def main(dsList0, dsList1, dimsOfInterest, outputdir, info='', verbose=True):
"""One table per dimension, modified to fit in 1 page per table."""
#TODO: method is long, split if possible
dictDim0 = dsList0.dictByDim()
dictDim1 = dsList1.dictByDim()
alg0 = set(i[0] for i in dsList0.dictByAlg().keys()).pop()[0:3]
alg1 = set(i[0] for i in dsList1.dictByAlg().keys()).pop()[0:3]
open(os.path.join(outputdir, 'bbob_pproc_commands.tex'), 'a'
).write(r'\providecommand{\algorithmAshort}{%s}' % writeLabels(alg0) + '\n' +
r'\providecommand{\algorithmBshort}{%s}' % writeLabels(alg1) + '\n')
if info:
info = '_' + info
dims = set.intersection(set(dictDim0.keys()), set(dictDim1.keys()))
if not bestalg.bestalgentries2009:
bestalg.loadBBOB2009()
header = [r'$\Delta f$']
for i in targetsOfInterest:
#header.append(r'\multicolumn{2}{@{}c@{}}{$10^{%d}$}' % (int(numpy.log10(i))))
header.append(r'\multicolumn{2}{@{}c@{}}{1e%+d}' % (int(numpy.log10(i))))
header.append(r'\multicolumn{2}{|@{}r@{}}{\#succ}')
for d in dimsOfInterest: # TODO set as input arguments
table = [header]
extraeol = [r'\hline']
try:
dictFunc0 = dictDim0[d].dictByFunc()
dictFunc1 = dictDim1[d].dictByFunc()
except KeyError:
continue
funcs = set.union(set(dictFunc0.keys()), set(dictFunc1.keys()))
nbtests = len(funcs) * 2. #len(dimsOfInterest)
for f in sorted(funcs):
bestalgentry = bestalg.bestalgentries2009[(d, f)]
curline = [r'${\bf f_{%d}}$' % f]
bestalgdata = bestalgentry.detERT(targetsOfInterest)
bestalgevals, bestalgalgs = bestalgentry.detEvals(targetsOfInterest)
for i in bestalgdata[:-1]:
curline.append(r'\multicolumn{2}{@{}c@{}}{%s}' % writeFEvalsMaxPrec(i, 2))
curline.append(r'\multicolumn{2}{@{}c@{}|}{%s}' % writeFEvalsMaxPrec(bestalgdata[-1], 2))
tmp = bestalgentry.detEvals([targetf])[0][0]
tmp2 = numpy.sum(numpy.isnan(tmp) == False)
curline.append('%d' % (tmp2))
if tmp2 > 0:
curline.append('/%d' % len(tmp))
table.append(curline[:])
extraeol.append('')
rankdata0 = [] # never used
# generate all data from ranksum test
entries = []
ertdata = {}
for nb, dsList in enumerate((dictFunc0, dictFunc1)):
try:
entry = dsList[f][0] # take the first DataSet, there should be only one?
except KeyError:
warnings.warn('data missing for data set ' + str(nb) + ' and function ' + str(f))
print('*** Warning: data missing for data set ' + str(nb) + ' and function ' + str(f) + '***')
continue # TODO: problem here!
ertdata[nb] = entry.detERT(targetsOfInterest)
entries.append(entry)
for _t in ertdata.values():
for _tt in _t:
if _tt is None:
raise ValueError
if len(entries) < 2: # funcion not available for *both* algorithms
continue # TODO: check which one is missing and make sure that what is there is displayed properly in the following
testres0vs1 = significancetest(entries[0], entries[1], targetsOfInterest)
testresbestvs1 = significancetest(bestalgentry, entries[1], targetsOfInterest)
testresbestvs0 = significancetest(bestalgentry, entries[0], targetsOfInterest)
for nb, entry in enumerate(entries):
if nb == 0:
curline = [r'1:\:\algorithmAshort\hspace*{\fill}']
else:
curline = [r'2:\:\algorithmBshort\hspace*{\fill}']
#data = entry.detERT(targetsOfInterest)
dispersion = []
data = []
evals = entry.detEvals(targetsOfInterest)
for i in evals:
succ = (numpy.isnan(i) == False)
tmp = i.copy()
tmp[succ==False] = entry.maxevals[numpy.isnan(i)]
#set_trace()
data.append(toolsstats.sp(tmp, issuccessful=succ)[0])
#if not any(succ):
#set_trace()
if any(succ):
tmp2 = toolsstats.drawSP(tmp[succ], tmp[succ==False],
(10, 50, 90), samplesize)[0]
dispersion.append((tmp2[-1]-tmp2[0])/2.)
