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:
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 = []
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']
for i in targetsOfInterest.labels():
curline.append(r'\multicolumn{2}{@{}c@{}}{%s}'
% i)
else:
curline = [r'$\Delta f_\mathrm{opt}$']
for t in targets:
curline.append(r'\multicolumn{2}{@{\,}X@{\,}}{%s}'
% writeFEvals2(t, precision=1, isscientific=True))
# curline.append(r'\multicolumn{2}{@{\,}X@{}|}{%s}'
# % writeFEvals2(targets[-1], precision=1, isscientific=True))
curline.append(r'\multicolumn{2}{@{}l@{}}{\#succ}')
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]]
if isinstance(targetsOfInterest, pproc.RunlengthBasedTargetValues):
# write ftarget:fevals
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)))
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)))
else:
# write #fevals of the reference alg
for i in refalgert[:-1]:
curline.append(r'\multicolumn{2}{@{}c@{}}{%s \quad}'
% writeFEvalsMaxPrec(i, 2))
curline.append(r'\multicolumn{2}{@{}c@{}|}{%s}'
% writeFEvalsMaxPrec(refalgert[-1], 2))
# 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))
table.append(curline[:])
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):
#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_pathname2(alg)))
curline = [commandname + r'\hspace*{\fill}'] # each list element becomes a &-separated table entry?
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 = ''
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]))
str_significance_subsup = r"^{%s%s}" % (significance_vs_others_symbol, 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 '')
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))
continue
tmp = writeFEvalsMaxPrec(data, precfloat, maxfloatrepr=maxfloatrepr)
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)
else:
tmp = writeFEvalsMaxPrec(data, precscien, maxfloatrepr=data)
if isBold:
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
curline.append(r'\multicolumn{2}{%s}{%s%s}' % (alignment, tmp, str_significance_subsup))
else:
tmp2 = tmp.split('.', 1)
if len(tmp2) < 2:
tmp2.append('')
else:
tmp2[-1] = '.' + tmp2[-1]
if isBold:
tmp3 = []
for k in tmp2:
tmp3.append(r'\textbf{%s}' % k)
tmp2 = tmp3
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))
tmp2[-1] += str_significance_subsup
curline.extend(tmp2)
curline.append('%d' % algnbsucc[i])
curline.append('/%d' % algnbruns[i])
table.append(curline)
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)
if verbose:
print 'Wrote table in %s' % filename
except:
raise
else:
f.close()
def main(dictAlg, sortedAlgs=None, target=ftarget_default, outputdir='ppdata', verbose=True):
"""From a DataSetList, returns figures showing the scaling: ERT/dim vs dim.
One function and one target per figure.
``target`` can be a scalar, a list with one element or a
``pproc.TargetValues`` instance with one target.
