def plotECDF(x, n=None, **plotArgs):
"""Plot an empirical cumulative distribution function.
:param seq x: data
:param int n: number of samples, if not provided len(x) is used
:param plotArgs: optional keyword arguments provided to plot.
:returns: handles of the plot elements.
"""
if n is None:
n = len(x)
nx = len(x)
if n == 0 or nx == 0:
res = plt.plot([], [], **plotArgs)
else:
x = sorted(x) # do not sort in place
x = np.hstack((x, x[-1]))
y = np.hstack((np.arange(0., nx) / n, float(nx) / n))
res = plotUnifLogXMarkers(x,
y,
nbperdecade=nbperdecade,
drawstyle='steps',
**plotArgs)
return res
def plotECDF(x, n=None, **plotArgs):
"""Plot an empirical cumulative distribution function.
:param seq x: data
:param int n: number of samples, if not provided len(x) is used
:param plotArgs: optional keyword arguments provided to plot.
:returns: handles of the plot elements.
"""
if n is None:
n = len(x)
nx = len(x)
if n == 0 or nx == 0:
res = plt.plot([], [], **plotArgs)
else:
x = sorted(x) # do not sort in place
x = np.hstack((x, x[-1]))
y = np.hstack((np.arange(0.0, nx) / n, float(nx) / n))
res = plotUnifLogXMarkers(x, y, nbperdecade=nbperdecade, drawstyle="steps", **plotArgs)
return res
def plotdata(data, maxval=None, maxevals=None, CrE=0., **kwargs):
"""Draw a normalized ECDF. What means normalized?
:param seq data: data set, a 1-D ndarray of runlengths
:param float maxval: right-most value to be displayed, will use the
largest non-inf, non-nan value in data if not
provided
:param seq maxevals: if provided, will plot the median of this
sequence as a single cross marker
:param float CrE: Crafting effort the data will be multiplied by
the exponential of this value.
:param kwargs: optional arguments provided to plot function.
"""
#Expect data to be a ndarray.
x = data[np.isnan(data) == False] # Take away the nans
nn = len(x)
x = x[np.isinf(x) == False] # Take away the infs
n = len(x)
x = np.exp(CrE) * x # correction by crafting effort CrE
if n == 0:
#res = plt.plot((1., ), (0., ), **kwargs)
res = pprldistr.plotECDF(np.array((1., )), n=np.inf, **kwargs)
else:
dictx = {} # number of appearances of each value in x
for i in x:
dictx[i] = dictx.get(i, 0) + 1
x = np.array(sorted(dictx)) # x is not a multiset anymore
y = np.cumsum(list(
dictx[i]
for i in x)) # cumsum of size of y-steps (nb of appearences)
idx = sum(x <= x_limit**annotation_space_end_relative) - 1
y_last, x_last = y[idx] / float(nn), x[idx]
if maxval is None:
maxval = max(x)
end = np.sum(x <= maxval)
x = x[:end]
y = y[:end]
try: # plot the very last point outside of the "normal" plotting area
c = kwargs['color']
plt_plot([x_last] * 2, [y_last] * 2,
'.',
color=c,
markeredgecolor=c)
except:
pass
x2 = np.hstack([np.repeat(x, 2),
maxval]) # repeat x-values for each step in the cdf
y2 = np.hstack([0.0, np.repeat(y / float(nn), 2)])
res = ppfig.plotUnifLogXMarkers(x2,
y2,
nbperdecade * 3 / np.log10(maxval),
logscale=False,
clip_on=False,
**kwargs)
# res = plotUnifLogXMarkers(x2, y2, nbperdecade, logscale=False, **kwargs)
if maxevals: # Should cover the case where maxevals is None or empty
x3 = np.median(maxevals)
if (x3 <= maxval and
# np.any(x2 <= x3) and # maxval < median(maxevals)
not plt.getp(res[-1], 'label').startswith('best')
): # TODO: HACK for not considering the best 2009 line
try:
y3 = y2[x2 <= x3][
-1] # find right y-value for x3==median(maxevals)
except IndexError: # median(maxevals) is smaller than any data, can only happen because of CrE?
y3 = y2[0]
h = plt.plot(
(x3, ),
(y3, ),
marker=median_max_evals_marker_format[0],
markersize=median_max_evals_marker_format[1],
markeredgewidth=median_max_evals_marker_format[2],
# marker='x', markersize=24, markeredgewidth=3,
markeredgecolor=plt.getp(res[0], 'color'),
ls=plt.getp(res[0], 'ls'),
color=plt.getp(res[0], 'color'))
h.extend(res)
res = h # so the last element in res still has the label.