else:
dispersion.append(None)
if nb == 0:
assert not isinstance(data, numpy.ndarray)
data0 = data[:] # TODO: check if it is not an array, it's never used anyway?
for i, dati in enumerate(data):
z, p = testres0vs1[i] # TODO: there is something with the sign that I don't get
# assign significance flag, which is the -log10(p)
significance0vs1 = 0
if nb != 0:
z = -z # the test is symmetric
if nbtests * p < 0.05 and z > 0:
significance0vs1 = -int(numpy.ceil(numpy.log10(min([1.0, nbtests * p])))) # this is the larger the more significant
isBold = significance0vs1 > 0
alignment = 'c'
if i == len(data) - 1: # last element
alignment = 'c|'
if numpy.isinf(bestalgdata[i]): # if the 2009 best did not solve the problem
tmp = writeFEvalsMaxPrec(float(dati), 2)
if not numpy.isinf(dati):
tmp = r'\textit{%s}' % (tmp)
if isBold:
tmp = r'\textbf{%s}' % tmp
if dispersion[i] and numpy.isfinite(dispersion[i]):
tmp += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(dispersion[i], 1)
tableentry = (r'\multicolumn{2}{@{}%s@{}}{%s}'
% (alignment, tmp))
else:
# Formatting
tmp = float(dati)/bestalgdata[i]
assert not numpy.isnan(tmp)
isscientific = False
if tmp >= 1000:
isscientific = True
tableentry = writeFEvals2(tmp, 2, isscientific=isscientific)
tableentry = writeFEvalsMaxPrec(tmp, 2)
if numpy.isinf(tmp) and i == len(data)-1:
tableentry = (tableentry
+ r'\textit{%s}' % writeFEvals2(numpy.median(entry.maxevals), 2))
if isBold:
tableentry = r'\textbf{%s}' % tableentry
elif 11 < 3 and significance0vs1 < 0: # cave: negative significance has no meaning anymore
tableentry = r'\textit{%s}' % tableentry
if dispersion[i] and numpy.isfinite(dispersion[i]/bestalgdata[i]):
tableentry += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 1)
tableentry = (r'\multicolumn{2}{@{}%s@{}}{%s}'
% (alignment, tableentry))
elif tableentry.find('e') > -1 or (numpy.isinf(tmp) and i != len(data) - 1):
if isBold:
tableentry = r'\textbf{%s}' % tableentry
elif 11 < 3 and significance0vs1 < 0:
tableentry = r'\textit{%s}' % tableentry
if dispersion[i] and numpy.isfinite(dispersion[i]/bestalgdata[i]):
tableentry += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 1)
tableentry = (r'\multicolumn{2}{@{}%s@{}}{%s}'
% (alignment, tableentry))
else:
tmp = tableentry.split('.', 1)
if isBold:
tmp = list(r'\textbf{%s}' % i for i in tmp)
elif 11 < 3 and significance0vs1 < 0:
tmp = list(r'\textit{%s}' % i for i in tmp)
tableentry = ' & .'.join(tmp)
if len(tmp) == 1:
tableentry += '&'
if dispersion[i] and numpy.isfinite(dispersion[i]/bestalgdata[i]):
tableentry += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 1)
superscript = ''
if nb == 0:
z, p = testresbestvs0[i]
else:
z, p = testresbestvs1[i]
#The conditions are now that ERT < ERT_best
if ((nbtests * p) < 0.05 and dati - bestalgdata[i] < 0.
