latex_commands_file = os.path.join(outputdir.split(os.sep)[0], "bbob_pproc_commands.tex") html_file = os.path.join(outputdir, genericsettings.single_algorithm_file_name + ".html") prepend_to_file( latex_commands_file, ["\\providecommand{\\bbobloglosstablecaption}[1]{", pplogloss.table_caption, "}"] ) prepend_to_file( latex_commands_file, ["\\providecommand{\\bbobloglossfigurecaption}[1]{", pplogloss.figure_caption, "}"] ) prepend_to_file( latex_commands_file, [ "\\providecommand{\\bbobpprldistrlegend}[1]{", pprldistr.caption_single( np.max([val / dim for dim, val in dict_max_fun_evals.iteritems()]) ), # depends on the config setting, should depend on maxfevals "}", ], ) replace_in_file( html_file, r"TOBEREPLACED", "D, ".join([str(i) for i in pprldistr.single_runlength_factors[:6]]) + "D,…", ) prepend_to_file( latex_commands_file, ["\\providecommand{\\bbobppfigdimlegend}[1]{", ppfigdim.scaling_figure_caption(), "}"] ) prepend_to_file(latex_commands_file, ["\\providecommand{\\bbobpptablecaption}[1]{", pptable.table_caption, "}"]) prepend_to_file(latex_commands_file, ["\\providecommand{\\algfolder}{" + algfolder + "/}"]) prepend_to_file( latex_commands_file, [ "\\providecommand{\\algname}{" + (str_to_latex(strip_pathname1(args[0])) if len(args) == 1 else str_to_latex(dsList[0].algId)) + "{}}" ], ) if genericsettings.isFig or genericsettings.isTab or genericsettings.isRLDistr or genericsettings.isLogLoss: print "Output data written to folder %s" % outputdir plt.rcdefaults()
def main(argv=None): r"""Post-processing COCO data of a single algorithm. Provided with some data, this routine outputs figure and TeX files in a folder needed for the compilation of the provided LaTeX templates for one algorithm (``*article.tex`` or ``*1*.tex``). The used template file needs to be edited so that the commands ``\bbobdatapath`` and ``\algfolder`` point to the output folder created by this routine. These output files will contain performance tables, performance scaling figures and empirical cumulative distribution figures. On subsequent executions, new files will be added to the output folder, overwriting existing older files in the process. Keyword arguments: *argv* -- list of strings containing options and arguments. If not given, sys.argv is accessed. *argv* should list either names of :file:`info` files or folders containing :file:`info` files. argv can also contain post-processed :file:`pickle` files generated by this routine. Furthermore, *argv* can begin with, in any order, facultative option flags listed below. -h, --help displays this message. -v, --verbose verbose mode, prints out all operations. -p, --pickle generates pickle post processed data files. -o OUTPUTDIR, --output-dir=OUTPUTDIR changes the default output directory (:file:`ppdata`) to :file:`OUTPUTDIR`. --crafting-effort=VALUE sets the crafting effort to VALUE (float). Otherwise the default value of 0. will be used. --noise-free, --noisy processes only part of the data. --settings=SETTINGS changes the style of the output figures and tables. At the moment the only differences are in the colors of the output figures. SETTINGS can be either "grayscale", "color" or "black-white". The default setting is "color". --tab-only, --fig-only, --rld-only, --los-only these options can be used to output respectively the TeX tables, convergence and ERTs graphs figures, run length distribution figures, ERT loss ratio figures only. A combination of any two of these options results in no output. --conv if this option is chosen, additionally convergence plots for each function and algorithm are generated. --rld-single-fcts generate also runlength distribution figures for each single function. --expensive runlength-based f-target values and fixed display limits, useful with comparatively small budgets. By default the setting is based on the budget used in the data. --not-expensive