# Only take sequences for x and y!
return res
def main(dsList0, dsList1, minfvalue=1e-8, outputdir='', verbose=True):
"""Returns ERT1/ERT0 comparison figure."""
#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)
# minfvalue = pproc.TargetValues.cast(minfvalue)
dictFun0 = dsList0.dictByFunc()
dictFun1 = dsList1.dictByFunc()
for func in set.intersection(set(dictFun0), set(dictFun1)):
dictDim0 = dictFun0[func].dictByDim()
dictDim1 = dictFun1[func].dictByDim()
if isBenchmarkinfosFound:
title = funInfos[func]
else:
title = ''
filename = os.path.join(outputdir, 'ppfig2_f%03d' % (func))
dims = sorted(set.intersection(set(dictDim0), set(dictDim1)))
handles = []
dataperdim = {}
fvalueswitch = {}
nbtests = 0
for i, dim in enumerate(dimensions):
try:
entry0 = dictDim0[dim][0]
entry1 = dictDim1[dim][0]
except KeyError:
continue
nbtests += 1
# generateData:
data = _generateData(entry0, entry1, fthresh=fthresh)
dataperdim[dim] = data
if len(data[0]) == 0 and len(data[1]) == 0:
continue
# TODO: hack, modify slightly so line goes to 'zero'
if minfvalue:
for d in data:
tmp = d[:, 0]
tmp[tmp == 0] = min(min(tmp[tmp > 0]), minfvalue)**2
# plot
idx = np.isfinite(data[0][:, 1]) * np.isfinite(data[1][:, 1])
ydata = data[1][idx, 1] / data[0][idx, 1]
kwargs = styles[i].copy()
kwargs['label'] = '%2d-D' % dim
tmp = plotUnifLogXMarkers(data[0][idx, 0],
ydata,
nbperdecade=1,
logscale=True,
**kwargs)
plt.setp(tmp, markersize=3 * linewidth)
plt.setp(tmp[0], ls='--')
# This is only one possibility:
#idx = (data[0][:, 3] >= 5) * (data[1][:, 3] >= 5)
idx = ((data[0][:, 1] <= 3 * np.median(entry0.maxevals)) *
(data[1][:, 1] <= 3 * np.median(entry1.maxevals)))
if not idx.any():
fvalueswitch[dim] = np.inf
# Hack: fvalueswitch is the smallest value of f where the line
# was still solid.
continue
fvalueswitch[dim] = min(data[0][idx, 0])
ydata = data[1][idx, 1] / data[0][idx, 1]
tmp = plotUnifLogXMarkers(data[0][idx, 0],
ydata,
nbperdecade=1,
logscale=True,
**styles[i])
plt.setp(tmp[1], markersize=3 * linewidth)
beautify(xmin=minfvalue)
#beautify()
ax = plt.gca()
# Freeze the boundaries
ax.set_autoscale_on(False)
#trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
# Plot everything else
for i, dim in enumerate(dimensions):
try:
entry0 = dictDim0[dim][0]
entry1 = dictDim1[dim][0]
data = dataperdim[dim]
except KeyError:
continue
if len(data[0]) == 0 and len(data[1]) == 0:
continue
# annotation
annotate(entry0, entry1, dim, minfvalue, nbtests=nbtests)
tmp0 = np.isfinite(data[0][:, 1])
tmp1 = np.isfinite(data[1][:, 1])
idx = tmp0 * tmp1
if not idx.any():
continue
#Do not plot anything else if it happens after minfvalue
if data[0][idx, 0][-1] <= minfvalue:
# hack for the legend
continue
# Determine which algorithm went further
algstoppedlast = 0
algstoppedfirst = 1
if np.sum(tmp0) < np.sum(tmp1):
algstoppedlast = 1
algstoppedfirst = 0
#marker if an algorithm stopped
ydata = data[1][idx, 1] / data[0][idx, 1]
plt.plot((data[0][idx, 0][-1], ), (ydata[-1], ),
marker='D',
ls='',
color=styles[i]['color'],
markeredgecolor=styles[i]['color'],
markerfacecolor=styles[i]['color'],
markersize=4 * linewidth)
tmpy = ydata[-1]
# plot probability of success line
dataofinterest = data[algstoppedlast]
tmp = np.nonzero(idx)[0][-1] # Why [0]?