and z < 0.):
nbstars = -numpy.ceil(numpy.log10(nbtests * p))
#tmp = '\hspace{-.5ex}'.join(nbstars * [r'\star'])
if z > 0:
superscript = r'\uparrow' #* nbstars
else:
superscript = r'\downarrow' #* nbstars
# print z, linebest[i], line1
if nbstars > 1:
superscript += str(int(nbstars))
if superscript or significance0vs1:
s = ''
if significance0vs1 > 0:
s = '\star'
if significance0vs1 > 1:
s += str(significance0vs1)
s = r'$^{' + s + superscript + r'}$'
if tableentry.endswith('}'):
tableentry = tableentry[:-1] + s + r'}'
else:
tableentry += s
curline.append(tableentry)
#curline.append(tableentry)
#if dispersion[i] is None or numpy.isinf(bestalgdata[i]):
#curline.append('')
#else:
#tmp = writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 2)
#curline.append('(%s)' % tmp)
tmp = entry.evals[entry.evals[:, 0] <= targetf, 1:]
try:
tmp = tmp[0]
curline.append('%d' % numpy.sum(numpy.isnan(tmp) == False))
except IndexError:
curline.append('%d' % 0)
curline.append('/%d' % entry.nbRuns())
table.append(curline[:])
extraeol.append('')
extraeol[-1] = r'\hline'
extraeol[-1] = ''
outputfile = os.path.join(outputdir, 'pptable2_%02dD%s.tex' % (d, info))
spec = r'@{}c@{}|' + '*{%d}{@{}r@{}@{}l@{}}' % len(targetsOfInterest) + '|@{}r@{}@{}l@{}'
res = r'\providecommand{\algorithmAshort}{%s}' % writeLabels(alg0) + '\n'
res += r'\providecommand{\algorithmBshort}{%s}' % writeLabels(alg1) + '\n'
# open(os.path.join(outputdir, 'bbob_pproc_commands.tex'), 'a').write(res)
#res += tableLaTeXStar(table, width=r'0.45\textwidth', spec=spec,
#extraeol=extraeol)
res += tableLaTeX(table, spec=spec, extraeol=extraeol)
f = open(outputfile, 'w')
f.write(res)
f.close()
if verbose:
print "Table written in %s" % outputfile
def main(dsList0, dsList1, dimsOfInterest, outputdir, info='', verbose=True):
"""One table per dimension, modified to fit in 1 page per table."""
#TODO: method is long, split if possible
dictDim0 = dsList0.dictByDim()
dictDim1 = dsList1.dictByDim()
alg0 = set(i[0] for i in dsList0.dictByAlg().keys()).pop()[0:3]
alg1 = set(i[0] for i in dsList1.dictByAlg().keys()).pop()[0:3]
open(os.path.join(outputdir, 'bbob_pproc_commands.tex'), 'a').write(
r'\providecommand{\algorithmAshort}{%s}' % writeLabels(alg0) + '\n' +
r'\providecommand{\algorithmBshort}{%s}' % writeLabels(alg1) + '\n')
if info:
info = '_' + info
dims = set.intersection(set(dictDim0.keys()), set(dictDim1.keys()))
if not bestalg.bestalgentries2009:
bestalg.loadBBOB2009()
header = []
if isinstance(targetsOfInterest, pproc.RunlengthBasedTargetValues):
header = [r'\#FEs/D']
for label in targetsOfInterest.labels():
header.append(r'\multicolumn{2}{@{}c@{}}{%s}' % label)
else:
header = [r'$\Delta f_\mathrm{opt}$']
for label in targetsOfInterest.labels():
header.append(r'\multicolumn{2}{@{\,}c@{\,}}{%s}' % label)
header.append(r'\multicolumn{2}{@{}l@{}}{\#succ}')
for d in dimsOfInterest: # TODO set as input arguments
table = [header]
extraeol = [r'\hline']
try:
dictFunc0 = dictDim0[d].dictByFunc()
dictFunc1 = dictDim1[d].dictByFunc()
except KeyError:
continue
funcs = set.union(set(dictFunc0.keys()), set(dictFunc1.keys()))
nbtests = len(funcs) * 2. #len(dimsOfInterest)
for f in sorted(funcs):
targets = targetsOfInterest((f, d))
targetf = targets[-1]
bestalgentry = bestalg.bestalgentries2009[(d, f)]
curline = [r'${\bf f_{%d}}$' % f]
bestalgdata = bestalgentry.detERT(targets)
bestalgevals, bestalgalgs = bestalgentry.detEvals(targets)
if isinstance(targetsOfInterest, pproc.RunlengthBasedTargetValues):
# write ftarget:fevals
for i in xrange(len(bestalgdata[:-1])):
temp = "%.1e" % targetsOfInterest((f, d))[i]
if temp[-2] == "0":
temp = temp[:-2] + temp[-1]
curline.append(
r'\multicolumn{2}{@{}c@{}}{\textit{%s}:%s \quad}' %
(temp, writeFEvalsMaxPrec(bestalgdata[i], 2)))
temp = "%.1e" % targetsOfInterest((f, d))[-1]
if temp[-2] == "0":
temp = temp[:-2] + temp[-1]
curline.append(r'\multicolumn{2}{@{}c@{}|}{\textit{%s}:%s }' %
(temp, writeFEvalsMaxPrec(bestalgdata[-1], 2)))
else:
# write #fevals of the reference alg
for i in bestalgdata[:-1]:
curline.append(r'\multicolumn{2}{@{}c@{}}{%s \quad}' %
writeFEvalsMaxPrec(i, 2))
curline.append(r'\multicolumn{2}{@{}c@{}|}{%s}' %
writeFEvalsMaxPrec(bestalgdata[-1], 2))
tmp = bestalgentry.detEvals([targetf])[0][0]
tmp2 = numpy.sum(numpy.isnan(tmp) == False)
curline.append('%d' % (tmp2))
if tmp2 > 0:
curline.append('/%d' % len(tmp))
table.append(curline[:])
extraeol.append('')
rankdata0 = [] # never used
# generate all data from ranksum test
entries = []
ertdata = {}
for nb, dsList in enumerate((dictFunc0, dictFunc1)):
try:
entry = dsList[f][
0] # take the first DataSet, there should be only one?