``sortedAlgs`` is a list of string-identifies (folder names)
"""
# target becomes a TargetValues "list" with one element
target = pproc.TargetValues.cast([target] if numpy.isscalar(target) else target)
latex_commands_filename = os.path.join(outputdir, 'bbob_pproc_commands.tex')
assert isinstance(target, pproc.TargetValues)
if len(target) != 1:
raise ValueError('only a single target can be managed in ppfigs, ' + str(len(target)) + ' targets were given')
dictFunc = pproc.dictAlgByFun(dictAlg)
if sortedAlgs is None:
sortedAlgs = sorted(dictAlg.keys())
if not os.path.isdir(outputdir):
os.mkdir(outputdir)
for f in dictFunc:
filename = os.path.join(outputdir,'ppfigs_f%03d' % (f))
handles = []
fix_styles(len(sortedAlgs)) #
for i, alg in enumerate(sortedAlgs):
dictDim = dictFunc[f][alg].dictByDim() # this does not look like the most obvious solution
#Collect data
dimert = []
ert = []
dimnbsucc = []
ynbsucc = []
nbsucc = []
dimmaxevals = []
maxevals = []
dimmedian = []
medianfes = []
for dim in sorted(dictDim):
assert len(dictDim[dim]) == 1
entry = dictDim[dim][0]
data = generateData(entry, target((f, dim))[0]) # TODO: here we might want a different target for each function
if 1 < 3 or data[2] == 0: # No success
dimmaxevals.append(dim)
maxevals.append(float(data[3])/dim)
if data[2] > 0:
dimmedian.append(dim)
medianfes.append(data[4]/dim)
dimert.append(dim)
ert.append(float(data[0])/dim)
if data[1] < 1.:
dimnbsucc.append(dim)
ynbsucc.append(float(data[0])/dim)
nbsucc.append('%d' % data[2])
# Draw lines
tmp = plt.plot(dimert, ert, **styles[i]) #label=alg, )
plt.setp(tmp[0], markeredgecolor=plt.getp(tmp[0], 'color'))
# For legend
# tmp = plt.plot([], [], label=alg.replace('..' + os.sep, '').strip(os.sep), **styles[i])
tmp = plt.plot([], [], label=alg.split(os.sep)[-1], **styles[i])
plt.setp(tmp[0], markersize=12.,
markeredgecolor=plt.getp(tmp[0], 'color'))
if dimmaxevals:
tmp = plt.plot(dimmaxevals, maxevals, **styles[i])
plt.setp(tmp[0], markersize=20, #label=alg,
markeredgecolor=plt.getp(tmp[0], 'color'),
markeredgewidth=1,
markerfacecolor='None', linestyle='None')
handles.append(tmp)
#tmp2 = plt.plot(dimmedian, medianfes, ls='', marker='+',
# markersize=30, markeredgewidth=5,
# markeredgecolor=plt.getp(tmp, 'color'))[0]
#for i, n in enumerate(nbsucc):
# plt.text(dimnbsucc[i], numpy.array(ynbsucc[i])*1.85, n,
# verticalalignment='bottom',
# horizontalalignment='center')
if not bestalg.bestalgentries2009:
bestalg.loadBBOB2009()
bestalgdata = []
dimbestalg = list(df[0] for df in bestalg.bestalgentries2009 if df[1] == f)
dimbestalg.sort()
dimbestalg2 = []
for d in dimbestalg:
entry = bestalg.bestalgentries2009[(d, f)]
tmp = entry.detERT(target((f, d)))[0]
if numpy.isfinite(tmp):
bestalgdata.append(float(tmp)/d)
dimbestalg2.append(d)
tmp = plt.plot(dimbestalg2, bestalgdata, color=refcolor, linewidth=10,
marker='d', markersize=25, markeredgecolor=refcolor, zorder=-1
#label='best 2009',
)
handles.append(tmp)
if show_significance: # plot significance-stars
xstar, ystar = [], []
dims = sorted(pproc.dictAlgByDim(dictFunc[f]))
for i, dim in enumerate(dims):
datasets = pproc.dictAlgByDim(dictFunc[f])[dim]
assert all([len(datasets[ialg]) == 1 for ialg in sortedAlgs if datasets[ialg]])
dsetlist = [datasets[ialg][0] for ialg in sortedAlgs if datasets[ialg]]
if len(dsetlist) > 1:
arzp, arialg = toolsstats.significance_all_best_vs_other(dsetlist, target((f, dim)))
if arzp[0][1] * len(dims) < show_significance:
ert = dsetlist[arialg[0]].detERT(target((f, dim)))[0]
if ert < numpy.inf:
xstar.append(dim)
ystar.append(ert/dim)
plt.plot(xstar, ystar, 'k*', markerfacecolor=None, markeredgewidth=2, markersize=0.5*styles[0]['markersize'])