# add the last line for which both algorithm still have a success
idx = (data[algstoppedfirst][:, 2]
== 0.) * (dataofinterest[:, 2] > 0.)
idx[tmp] = True
if np.sum(idx) <= 1: #len(idx) == 0 or not idx.any():
continue
ymin, ymax = plt.ylim()
#orientation = -1
ybnd = ymin
if algstoppedlast == 0:
ybnd = ymax
#orientation = 1
#ydata = orientation * dataofinterest[idx, 2] / 2 + 0.5
ydata = np.power(
10,
np.log10(ybnd) *
(dataofinterest[idx, 2] - offset *
(5 - i) * np.log10(ymax / ymin) / np.abs(np.log10(ybnd))))
ls = '-'
if dataofinterest[idx, 0][0] < fvalueswitch[dim]:
ls = '--'
tmp = plt.plot([dataofinterest[idx, 0][0]] * 2, (tmpy, ydata[0]),
**styles[i])
plt.setp(tmp, ls=ls, marker='')
tmp = plt.plot((dataofinterest[idx, 0][0], ), (ydata[0], ),
marker='D',
ls='',
color=styles[i]['color'],
markeredgecolor=styles[i]['color'],
markerfacecolor=styles[i]['color'],
markersize=4 * linewidth)
kwargs = styles[i].copy()
kwargs['ls'] = ls
tmp = plotUnifLogXMarkers(dataofinterest[idx, 0],
ydata,
nbperdecade=1,
logscale=True,
**kwargs)
plt.setp(tmp, markersize=3 * linewidth)
#Do not plot anything else if it happens after minfvalue
if dataofinterest[idx, 0][-1] <= minfvalue:
continue
#plt.plot((dataofinterest[idx, 0][-1], ), (ydata[-1], ), marker='d',
# color=styles[i]['color'], markeredgecolor=styles[i]['color'],
# markerfacecolor=styles[i]['color'], markersize=4*linewidth)
if isBenchmarkinfosFound:
plt.title(funInfos[func])
if func in functions_with_legend:
plt.legend(loc='best')
# save
saveFigure(filename, verbose=verbose)
plt.close()
def main(dsList0, dsList1, minfvalue=1e-8, outputdir='', verbose=True):
"""Returns ERT1/ERT0 comparison figure."""
#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)
# minfvalue = pproc.TargetValues.cast(minfvalue)
dictFun0 = dsList0.dictByFunc()
dictFun1 = dsList1.dictByFunc()
for func in set.intersection(set(dictFun0), set(dictFun1)):
dictDim0 = dictFun0[func].dictByDim()
dictDim1 = dictFun1[func].dictByDim()
if isBenchmarkinfosFound:
title = funInfos[func]
else:
title = ''
filename = os.path.join(outputdir,'ppfig2_f%03d' % (func))
dims = sorted(set.intersection(set(dictDim0), set(dictDim1)))
handles = []
dataperdim = {}
fvalueswitch = {}
nbtests = 0
for i, dim in enumerate(dimensions):
try:
entry0 = dictDim0[dim][0]
entry1 = dictDim1[dim][0]
except KeyError:
continue
nbtests += 1
# generateData:
data = _generateData(entry0, entry1, fthresh=fthresh)
dataperdim[dim] = data
if len(data[0]) == 0 and len(data[1]) == 0:
continue
# TODO: hack, modify slightly so line goes to 'zero'
if minfvalue:
for d in data:
tmp = d[:, 0]
tmp[tmp == 0] = min(min(tmp[tmp > 0]), minfvalue)**2
# plot
idx = np.isfinite(data[0][:, 1]) * np.isfinite(data[1][:, 1])
ydata = data[1][idx, 1]/data[0][idx, 1]
kwargs = styles[i].copy()
kwargs['label'] = '%2d-D' % dim
tmp = plotUnifLogXMarkers(data[0][idx, 0], ydata, nbperdecade=1, logscale=True, **kwargs)
plt.setp(tmp, markersize=3*linewidth)
plt.setp(tmp[0], ls='--')
# This is only one possibility:
#idx = (data[0][:, 3] >= 5) * (data[1][:, 3] >= 5)
idx = ((data[0][:, 1] <= 3 * np.median(entry0.maxevals))
* (data[1][:, 1] <= 3 * np.median(entry1.maxevals)))
if not idx.any():