except KeyError:
warnings.warn('data missing for data set ' + str(nb) +
' and function ' + str(f))
print('*** Warning: data missing for data set ' + str(nb) +
' and function ' + str(f) + '***')
continue # TODO: problem here!
ertdata[nb] = entry.detERT(targets)
entries.append(entry)
for _t in ertdata.values():
for _tt in _t:
if _tt is None:
raise ValueError
if len(entries) < 2: # funcion not available for *both* algorithms
continue # TODO: check which one is missing and make sure that what is there is displayed properly in the following
testres0vs1 = significancetest(entries[0], entries[1], targets)
testresbestvs1 = significancetest(bestalgentry, entries[1],
targets)
testresbestvs0 = significancetest(bestalgentry, entries[0],
targets)
for nb, entry in enumerate(entries):
if nb == 0:
curline = [r'1:\:\algorithmAshort\hspace*{\fill}']
else:
curline = [r'2:\:\algorithmBshort\hspace*{\fill}']
#data = entry.detERT(targetsOfInterest)
dispersion = []
data = []
evals = entry.detEvals(targets)
for i in evals:
succ = (numpy.isnan(i) == False)
tmp = i.copy()
tmp[succ == False] = entry.maxevals[numpy.isnan(i)]
#set_trace()
data.append(toolsstats.sp(tmp, issuccessful=succ)[0])
#if not any(succ):
#set_trace()
if any(succ):
tmp2 = toolsstats.drawSP(tmp[succ], tmp[succ == False],
(10, 50, 90), samplesize)[0]
dispersion.append((tmp2[-1] - tmp2[0]) / 2.)
else:
dispersion.append(None)
if nb == 0:
assert not isinstance(data, numpy.ndarray)
data0 = data[:] # TODO: check if it is not an array, it's never used anyway?
for i, dati in enumerate(data):
z, p = testres0vs1[
i] # TODO: there is something with the sign that I don't get
# assign significance flag, which is the -log10(p)
significance0vs1 = 0
if nb != 0:
z = -z # the test is symmetric
if nbtests * p < 0.05 and z > 0:
significance0vs1 = -int(
numpy.ceil(numpy.log10(min([
1.0, nbtests * p
])))) # this is the larger the more significant
isBold = significance0vs1 > 0
alignment = 'c'
if i == len(data) - 1: # last element
alignment = 'c|'
if numpy.isinf(
bestalgdata[i]
): # if the 2009 best did not solve the problem
tmp = writeFEvalsMaxPrec(float(dati), 2)
if not numpy.isinf(dati):
tmp = r'\textit{%s}' % (tmp)
if isBold:
tmp = r'\textbf{%s}' % tmp
if dispersion[i] and numpy.isfinite(dispersion[i]):
tmp += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(
dispersion[i], 1)
tableentry = (r'\multicolumn{2}{@{}%s@{}}{%s}' %
(alignment, tmp))
else:
# Formatting
tmp = float(dati) / bestalgdata[i]
assert not numpy.isnan(tmp)
isscientific = False
if tmp >= 1000:
isscientific = True
tableentry = writeFEvals2(tmp,
2,
isscientific=isscientific)
tableentry = writeFEvalsMaxPrec(tmp, 2)
if numpy.isinf(tmp) and i == len(data) - 1:
tableentry = (
tableentry + r'\textit{%s}' %
writeFEvals2(numpy.median(entry.maxevals), 2))
if isBold:
tableentry = r'\textbf{%s}' % tableentry
elif 11 < 3 and significance0vs1 < 0: # cave: negative significance has no meaning anymore
tableentry = r'\textit{%s}' % tableentry
if dispersion[i] and numpy.isfinite(
dispersion[i] / bestalgdata[i]):
tableentry += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(
dispersion[i] / bestalgdata[i], 1)
tableentry = (r'\multicolumn{2}{@{}%s@{}}{%s}' %
(alignment, tableentry))
elif tableentry.find('e') > -1 or (numpy.isinf(tmp) and
i != len(data) - 1):
if isBold:
tableentry = r'\textbf{%s}' % tableentry
elif 11 < 3 and significance0vs1 < 0:
tableentry = r'\textit{%s}' % tableentry
if dispersion[i] and numpy.isfinite(
dispersion[i] / bestalgdata[i]):
tableentry += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(
dispersion[i] / bestalgdata[i], 1)
tableentry = (r'\multicolumn{2}{@{}%s@{}}{%s}' %
(alignment, tableentry))
else:
tmp = tableentry.split('.', 1)
if isBold:
tmp = list(r'\textbf{%s}' % i for i in tmp)
elif 11 < 3 and significance0vs1 < 0:
tmp = list(r'\textit{%s}' % i for i in tmp)
tableentry = ' & .'.join(tmp)
if len(tmp) == 1:
tableentry += '&'
if dispersion[i] and numpy.isfinite(
dispersion[i] / bestalgdata[i]):
tableentry += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(
dispersion[i] / bestalgdata[i], 1)
superscript = ''
if nb == 0:
z, p = testresbestvs0[i]
else:
z, p = testresbestvs1[i]
#The conditions are now that ERT < ERT_best
if ((nbtests * p) < 0.05 and dati - bestalgdata[i] < 0.