if funInfos:
plt.gca().set_title(funInfos[f])
isLegend = False
if legend:
plotLegend(handles)
elif 1 < 3:
if f in (1, 24, 101, 130) and len(sortedAlgs) < 6: # 6 elements at most in the boxed legend
isLegend = True
beautify(legend=isLegend, rightlegend=legend)
plt.text(plt.xlim()[0], plt.ylim()[0], 'target ' + target.label_name() + ': ' + target.label(0)) # TODO: check
saveFigure(filename, verbose=verbose)
plt.close()
# generate commands in tex file:
try:
abc = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
alg_definitions = []
for i in range(len(sortedAlgs)):
symb = r'{%s%s}' % (color_to_latex(styles[i]['color']),
marker_to_latex(styles[i]['marker']))
alg_definitions.append((', ' if i > 0 else '') + '%s:%s' % (symb, '\\algorithm' + abc[i % len(abc)]))
toolsdivers.prepend_to_file(latex_commands_filename,
[#'\\providecommand{\\bbobppfigsftarget}{\\ensuremath{10^{%s}}}'
# % target.loglabel(0), # int(numpy.round(numpy.log10(target))),
'\\providecommand{\\bbobppfigslegend}[1]{',
scaling_figure_caption(target),
'Legend: '] + alg_definitions + ['}']
)
toolsdivers.prepend_to_file(latex_commands_filename,
['\\providecommand{\\bbobECDFslegend}[1]{',
ecdfs_figure_caption(target), '}']
)
if verbose:
print 'Wrote commands and legend to %s' % filename
# this is obsolete (however check templates)
filename = os.path.join(outputdir,'ppfigs.tex')
f = open(filename, 'w')
f.write('% Do not modify this file: calls to post-processing software'
+ ' will overwrite any modification.\n')
f.write('Legend: ')
for i in range(0, len(sortedAlgs)):
symb = r'{%s%s}' % (color_to_latex(styles[i]['color']),
marker_to_latex(styles[i]['marker']))
f.write((', ' if i > 0 else '') + '%s:%s' % (symb, writeLabels(sortedAlgs[i])))
f.close()
if verbose:
print '(obsolete) Wrote legend in %s' % filename
except IOError:
raise
handles.append(tmp)
if funInfos:
plt.gca().set_title(funInfos[f])
beautify(rightlegend=legend)
if legend:
plotLegend(handles)
else:
if f in (1, 24, 101, 130):
plt.legend()
saveFigure(filename, figFormat=genericsettings.fig_formats, verbose=verbose)
plt.close()
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(dictAlg,
sortedAlgs,
targets,
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...
"""
# 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
# 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:
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)
e[succ == False] = entry.maxevals[succ == False]
ert = toolsstats.sp(e, issuccessful=succ)[0]
#tmpdata.append(ert/refalgert[i])
if succ.any():
tmp = toolsstats.drawSP(e[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 = []
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):
curline = [r'$\Delta f_\mathrm{opt}$']
for t in targets[0:-1]:
curline.append(r'\multicolumn{2}{@{\,}X@{\,}}{%s}' %
writeFEvals2(t, precision=1, isscientific=True))
curline.append(
r'\multicolumn{2}{@{\,}X@{}|}{%s}' %
writeFEvals2(targets[-1], precision=1, isscientific=True))
curline.append(r'\multicolumn{2}{@{}l@{}}{\#succ}')
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]]
for i in refalgert[0:-1]:
curline.append(r'\multicolumn{2}{@{\,}X@{\,}}{%s}' %
writeFEvalsMaxPrec(float(i), 2))
curline.append(r'\multicolumn{2}{@{\,}X@{\,}|}{%s}' %
writeFEvalsMaxPrec(float(refalgert[-1]), 2))
curline.append('%d' % refalgnbsucc)
if refalgnbsucc:
curline.append('/%d' % refalgnbruns)
#curline.append(curline[0])
table.append(curline)
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):
#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_pathname(alg)))
curline = [
commandname + r'\hspace*{\fill}'
] # each list element becomes a &-separated table entry?