fvalueswitch[dim] = np.inf
# Hack: fvalueswitch is the smallest value of f where the line
# was still solid.
continue
fvalueswitch[dim] = min(data[0][idx, 0])
ydata = data[1][idx, 1]/data[0][idx, 1]
tmp = plotUnifLogXMarkers(data[0][idx, 0], ydata, nbperdecade=1, logscale=True, **styles[i])
plt.setp(tmp[1], markersize=3*linewidth)
beautify(xmin=minfvalue)
#beautify()
ax = plt.gca()
# Freeze the boundaries
ax.set_autoscale_on(False)
#trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
# Plot everything else
for i, dim in enumerate(dimensions):
try:
entry0 = dictDim0[dim][0]
entry1 = dictDim1[dim][0]
data = dataperdim[dim]
except KeyError:
continue
if len(data[0]) == 0 and len(data[1]) == 0:
continue
# annotation
annotate(entry0, entry1, dim, minfvalue, nbtests=nbtests)
tmp0 = np.isfinite(data[0][:, 1])
tmp1 = np.isfinite(data[1][:, 1])
idx = tmp0 * tmp1
if not idx.any():
continue
#Do not plot anything else if it happens after minfvalue
if data[0][idx, 0][-1] <= minfvalue:
# hack for the legend
continue
# Determine which algorithm went further
algstoppedlast = 0
algstoppedfirst = 1
if np.sum(tmp0) < np.sum(tmp1):
algstoppedlast = 1
algstoppedfirst = 0
#marker if an algorithm stopped
ydata = data[1][idx, 1]/data[0][idx, 1]
plt.plot((data[0][idx, 0][-1], ), (ydata[-1], ), marker='D', ls='',
color=styles[i]['color'], markeredgecolor=styles[i]['color'],
markerfacecolor=styles[i]['color'], markersize=4*linewidth)
tmpy = ydata[-1]
# plot probability of success line
dataofinterest = data[algstoppedlast]
tmp = np.nonzero(idx)[0][-1] # Why [0]?
# add the last line for which both algorithm still have a success
idx = (data[algstoppedfirst][:, 2] == 0.) * (dataofinterest[:, 2] > 0.)
idx[tmp] = True
if np.sum(idx) <= 1:#len(idx) == 0 or not idx.any():
continue
ymin, ymax = plt.ylim()
#orientation = -1
ybnd = ymin
if algstoppedlast == 0:
ybnd = ymax
#orientation = 1
#ydata = orientation * dataofinterest[idx, 2] / 2 + 0.5
ydata = np.power(10, np.log10(ybnd) * (dataofinterest[idx, 2]
-offset*(5-i)*np.log10(ymax/ymin)/np.abs(np.log10(ybnd))))
ls = '-'
if dataofinterest[idx, 0][0] < fvalueswitch[dim]:
ls = '--'
tmp = plt.plot([dataofinterest[idx, 0][0]]*2, (tmpy, ydata[0]),
**styles[i])
plt.setp(tmp, ls=ls, marker='')
tmp = plt.plot((dataofinterest[idx, 0][0], ), (ydata[0], ), marker='D', ls='',
color=styles[i]['color'], markeredgecolor=styles[i]['color'],
markerfacecolor=styles[i]['color'], markersize=4*linewidth)
kwargs = styles[i].copy()
kwargs['ls'] = ls
tmp = plotUnifLogXMarkers(dataofinterest[idx, 0], ydata, nbperdecade=1, logscale=True, **kwargs)
plt.setp(tmp, markersize=3*linewidth)
#Do not plot anything else if it happens after minfvalue
if dataofinterest[idx, 0][-1] <= minfvalue:
continue
#plt.plot((dataofinterest[idx, 0][-1], ), (ydata[-1], ), marker='d',
# color=styles[i]['color'], markeredgecolor=styles[i]['color'],
# markerfacecolor=styles[i]['color'], markersize=4*linewidth)
if isBenchmarkinfosFound:
plt.title(funInfos[func])
if func in functions_with_legend:
plt.legend(loc='best')
# save
saveFigure(filename, verbose=verbose)
plt.close()
def plotdata(data, maxval=None, maxevals=None, CrE=0., **kwargs):
"""Draw a normalized ECDF. What means normalized?