and z < 0.):
nbstars = -numpy.ceil(numpy.log10(nbtests * p))
#tmp = '\hspace{-.5ex}'.join(nbstars * [r'\star'])
if z > 0:
superscript = r'\uparrow' #* nbstars
else:
superscript = r'\downarrow' #* nbstars
# print z, linebest[i], line1
if nbstars > 1:
superscript += str(int(nbstars))
if superscript or significance0vs1:
s = ''
if significance0vs1 > 0:
s = '\star'
if significance0vs1 > 1:
s += str(significance0vs1)
s = r'$^{' + s + superscript + r'}$'
if tableentry.endswith('}'):
tableentry = tableentry[:-1] + s + r'}'
else:
tableentry += s
curline.append(tableentry)
#curline.append(tableentry)
#if dispersion[i] is None or numpy.isinf(bestalgdata[i]):
#curline.append('')
#else:
#tmp = writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 2)
#curline.append('(%s)' % tmp)
tmp = entry.evals[entry.evals[:, 0] <= targetf, 1:]
try:
tmp = tmp[0]
curline.append('%d' % numpy.sum(numpy.isnan(tmp) == False))
except IndexError:
curline.append('%d' % 0)
curline.append('/%d' % entry.nbRuns())
table.append(curline[:])
extraeol.append('')
extraeol[-1] = r'\hline'
extraeol[-1] = ''
outputfile = os.path.join(outputdir,
'pptable2_%02dD%s.tex' % (d, info))
spec = r'@{}c@{}|' + '*{%d}{@{}r@{}@{}l@{}}' % len(
targetsOfInterest) + '|@{}r@{}@{}l@{}'
res = r'\providecommand{\algorithmAshort}{%s}' % writeLabels(
alg0) + '\n'
res += r'\providecommand{\algorithmBshort}{%s}' % writeLabels(
alg1) + '\n'
# open(os.path.join(outputdir, 'bbob_pproc_commands.tex'), 'a').write(res)
#res += tableLaTeXStar(table, width=r'0.45\textwidth', spec=spec,
#extraeol=extraeol)
res += tableLaTeX(table, spec=spec, extraeol=extraeol)
f = open(outputfile, 'w')
f.write(res)
f.close()
if verbose:
print "Table written in %s" % outputfile
def main(dsList0, dsList1, dimsOfInterest, outputdir, info='', verbose=True):
"""One table per dimension, modified to fit in 1 page per table."""