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 = ''
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]))
str_significance_subsup = r"^{%s%s}" % (
significance_vs_others_symbol,
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[j]])
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 '')
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))
continue
tmp = writeFEvalsMaxPrec(data,
precfloat,
maxfloatrepr=maxfloatrepr)
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)
else:
tmp = writeFEvalsMaxPrec(data,
precscien,
maxfloatrepr=data)
if isBold:
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
curline.append(
r'\multicolumn{2}{%s}{%s%s}' %
(alignment, tmp, str_significance_subsup))
else:
tmp2 = tmp.split('.', 1)
if len(tmp2) < 2:
tmp2.append('')
else:
tmp2[-1] = '.' + tmp2[-1]
if isBold:
tmp3 = []
for k in tmp2:
tmp3.append(r'\textbf{%s}' % k)
tmp2 = tmp3
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))
tmp2[-1] += str_significance_subsup
curline.extend(tmp2)
curline.append('%d' % algnbsucc[i])
curline.append('/%d' % algnbruns[i])
table.append(curline)
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)
if verbose:
print 'Wrote table in %s' % filename
except:
raise
else:
f.close()
def main(dictAlg, sortedAlgs, target=1e-8, outputdir='ppdata', verbose=True):
"""From a DataSetList, returns figures showing the scaling: ERT/dim vs dim.
One function and one target per figure.
sortedAlgs is a list of string-identifies (folder names)
"""
dictFunc = pproc.dictAlgByFun(dictAlg)
for f in dictFunc:
filename = os.path.join(outputdir,'ppfigs_f%03d' % (f))
handles = []
fix_styles(len(sortedAlgs)) #
for i, alg in enumerate(sortedAlgs):
dictDim = dictFunc[f][alg].dictByDim()
#Collect data
dimert = []
ert = []
dimnbsucc = []
ynbsucc = []
nbsucc = []
dimmaxevals = []
maxevals = []
dimmedian = []
medianfes = []
for dim in sorted(dictDim):
assert len(dictDim[dim]) == 1
entry = dictDim[dim][0]
data = generateData(entry, target) # TODO: here we might want a different target for each function
if 1 < 3 or data[2] == 0: # No success
dimmaxevals.append(dim)
maxevals.append(float(data[3])/dim)
if data[2] > 0:
dimmedian.append(dim)
medianfes.append(data[4]/dim)
dimert.append(dim)
ert.append(float(data[0])/dim)
if data[1] < 1.:
dimnbsucc.append(dim)
ynbsucc.append(float(data[0])/dim)
nbsucc.append('%d' % data[2])
# Draw lines
tmp = plt.plot(dimert, ert, **styles[i]) #label=alg, )
plt.setp(tmp[0], markeredgecolor=plt.getp(tmp[0], 'color'))
# For legend
# tmp = plt.plot([], [], label=alg.replace('..' + os.sep, '').strip(os.sep), **styles[i])
tmp = plt.plot([], [], label=alg.split(os.sep)[-1], **styles[i])
plt.setp(tmp[0], markersize=12.,
markeredgecolor=plt.getp(tmp[0], 'color'))
if dimmaxevals:
tmp = plt.plot(dimmaxevals, maxevals, **styles[i])
plt.setp(tmp[0], markersize=20, #label=alg,
markeredgecolor=plt.getp(tmp[0], 'color'),
markeredgewidth=1,
markerfacecolor='None', linestyle='None')
handles.append(tmp)
#tmp2 = plt.plot(dimmedian, medianfes, ls='', marker='+',
# markersize=30, markeredgewidth=5,
# markeredgecolor=plt.getp(tmp, 'color'))[0]
#for i, n in enumerate(nbsucc):