:param seq data: data set, a 1-D ndarray of runlengths
:param float maxval: right-most value to be displayed, will use the
largest non-inf, non-nan value in data if not
provided
:param seq maxevals: if provided, will plot the median of this
sequence as a single cross marker
:param float CrE: Crafting effort the data will be multiplied by
the exponential of this value.
:param kwargs: optional arguments provided to plot function.
"""
#Expect data to be a ndarray.
x = data[np.isnan(data)==False] # Take away the nans
nn = len(x)
x = x[np.isinf(x)==False] # Take away the infs
n = len(x)
x = np.exp(CrE) * x # correction by crafting effort CrE
if n == 0:
#res = plt.plot((1., ), (0., ), **kwargs)
res = pprldistr.plotECDF(np.array((1., )), n=np.inf, **kwargs)
else:
dictx = {} # number of appearances of each value in x
for i in x:
dictx[i] = dictx.get(i, 0) + 1
x = np.array(sorted(dictx)) # x is not a multiset anymore
y = np.cumsum(list(dictx[i] for i in x)) # cumsum of size of y-steps (nb of appearences)
idx = sum(x <= x_limit**annotation_space_end_relative) - 1
y_last, x_last = y[idx] / float(nn), x[idx]
if maxval is None:
maxval = max(x)
end = np.sum(x <= maxval)
x = x[:end]
y = y[:end]
try: # plot the very last point outside of the "normal" plotting area
c = kwargs['color']
plt_plot([x_last] * 2, [y_last] * 2, '.', color=c, markeredgecolor=c)
except:
pass
x2 = np.hstack([np.repeat(x, 2), maxval]) # repeat x-values for each step in the cdf
y2 = np.hstack([0.0, np.repeat(y / float(nn), 2)])
res = ppfig.plotUnifLogXMarkers(x2, y2, nbperdecade * 3 / np.log10(maxval),
logscale=False, clip_on=False, **kwargs)
# res = plotUnifLogXMarkers(x2, y2, nbperdecade, logscale=False, **kwargs)
if maxevals: # Should cover the case where maxevals is None or empty
x3 = np.median(maxevals)
if (x3 <= maxval and
# np.any(x2 <= x3) and # maxval < median(maxevals)
not plt.getp(res[-1], 'label').startswith('best')
): # TODO: HACK for not considering the best 2009 line
try:
y3 = y2[x2<=x3][-1] # find right y-value for x3==median(maxevals)
except IndexError: # median(maxevals) is smaller than any data, can only happen because of CrE?
y3 = y2[0]
h = plt.plot((x3,), (y3,),
marker=median_max_evals_marker_format[0],
markersize=median_max_evals_marker_format[1],
markeredgewidth=median_max_evals_marker_format[2],
# marker='x', markersize=24, markeredgewidth=3,
markeredgecolor=plt.getp(res[0], 'color'),
ls=plt.getp(res[0], 'ls'),
color=plt.getp(res[0], 'color'))
h.extend(res)
res = h # so the last element in res still has the label.
# Only take sequences for x and y!
return res
def plotLogAbs(dsList0, dsList1, dim, targetValuesToReach, verbose=True):
"""Creates ECDF of run length ratios.