#TODO: method is long, split if possible
dictDim0 = dsList0.dictByDim()
dictDim1 = dsList1.dictByDim()
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]
open(os.path.join(outputdir, 'bbob_pproc_commands.tex'), 'a'
).write(r'\providecommand{\algorithmAshort}{%s}' % writeLabels(alg0) + '\n' +
r'\providecommand{\algorithmBshort}{%s}' % writeLabels(alg1) + '\n')
if info:
info = '_' + info
dims = set.intersection(set(dictDim0.keys()), set(dictDim1.keys()))
bestalgentries = bestalg.loadBestAlgorithm(dsList0.isBiobjective())
header = []
if isinstance(targetsOfInterest, pproc.RunlengthBasedTargetValues):
header = [r'\#FEs/D']
headerHtml = ['<thead>\n<tr>\n<th>#FEs/D</th>\n']
for label in targetsOfInterest.labels():
header.append(r'\multicolumn{2}{@{}c@{}}{%s}' % label)
headerHtml.append('<td>%s</td>\n' % label)
else:
header = [r'$\Delta f_\mathrm{opt}$']
headerHtml = ['<thead>\n<tr>\n<th>Δ f</th>\n']
for label in targetsOfInterest.labels():
header.append(r'\multicolumn{2}{@{\,}c@{\,}}{%s}' % label)
headerHtml.append('<td>%s</td>\n' % label)
header.append(r'\multicolumn{2}{@{}l@{}}{\#succ}')
headerHtml.append('<td>#succ</td>\n</tr>\n</thead>\n')
for d in dimsOfInterest: # TODO set as input arguments
table = [header]
tableHtml = headerHtml
extraeol = [r'\hline']
try:
dictFunc0 = dictDim0[d].dictByFunc()
dictFunc1 = dictDim1[d].dictByFunc()
except KeyError:
continue
funcs = set.union(set(dictFunc0.keys()), set(dictFunc1.keys()))
nbtests = len(funcs) * 2. #len(dimsOfInterest)
tableHtml.append('<tbody>\n')
for f in sorted(funcs):
tableHtml.append('<tr>\n')
targets = targetsOfInterest((f, d))
targetf = targets[-1]
bestalgentry = bestalgentries[(d, f)]
curline = [r'${\bf f_{%d}}$' % f]
curlineHtml = ['<th><b>f<sub>%d</sub></b></th>\n' % f]
bestalgdata = bestalgentry.detERT(targets)
bestalgevals, bestalgalgs = bestalgentry.detEvals(targets)
if isinstance(targetsOfInterest, pproc.RunlengthBasedTargetValues):
# write ftarget:fevals
for i in xrange(len(bestalgdata[:-1])):
temp = "%.1e" % targetsOfInterest((f, d))[i]
if temp[-2]=="0":
temp = temp[:-2]+temp[-1]
curline.append(r'\multicolumn{2}{@{}c@{}}{\textit{%s}:%s \quad}'
% (temp,writeFEvalsMaxPrec(bestalgdata[i], 2)))
curlineHtml.append('<td><i>%s</i>:%s</td>\n'
% (temp, writeFEvalsMaxPrec(bestalgdata[i], 2)))
temp = "%.1e" % targetsOfInterest((f, d))[-1]
if temp[-2]=="0":
temp = temp[:-2]+temp[-1]
curline.append(r'\multicolumn{2}{@{}c@{}|}{\textit{%s}:%s }'
% (temp,writeFEvalsMaxPrec(bestalgdata[-1], 2)))
curlineHtml.append('<td><i>%s</i>:%s</td>\n'
% (temp, writeFEvalsMaxPrec(bestalgdata[-1], 2)))
else:
# write #fevals of the reference alg
for i in bestalgdata[:-1]:
curline.append(r'\multicolumn{2}{@{}c@{}}{%s \quad}'
% writeFEvalsMaxPrec(i, 2))
curlineHtml.append('<td>%s</td>\n' % writeFEvalsMaxPrec(i, 2))
curline.append(r'\multicolumn{2}{@{}c@{}|}{%s}'
% writeFEvalsMaxPrec(bestalgdata[-1], 2))
curlineHtml.append('<td>%s</td>\n' % writeFEvalsMaxPrec(bestalgdata[-1], 2))
tmp = bestalgentry.detEvals([targetf])[0][0]
tmp2 = numpy.sum(numpy.isnan(tmp) == False)
curline.append('%d' % (tmp2))
if tmp2 > 0:
curline.append('/%d' % len(tmp))
curlineHtml.append('<td>%d/%d</td>\n' % (tmp2, len(tmp)))
else:
curlineHtml.append('<td>%d</td>\n' % (tmp2))
table.append(curline[:])
tableHtml.extend(curlineHtml[:])
tableHtml.append('</tr>\n')
extraeol.append('')
rankdata0 = [] # never used
# generate all data from ranksum test
entries = []
ertdata = {}
for nb, dsList in enumerate((dictFunc0, dictFunc1)):
try:
entry = dsList[f][0] # take the first DataSet, there should be only one?
except KeyError:
warnings.warn('data missing for data set ' + str(nb) + ' and function ' + str(f))
print('*** Warning: data missing for data set ' + str(nb) + ' and function ' + str(f) + '***')
continue # TODO: problem here!