expensive setting off. --svg generate also the svg figures which are used in html files --runlength-based runlength-based f-target values, such that the "level of difficulty" is similar for all functions. Exceptions raised: *Usage* -- Gives back a usage message. Examples: * Calling the rungeneric1.py interface from the command line:: $ python bbob_pproc/rungeneric1.py -v experiment1 will post-process the folder experiment1 and all its containing data, base on the .info files found in the folder. The result will appear in the default output folder. The -v option adds verbosity. :: $ python bbob_pproc/rungeneric1.py -o exp2 experiment2/*.info This will execute the post-processing on the info files found in :file:`experiment2`. The result will be located in the alternative location :file:`exp2`. * Loading this package and calling the main from the command line (requires that the path to this package is in python search path):: $ python -m bbob_pproc.rungeneric1 -h This will print out this help message. * From the python interpreter (requires that the path to this package is in python search path):: >> import bbob_pproc as bb >> bb.rungeneric1.main('-o outputfolder folder1'.split()) This will execute the post-processing on the index files found in :file:`folder1`. The ``-o`` option changes the output folder from the default to :file:`outputfolder`. """ if argv is None: argv = sys.argv[1:] # The zero-th input argument which is the name of the calling script is # disregarded. if 1 < 3: opts, args = getopt.getopt(argv, genericsettings.shortoptlist, genericsettings.longoptlist) if 11 < 3: try: opts, args = getopt.getopt(argv, genericsettings.shortoptlist, genericsettings.longoptlist) except getopt.error, msg: raise Usage(msg) if not (args) and not '--help' in argv and not 'h' in argv: print 'not enough input arguments given' print 'cave: the following options also need an argument:' print [o for o in genericsettings.longoptlist if o[-1] == '='] print 'options given:' print opts print 'try --help for help' sys.exit() # Process options outputdir = genericsettings.outputdir for o, a in opts: if o in ("-v", "--verbose"): genericsettings.verbose = True elif o in ("-h", "--help"): usage() sys.exit() elif o in ("-p", "--pickle"): genericsettings.isPickled = True elif o in ("-o", "--output-dir"): outputdir = a elif o == "--noisy": genericsettings.isNoisy = True elif o == "--noise-free": genericsettings.isNoiseFree = True # The next 4 are for testing purpose elif o == "--tab-only": genericsettings.isFig = False genericsettings.isRLDistr = False genericsettings.isLogLoss = False elif o == "--fig-only": genericsettings.isTab = False genericsettings.isRLDistr = False genericsettings.isLogLoss = False elif o == "--rld-only": genericsettings.isTab = False genericsettings.isFig = False genericsettings.isLogLoss = False elif o == "--los-only": genericsettings.isTab = False genericsettings.isFig = False genericsettings.isRLDistr = False elif o == "--crafting-effort": try: genericsettings.inputCrE = float(a) except ValueError: raise Usage('Expect a valid float for flag crafting-effort.') elif o == "--settings": genericsettings.inputsettings = a elif o == "--conv": genericsettings.isConv = True elif o == "--rld-single-fcts": genericsettings.isRldOnSingleFcts = True elif o == "--runlength-based": genericsettings.runlength_based_targets = True elif o == "--expensive": genericsettings.isExpensive = True # comprises runlength-based elif o == "--not-expensive": genericsettings.isExpensive = False elif o == "--svg": genericsettings.generate_svg_files = True elif o == "--sca-only": warnings.warn("option --sca-only will have no effect with rungeneric1.py") else: assert False, "unhandled option" # from bbob_pproc import bbob2010 as inset # input settings if genericsettings.inputsettings == "color": from bbob_pproc import genericsettings as inset # input settings elif genericsettings.inputsettings == "grayscale": from bbob_pproc import grayscalesettings as inset # input settings elif genericsettings.inputsettings == "black-white": from bbob_pproc import bwsettings as inset # input settings else: txt = ('Settings: %s is not an appropriate ' % genericsettings.inputsettings + 'argument for input flag "--settings".') raise Usage(txt) if 11 < 3: from bbob_pproc import config # input settings config.config() import imp # import testbedsettings as testbedsettings # input settings try: fp, pathname, description = imp.find_module("testbedsettings") testbedsettings = imp.load_module("testbedsettings", fp, pathname, description) finally: fp.close() if (not genericsettings.verbose): warnings.simplefilter('module') # warnings.simplefilter('ignore') #get directory name if outputdir is a archive file algfolder = findfiles.get_output_directory_subfolder(args[0]) outputdir = os.path.join(outputdir, algfolder) print ("Post-processing (1): will generate output " + "data in folder %s" % outputdir) print " this might take several minutes." filelist = list() for i in args: i = i.strip() if os.path.isdir(i): filelist.extend(findfiles.main(i, genericsettings.verbose)) elif os.path.isfile(i): filelist.append(i) else: txt = 'Input file or folder %s could not be found.' % i print txt raise Usage(txt) dsList = DataSetList(filelist, genericsettings.verbose) if not dsList: raise Usage("Nothing to do: post-processing stopped.") if genericsettings.isNoisy and not genericsettings.isNoiseFree: dsList = dsList.dictByNoise().get('nzall', DataSetList()) if genericsettings.isNoiseFree and not genericsettings.isNoisy: dsList = dsList.dictByNoise().get('noiselessall', DataSetList()) # compute maxfuneval values dict_max_fun_evals = {} for ds in dsList: dict_max_fun_evals[ds.dim] = np.max((dict_max_fun_evals.setdefault(ds.dim, 0), float(np.max(ds.maxevals)))) from bbob_pproc import config config.target_values(genericsettings.isExpensive, dict_max_fun_evals) config.config() if (genericsettings.verbose): for i in dsList: if (dict((j, i.instancenumbers.count(j)) for j in set(i.instancenumbers)) != inset.instancesOfInterest): warnings.warn('The data of %s do not list ' % (i) + 'the correct instances ' + 'of function F%d.' % (i.funcId)) dictAlg = dsList.dictByAlg() if len(dictAlg) > 1: warnings.warn('Data with multiple algId %s ' % str(dictAlg.keys()) + 'will be processed together.') # TODO: in this case, all is well as long as for a given problem # (given dimension and function) there is a single instance of # DataSet associated. If there are more than one, the first one only # will be considered... which is probably not what one would expect. # TODO: put some errors where this case would be a problem. # raise Usage? if genericsettings.isFig or genericsettings.isTab or genericsettings.isRLDistr or genericsettings.isLogLoss: if not os.path.exists(outputdir): os.makedirs(outputdir) if genericsettings.verbose: print 'Folder %s was created.' % (outputdir) if genericsettings.isPickled: dsList.pickle(verbose=genericsettings.verbose) if genericsettings.isConv: ppconverrorbars.main(dictAlg, outputdir, genericsettings.verbose) if genericsettings.isFig: print "Scaling figures...", sys.stdout.flush() # ERT/dim vs dim. plt.rc("axes", **inset.rcaxeslarger) plt.rc("xtick", **inset.rcticklarger) plt.rc("ytick", **inset.rcticklarger) plt.rc("font", **inset.rcfontlarger) plt.rc("legend", **inset.rclegendlarger) plt.rc('pdf', fonttype = 42) ppfigdim.main(dsList, ppfigdim.values_of_interest, outputdir, genericsettings.verbose) plt.rcdefaults() print_done() plt.rc("axes", **inset.rcaxes) plt.rc("xtick", **inset.rctick) plt.rc("ytick", **inset.rctick) plt.rc("font", **inset.rcfont) plt.rc("legend", **inset.rclegend) plt.rc('pdf', fonttype = 42) if genericsettings.isTab: print "TeX tables...", sys.stdout.flush() dictNoise = dsList.dictByNoise() for noise, sliceNoise in dictNoise.iteritems(): pptable.main(sliceNoise, inset.tabDimsOfInterest, outputdir, noise, genericsettings.verbose) print_done() if genericsettings.isRLDistr: print "ECDF graphs...", sys.stdout.flush() dictNoise = dsList.dictByNoise() if len(dictNoise) > 1: warnings.warn('Data for functions from both the noisy and ' 'non-noisy testbeds have been found. Their ' 'results will be mixed in the "all functions" ' 'ECDF figures.') dictDim = dsList.dictByDim() for dim in inset.rldDimsOfInterest: try: sliceDim = dictDim[dim] except KeyError: continue pprldistr.main(sliceDim, True, outputdir, 'all', genericsettings.verbose) dictNoise = sliceDim.dictByNoise() for noise, sliceNoise in dictNoise.iteritems(): pprldistr.main(sliceNoise, True, outputdir, '%s' % noise, genericsettings.verbose) dictFG = sliceDim.dictByFuncGroup() for fGroup, sliceFuncGroup in dictFG.items(): pprldistr.main(sliceFuncGroup, True, outputdir, '%s' % fGroup, genericsettings.verbose) pprldistr.fmax = None # Resetting the max final value pprldistr.evalfmax = None # Resetting the max #fevalsfactor if genericsettings.isRldOnSingleFcts: # copy-paste from above, here for each function instead of function groups # ECDFs for each function pprldmany.all_single_functions(dictAlg, None, outputdir, genericsettings.verbose) print_done() if genericsettings.isLogLoss: print "ERT loss ratio figures and tables...", sys.stdout.flush() for ng, sliceNoise in dsList.dictByNoise().iteritems(): if ng == 'noiselessall': testbed = 'noiseless' elif ng == 'nzall': testbed = 'noisy' txt = ("Please input crafting effort value " + "for %s testbed:\n CrE = " % testbed) CrE = genericsettings.inputCrE while CrE is None: try: CrE = float(raw_input(txt)) except (SyntaxError, NameError, ValueError): print "Float value required." dictDim = sliceNoise.dictByDim() for d in inset.rldDimsOfInterest: try: sliceDim = dictDim[d] except KeyError: continue info = '%s' % ng pplogloss.main(sliceDim, CrE, True, outputdir, info, verbose=genericsettings.verbose) pplogloss.generateTable(sliceDim, CrE, outputdir, info, verbose=genericsettings.verbose) for fGroup, sliceFuncGroup in sliceDim.dictByFuncGroup().iteritems(): info = '%s' % fGroup pplogloss.main(sliceFuncGroup, CrE, True, outputdir, info, verbose=genericsettings.verbose) pplogloss.evalfmax = None # Resetting the max #fevalsfactor print_done() latex_commands_file = os.path.join(outputdir.split(os.sep)[0], 'bbob_pproc_commands.tex') html_file = os.path.join(outputdir, genericsettings.single_algorithm_file_name + '.html') prepend_to_file(latex_commands_file, ['\\providecommand{\\bbobloglosstablecaption}[1]{', pplogloss.table_caption, '}']) prepend_to_file(latex_commands_file, ['\\providecommand{\\bbobloglossfigurecaption}[1]{', pplogloss.figure_caption, '}']) prepend_to_file(latex_commands_file, ['\\providecommand{\\bbobpprldistrlegend}[1]{', pprldistr.caption_single(np.max([ val / dim for dim, val in dict_max_fun_evals.iteritems()])), # depends on the config setting, should depend on maxfevals '}']) replace_in_file(html_file, r'TOBEREPLACED', 'D, '.join([str(i) for i in pprldistr.single_runlength_factors[:6]]) + 'D,…') prepend_to_file(latex_commands_file, ['\\providecommand{\\bbobppfigdimlegend}[1]{', ppfigdim.scaling_figure_caption(), '}']) prepend_to_file(latex_commands_file, ['\\providecommand{\\bbobpptablecaption}[1]{', pptable.table_caption, '}']) prepend_to_file(latex_commands_file, ['\\providecommand{\\algfolder}{' + algfolder + '/}']) prepend_to_file(latex_commands_file, ['\\providecommand{\\algname}{' + (str_to_latex(strip_pathname1(args[0])) if len(args) == 1 else str_to_latex(dsList[0].algId)) + '{}}']) if genericsettings.isFig or genericsettings.isTab or genericsettings.isRLDistr or genericsettings.isLogLoss: print "Output data written to folder %s" % outputdir plt.rcdefaults()