:param DataSetList dsList0: reference
:param DataSetList dsList1: data set list of algorithm of interest
:param int dim: dimension
:param TargetValues targetValuesToReach: target function values
:param bool verbose: controls verbosity
:returns: handles
"""
res = []
dictFunc0 = dsList0.dictByFunc()
dictFunc1 = dsList1.dictByFunc()
evals0 = {}
evals1 = {}
if not isinstance(targetValuesToReach, pproc.TargetValues):
targetValuesToReach = pproc.TargetValues(targetValuesToReach)
succ0 = [0] * len(targetValuesToReach)
succ1 = [0] * len(targetValuesToReach)
# TODO: check all functions are there...
for func in set(dictFunc0.keys()) & set(dictFunc1.keys()):
i0 = dictFunc0[func][0]
i1 = dictFunc1[func][0]
# tmp = list(i[func] for i in fvalueToReach)
# if tmp != targets:
# set_trace() # should not occur
# warnings.warn('')
evals0[func] = i0.detEvals(targetValuesToReach((func, dim)))
for i, evals in enumerate(evals0[func]):
tmp = numpy.isnan(evals)
evals[tmp] = numpy.inf
if not tmp.all():
succ0[i] += 1
evals1[func] = i1.detEvals(targetValuesToReach((func, dim)))
for i, evals in enumerate(evals1[func]):
tmp = numpy.isnan(evals)
evals[tmp] = numpy.inf
if not tmp.all():
succ1[i] += 1
for j in range(len(targetValuesToReach)):
x = []
for func in evals0:
# Compute the pair-wise ratio
tmp1 = numpy.reshape(evals1[func][j], (1, len(evals1[func][j])))
tmp0 = numpy.reshape(evals0[func][j], (len(evals0[func][j]), 1))
try:
x.append((tmp1 / tmp0).flatten()) # inf/inf results in nan
except FloatingPointError:
if numpy.isfinite(tmp1).all() or numpy.isfinite(tmp1).all():
raise
# TODO: check division, check numpy.inf...
if isinstance(targetValuesToReach, pproc.RunlengthBasedTargetValues):
label = "%s: %d/%d" % (targetValuesToReach.label(j), succ1[j], succ0[j])
else:
label = "%s: %d/%d" % (targetValuesToReach.loglabel(j), succ1[j], succ0[j])
if len(x) > 0: # prevent warning/error
x = numpy.hstack(x)
x = x[numpy.isnan(x) == False] # Is it correct?
n = len(x)
if n == 0:
res.extend(plt.plot([], [], label=label, linewidth=3.0, **rldStyles[j]))
continue # no plot?
x.sort()
# Catch negative values: zeros are not a problem...
tmp = len(list(i for i in x if i <= 0))
x = x[tmp:]
# Catch inf, those could be a problem with the log scale...
# tmp2 = 0
tmp2 = len(list(i for i in x if i > 0 and numpy.isinf(i)))
if tmp2 > 0:
x = x[:-tmp2]
# xbound = max(abs(numpy.floor(numpy.log10(x[0]))),
# abs(numpy.ceil(numpy.log10(x[-1]))))
if len(x) == 0:
res.append(plt.axhline(tmp / float(n), label=label, linewidth=3.0, **rldStyles[j]))
# tmp/float(n) == (n-tmp2)/float(n) # TODO: check
else:
x2 = numpy.hstack([numpy.repeat(x, 2)])
# maxEvalsF: used for the limit of the plot.
y2 = numpy.hstack(
[tmp / float(n), numpy.repeat(numpy.arange(tmp + 1, n - tmp2) / float(n), 2), (n - tmp2) / float(n)]
)
# res.extend(plt.plot(x2, y2, label=label, linewidth=3., **rldStyles[i]))
plotArgs = rldStyles[j].copy()
plotArgs["label"] = label
plotArgs["linewidth"] = 3.0
# res.extend(plotUnifLogXMarkers(x2, y2, 3, plotArgs))
res.append(plotUnifLogXMarkers(x2, y2, nbperdecade=3, logscale=False, **plotArgs)[0])
# TODO: check if all of evalsX[func] is numpy.inf and so on...
return res
def plotLogAbs(dsList0, dsList1, dim, targetValuesToReach, verbose=True):
"""Creates ECDF of run length ratios.