ertdata[nb] = entry.detERT(targets)
entries.append(entry)
for _t in ertdata.values():
for _tt in _t:
if _tt is None:
raise ValueError
if len(entries) < 2: # funcion not available for *both* algorithms
continue # TODO: check which one is missing and make sure that what is there is displayed properly in the following
testres0vs1 = significancetest(entries[0], entries[1], targets)
testresbestvs1 = significancetest(bestalgentry, entries[1], targets)
testresbestvs0 = significancetest(bestalgentry, entries[0], targets)
for nb, entry in enumerate(entries):
tableHtml.append('<tr>\n')
if nb == 0:
curline = [r'1:\:\algorithmAshort\hspace*{\fill}']
curlineHtml = ['<th>1: %s</th>\n' % alg0]
else:
curline = [r'2:\:\algorithmBshort\hspace*{\fill}']
curlineHtml = ['<th>2: %s</th>\n' % alg1]
#data = entry.detERT(targetsOfInterest)
dispersion = []
data = []
evals = entry.detEvals(targets)
for i in evals:
succ = (numpy.isnan(i) == False)
tmp = i.copy()
tmp[succ==False] = entry.maxevals[numpy.isnan(i)]
#set_trace()
data.append(toolsstats.sp(tmp, issuccessful=succ)[0])
#if not any(succ):
#set_trace()
if any(succ):
tmp2 = toolsstats.drawSP(tmp[succ], tmp[succ==False],
(10, 50, 90), samplesize)[0]
dispersion.append((tmp2[-1]-tmp2[0])/2.)
else:
dispersion.append(None)
if nb == 0:
assert not isinstance(data, numpy.ndarray)
data0 = data[:] # TODO: check if it is not an array, it's never used anyway?
for i, dati in enumerate(data):
z, p = testres0vs1[i] # TODO: there is something with the sign that I don't get
# assign significance flag, which is the -log10(p)
significance0vs1 = 0
if nb != 0:
z = -z # the test is symmetric
if nbtests * p < 0.05 and z > 0:
significance0vs1 = -int(numpy.ceil(numpy.log10(min([1.0, nbtests * p])))) # this is the larger the more significant
isBold = significance0vs1 > 0
alignment = 'c'
if i == len(data) - 1: # last element
alignment = 'c|'
if numpy.isinf(bestalgdata[i]): # if the 2009 best did not solve the problem
tmp = writeFEvalsMaxPrec(float(dati), 2)
if not numpy.isinf(dati):
tmpHtml = '<i>%s</i>' % (tmp)
tmp = r'\textit{%s}' % (tmp)
if isBold:
tmp = r'\textbf{%s}' % tmp
tmpHtml = '<b>%s</b>' % tmpHtml
if dispersion[i] and numpy.isfinite(dispersion[i]):
tmp += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(dispersion[i], 1)
tableentry = (r'\multicolumn{2}{@{}%s@{}}{%s}'
% (alignment, tmp))
tableentryHtml = (' (%s)' % tmp)
else:
# Formatting
tmp = float(dati)/bestalgdata[i]
assert not numpy.isnan(tmp)
isscientific = False
if tmp >= 1000:
isscientific = True
tableentry = writeFEvals2(tmp, 2, isscientific=isscientific)
tableentry = writeFEvalsMaxPrec(tmp, 2)
tableentryHtml = writeFEvalsMaxPrec(tmp, 2)
if numpy.isinf(tmp) and i == len(data)-1:
tableentry = (tableentry
+ r'\textit{%s}' % writeFEvals2(numpy.median(entry.maxevals), 2))
tableentryHtml = (tableentryHtml
+ ' <i>%s</i>' % writeFEvals2(numpy.median(entry.maxevals), 2))
if isBold:
tableentry = r'\textbf{%s}' % tableentry
tableentryHtml = '<b>%s</b>' % tableentryHtml
elif 11 < 3 and significance0vs1 < 0: # cave: negative significance has no meaning anymore
tableentry = r'\textit{%s}' % tableentry
tableentryHtml = '<i>%s</i>' % tableentryHtml
if dispersion[i] and numpy.isfinite(dispersion[i]/bestalgdata[i]):
tableentry += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 1)
tableentryHtml += ' (%s)' % writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 1)
tableentry = (r'\multicolumn{2}{@{}%s@{}}{%s}'
% (alignment, tableentry))
elif tableentry.find('e') > -1 or (numpy.isinf(tmp) and i != len(data) - 1):
if isBold:
tableentry = r'\textbf{%s}' % tableentry
tableentryHtml = '<b>%s</b>' % tableentryHtml
elif 11 < 3 and significance0vs1 < 0:
tableentry = r'\textit{%s}' % tableentry
tableentryHtml = '<i>%s</i>' % tableentryHtml
if dispersion[i] and numpy.isfinite(dispersion[i]/bestalgdata[i]):
tableentry += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 1)
tableentryHtml += ' (%s)' % writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 1)
tableentry = (r'\multicolumn{2}{@{}%s@{}}{%s}'
% (alignment, tableentry))
else:
tmp = tableentry.split('.', 1)
tmpHtml = tableentryHtml.split('.', 1)
if isBold:
tmp = list(r'\textbf{%s}' % i for i in tmp)
tmpHtml = list('<b>%s</b>' % i for i in tmpHtml)
elif 11 < 3 and significance0vs1 < 0:
tmp = list(r'\textit{%s}' % i for i in tmp)
tmpHtml = list('<i>%s</i>' % i for i in tmpHtml)
tableentry = ' & .'.join(tmp)
tableentryHtml = '.'.join(tmpHtml)
if len(tmp) == 1:
tableentry += '&'
if dispersion[i] and numpy.isfinite(dispersion[i]/bestalgdata[i]):
tableentry += r'${\scriptscriptstyle (%s)}$' % writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 1)
tableentryHtml += ' (%s)' % writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 1)
superscript = ''
superscriptHtml = ''
if nb == 0:
z, p = testresbestvs0[i]
else:
z, p = testresbestvs1[i]
#The conditions are now that ERT < ERT_best
if ((nbtests * p) < 0.05 and dati - bestalgdata[i] < 0.