# plt.text(dimnbsucc[i], numpy.array(ynbsucc[i])*1.85, n,
# verticalalignment='bottom',
# horizontalalignment='center')
if not bestalg.bestalgentries2009:
bestalg.loadBBOB2009()
bestalgdata = []
dimbestalg = list(df[0] for df in bestalg.bestalgentries2009 if df[1] == f)
dimbestalg.sort()
dimbestalg2 = []
for d in dimbestalg:
entry = bestalg.bestalgentries2009[(d, f)]
tmp = entry.detERT([target])[0]
if numpy.isfinite(tmp):
bestalgdata.append(float(tmp)/d)
dimbestalg2.append(d)
tmp = plt.plot(dimbestalg2, bestalgdata, color=refcolor, linewidth=10,
marker='d', markersize=25, markeredgecolor=refcolor, zorder=-1
#label='best 2009',
)
handles.append(tmp)
if show_significance: # plot significance-stars
xstar, ystar = [], []
dims = sorted(pproc.dictAlgByDim(dictFunc[f]))
for i, dim in enumerate(dims):
datasets = pproc.dictAlgByDim(dictFunc[f])[dim]
assert all([len(datasets[ialg]) == 1 for ialg in sortedAlgs if datasets[ialg]])
dsetlist = [datasets[ialg][0] for ialg in sortedAlgs if datasets[ialg]]
if len(dsetlist) > 1:
arzp, arialg = toolsstats.significance_all_best_vs_other(dsetlist, [target])
if arzp[0][1] * len(dims) < 0.05:
ert = dsetlist[arialg[0]].detERT([target])[0]
if ert < numpy.inf:
xstar.append(dim)
ystar.append(ert/dim)
plt.plot(xstar, ystar, 'k*', markerfacecolor=None, markeredgewidth=2, markersize=0.5*styles[0]['markersize'])
if funInfos:
plt.gca().set_title(funInfos[f])
isLegend = False
if legend:
plotLegend(handles)
elif 1 < 3:
if f in (1, 24, 101, 130) and len(sortedAlgs) < 6: # 6 elements at most in the boxed legend
isLegend = True
beautify(legend=isLegend, rightlegend=legend)
plt.text(plt.xlim()[0], plt.ylim()[0], 'ftarget=%.0e' % target)
saveFigure(filename, verbose=verbose)
plt.close()
# generate commands in tex file:
try:
abc = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
alg_definitions = []
for i in range(len(sortedAlgs)):
symb = r'{%s%s}' % (color_to_latex(styles[i]['color']),
marker_to_latex(styles[i]['marker']))
alg_definitions.append((', ' if i > 0 else '') + '%s:%s' % (symb, '\\algorithm' + abc[i % len(abc)]))
filename = os.path.join(outputdir, 'bbob_pproc_commands.tex')
toolsdivers.prepend_to_file(filename,
['\\providecommand{\\bbobppfigsftarget}{\\ensuremath{10^{%d}}}'
% int(numpy.round(numpy.log10(target))),
'\\providecommand{\\bbobppfigslegend}[1]{',
scaling_figure_legend,
'Legend: '] + alg_definitions + ['}']
)
if verbose:
print 'Wrote commands and legend to %s' % filename
# this is obsolete (however check templates)
filename = os.path.join(outputdir,'ppfigs.tex')
f = open(filename, 'w')
f.write('% Do not modify this file: calls to post-processing software'
+ ' will overwrite any modification.\n')
f.write('Legend: ')
for i in range(0, len(sortedAlgs)):
symb = r'{%s%s}' % (color_to_latex(styles[i]['color']),
marker_to_latex(styles[i]['marker']))
f.write((', ' if i > 0 else '') + '%s:%s' % (symb, writeLabels(sortedAlgs[i])))
f.close()
if verbose:
print '(obsolete) Wrote legend in %s' % filename
except IOError:
raise
handles.append(tmp)
if funInfos:
plt.gca().set_title(funInfos[f])
beautify(rightlegend=legend)
if legend:
plotLegend(handles)
else:
if f in (1, 24, 101, 130):
plt.legend()
saveFigure(filename, figFormat=genericsettings.fig_formats, verbose=verbose)
plt.close()