:param DataSetList dsList0: reference
:param DataSetList dsList1: data set list of algorithm of interest
:param int dim: dimension
:param TargetValues targetValuesToReach: target function values
:param bool verbose: controls verbosity
:returns: handles
"""
res = []
dictFunc0 = dsList0.dictByFunc()
dictFunc1 = dsList1.dictByFunc()
evals0 = {}
evals1 = {}
if not isinstance(targetValuesToReach, pproc.TargetValues):
targetValuesToReach = pproc.TargetValues(targetValuesToReach)
succ0 = [0] * len(targetValuesToReach)
succ1 = [0] * len(targetValuesToReach)
# TODO: check all functions are there...
for func in set(dictFunc0.keys()) & set(dictFunc1.keys()):
i0 = dictFunc0[func][0]
i1 = dictFunc1[func][0]
#tmp = list(i[func] for i in fvalueToReach)
#if tmp != targets:
# set_trace() # should not occur
# warnings.warn('')
evals0[func] = i0.detEvals(targetValuesToReach((func, dim)))
for i, evals in enumerate(evals0[func]):
tmp = numpy.isnan(evals)
evals[tmp] = numpy.inf
if not tmp.all():
succ0[i] += 1
evals1[func] = i1.detEvals(targetValuesToReach((func, dim)))
for i, evals in enumerate(evals1[func]):
tmp = numpy.isnan(evals)
evals[tmp] = numpy.inf
if not tmp.all():
succ1[i] += 1
for j in range(len(targetValuesToReach)):
x = []
for func in evals0:
# Compute the pair-wise ratio
tmp1 = numpy.reshape(evals1[func][j], (1, len(evals1[func][j])))
tmp0 = numpy.reshape(evals0[func][j], (len(evals0[func][j]), 1))
try:
x.append((tmp1 / tmp0).flatten()) # inf/inf results in nan
except FloatingPointError:
if numpy.isfinite(tmp1).all() or numpy.isfinite(tmp1).all():
raise
#TODO: check division, check numpy.inf...
if isinstance(targetValuesToReach, pproc.RunlengthBasedTargetValues):
label = '%s: %d/%d' % (targetValuesToReach.label(j), succ1[j],
succ0[j])
else:
label = '%s: %d/%d' % (targetValuesToReach.loglabel(j), succ1[j],
succ0[j])
if len(x) > 0: # prevent warning/error
x = numpy.hstack(x)
x = x[numpy.isnan(x) == False] # Is it correct?
n = len(x)
if n == 0:
res.extend(
plt.plot([], [], label=label, linewidth=3., **rldStyles[j]))
continue # no plot?
x.sort()
#Catch negative values: zeros are not a problem...
tmp = len(list(i for i in x if i <= 0))
x = x[tmp:]
#Catch inf, those could be a problem with the log scale...
#tmp2 = 0
tmp2 = len(list(i for i in x if i > 0 and numpy.isinf(i)))
if tmp2 > 0:
x = x[:-tmp2]
#xbound = max(abs(numpy.floor(numpy.log10(x[0]))),
# abs(numpy.ceil(numpy.log10(x[-1]))))
if len(x) == 0:
res.append(
plt.axhline(tmp / float(n),
label=label,
linewidth=3.,
**rldStyles[j]))
# tmp/float(n) == (n-tmp2)/float(n) # TODO: check
else:
x2 = numpy.hstack([numpy.repeat(x, 2)])
#maxEvalsF: used for the limit of the plot.
y2 = numpy.hstack([
tmp / float(n),
numpy.repeat(numpy.arange(tmp + 1, n - tmp2) / float(n), 2),
(n - tmp2) / float(n)
])
#res.extend(plt.plot(x2, y2, label=label, linewidth=3., **rldStyles[i]))
plotArgs = rldStyles[j].copy()
plotArgs['label'] = label
plotArgs['linewidth'] = 3.
#res.extend(plotUnifLogXMarkers(x2, y2, 3, plotArgs))
res.append(
plotUnifLogXMarkers(x2,
y2,
nbperdecade=3,
logscale=False,
**plotArgs)[0])
# TODO: check if all of evalsX[func] is numpy.inf and so on...
return res