and z < 0.):
nbstars = -numpy.ceil(numpy.log10(nbtests * p))
#tmp = '\hspace{-.5ex}'.join(nbstars * [r'\star'])
if z > 0:
superscript = r'\uparrow' #* nbstars
superscriptHtml = '↑'
else:
superscript = r'\downarrow' #* nbstars
superscriptHtml = '↓'
# print z, linebest[i], line1
if nbstars > 1:
superscript += str(int(nbstars))
superscriptHtml += str(int(nbstars))
if superscript or significance0vs1:
s = ''
shtml = ''
if significance0vs1 > 0:
s = '\star'
shtml = '★'
if significance0vs1 > 1:
s += str(significance0vs1)
shtml += str(significance0vs1)
s = r'$^{' + s + superscript + r'}$'
shtml = '<sup>' + shtml + superscriptHtml + '</sup>'
if tableentry.endswith('}'):
tableentry = tableentry[:-1] + s + r'}'
else:
tableentry += s
tableentryHtml += shtml
tableentryHtml = tableentryHtml.replace('$\infty$', '∞')
curlineHtml.append('<td>%s</td>\n' % tableentryHtml)
curline.append(tableentry)
#curline.append(tableentry)
#if dispersion[i] is None or numpy.isinf(bestalgdata[i]):
#curline.append('')
#else:
#tmp = writeFEvalsMaxPrec(dispersion[i]/bestalgdata[i], 2)
#curline.append('(%s)' % tmp)
tmp = entry.evals[entry.evals[:, 0] <= targetf, 1:]
try:
tmp = tmp[0]
curline.append('%d' % numpy.sum(numpy.isnan(tmp) == False))
curlineHtml.append('<td>%d' % numpy.sum(numpy.isnan(tmp) == False))
except IndexError:
curline.append('%d' % 0)
curlineHtml.append('<td>%d' % 0)
curline.append('/%d' % entry.nbRuns())
curlineHtml.append('/%d</td>\n' % entry.nbRuns())
table.append(curline[:])
tableHtml.extend(curlineHtml[:])
tableHtml.append('</tr>\n')
extraeol.append('')
extraeol[-1] = r'\hline'
extraeol[-1] = ''
outputfile = os.path.join(outputdir, 'pptable2_%02dD%s.tex' % (d, info))
spec = r'@{}c@{}|' + '*{%d}{@{}r@{}@{}l@{}}' % len(targetsOfInterest) + '|@{}r@{}@{}l@{}'
res = r'\providecommand{\algorithmAshort}{%s}' % writeLabels(alg0) + '\n'
res += r'\providecommand{\algorithmBshort}{%s}' % writeLabels(alg1) + '\n'
# open(os.path.join(outputdir, 'bbob_pproc_commands.tex'), 'a').write(res)
#res += tableLaTeXStar(table, width=r'0.45\textwidth', spec=spec,
#extraeol=extraeol)
res += tableLaTeX(table, spec=spec, extraeol=extraeol)
f = open(outputfile, 'w')
f.write(res)
f.close()
res = ("").join(str(item) for item in tableHtml)
res = '<p><b>%d-D</b></p>\n<table>\n%s</table>\n' % (d, res)
filename = os.path.join(outputdir, genericsettings.two_algorithm_file_name + '.html')
lines = []
with open(filename) as infile:
for line in infile:
if '<!--pptable2Html-->' in line:
lines.append(res)
lines.append(line)
with open(filename, 'w') as outfile:
for line in lines:
outfile.write(line)
if verbose:
print "Table written in %s" % outputfile
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, 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()
