def plotBarChartPerAlgorithmPerTerrain(data,
dataErr=None,
outfile="",
yAxLabel="",
plotTitle="",
maxVal=0,
legendPos='upper right'):
assert (len(data) == len(AVAILABLE_ALGORITHMS))
colors = ColorCyle()
barWidth = 1.0 / (len(data) + 1)
fig, ax = plt.subplots()
ax.set_ylabel(yAxLabel)
ax.set_title(plotTitle)
if maxVal > 0:
ax.set_autoscaley_on(False)
ax.set_ylim([0, maxVal])
for algoCount, algoName in enumerate(AVAILABLE_ALGORITHMS):
algoData = data[algoName]
algoErr = None
if dataErr != None:
algoErr = dataErr[algoName]
leftBorders = [
i + (algoCount * barWidth) for i in xrange(len(algoData))
]
rects = ax.bar(leftBorders,
algoData,
yerr=algoErr,
width=barWidth,
label=ALGORITHM_NAMES[algoName],
color=colors.next())
# add value label on top of bars
for rect in rects:
barHeight = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.0,
1.0 * barHeight,
'%.1f' % barHeight,
ha='center',
va='bottom')
ax.set_xticks([(i + (len(data) * barWidth) / 2)
for i in xrange(len(AVAILABLE_WORLDS))])
ax.set_xticklabels(getWorldNames())
ax.legend(loc=legendPos)
fig.set_size_inches(9.6, 5.4)
if len(outfile) > 0:
plt.savefig(outfile, dpi=100)
else:
plt.show()
def plot_ranking(rankings, optimum=0, title="", log=False,
save="", y_min=0, y_max=0, figsize=(16, 6), legend_ncols=4,
colors=None, markers=None, markersize=6, linewidth=3):
# check if all optimizers have the same number of runs
# if np.mean([name[1] for name in name_list]) != name_list[0][1]:
# raise Exception("All optimizers must have the same numbers of "
# "experiment runs! %s" % name_list)
fig = plt.figure(dpi=600, figsize=figsize)
ax = plt.subplot(111)
if colors is None:
colors = plot_util.get_plot_colors()
if markers is None:
markers = plot_util.get_empty_iterator()
for i, optimizer in enumerate(rankings):
ax.plot(range(0, rankings[optimizer].shape[0]),
rankings[optimizer],
marker=markers.next(), markersize=markersize,
color=colors.next(), linewidth=linewidth,
label=optimizer.replace("\\", "").
replace("learned", "d_c").replace("l1", "d_p"))
ax.legend(loc='upper center', #bbox_to_anchor=(0.5, -0.05),
fancybox=True, shadow=True, ncol=legend_ncols,
labelspacing=0.25, fontsize=12)
ax.set_xlabel("#Function evaluations")
ax.set_ylabel("Average rank")
box = ax.get_position()
#ax.set_position([box.x0, box.y0 + box.height * 0.1,
# box.width, box.height * 0.9])
if save != "":
plt.savefig(save, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches="tight", pad_inches=0.1)
else:
plt.show()
plt.close(fig)
# linFitYvals=np.empty(len(xValsToFit))
# linFitYvals.fill(np.NAN)
#
# j=0
# for i in xValsToFit:
# linFitYvals[j] = M*i + b
# j+=1
#############
# Plot Things
#############
qDataPI = np.array([convertQ(qq, trackLength) for qq in range(0, qLen)])
maxQrad = convertQ(maxQ, trackLength)
ax1 = plt.subplot(211)
m = markers.next()
c = colors.next()
ax1.plot(qDataPI,
convertW(fitFWHM / 2.0, fftSamples),
color=c,
linestyle='',
marker=m,
label=densityString)
#ax1.plot(qDataPI,convertW(fitFWHM/2.0,fftSamples),color=c,linestyle='',marker=m,label=trackString)
# plt.plot(gamma,convertW(fitFWHM,fftSamples)/2.0,'g.')
# plt.plot(gamma,gamma,'b')
#plt.plot(xValsToFit,linFitYvals,'k-')
ax1.set_title('Free peak in each q slice\n' + infoText)
ax1.set_ylabel('HWHM (rad / time step)', fontsize=16)
#ymax = np.nanmax(convertW(fitFWHM,fftSamples))/2*1.1
def plotBarChartPerTerrainPerAlgorithm(data, dataErr=None, outfile="", yAxLabel="", plotTitle="", maxVal=0, legendPos='upper right'):
assert(len(data) == len(AVAILABLE_WORLDS))
colors = ColorCyle()
barWidth = 1.0 / (len(data) + 1)
fig, ax = plt.subplots()
ax.set_ylabel(yAxLabel)
ax.set_title(plotTitle)
if maxVal > 0:
ax.set_autoscaley_on(False)
ax.set_ylim([0,maxVal])
for worldCount, worldName in enumerate(AVAILABLE_WORLDS):
worldData = data[worldName]
worldErr = None
if dataErr != None:
worldErr = dataErr[worldName]
leftBorders = [i + (worldCount * barWidth) for i in xrange(len(worldData))]
rects = ax.bar(leftBorders, worldData, yerr=worldErr, width=barWidth, label=WORLD_NAMES[worldName], color=colors.next())
# add value label on top of bars
for rect in rects:
barHeight = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.0, 1.0 * barHeight, '%.1f'%barHeight,
ha='center', va='bottom')
ax.set_xticks([(i + (len(data) * barWidth) / 2) for i in xrange(len(AVAILABLE_ALGORITHMS))])
ax.set_xticklabels(getAlgorithmNames())
ax.legend(loc=legendPos)
fig.set_size_inches(9.6,5.4)
if len(outfile) > 0:
plt.savefig(outfile, dpi=100)
else:
plt.show()
def plotBarChartPerRobotCountPerAlgorithm(data, dataErr=None, outfile="", yAxLabel="", plotTitle="", maxVal=0, legendPos='upper right'):
assert(len(data) == len(AVAILABLE_ROBOT_COUNTS))
colors = ColorCyle()
barWidth = 1.0 / (len(data) + 1)
fig, ax = plt.subplots()
ax.set_ylabel(yAxLabel)
ax.set_title(plotTitle)
if maxVal > 0:
ax.set_autoscaley_on(False)
ax.set_ylim([0,maxVal])
for robotNum, robotCount in enumerate(AVAILABLE_ROBOT_COUNTS):
robotCountData = data[robotCount]
robotCountErr = None
if dataErr != None:
robotCountErr = dataErr[robotCount]
leftBorders = [i + (robotNum * barWidth) for i in xrange(len(robotCountData))]
labelStr = ""
if robotCount == 1:
labelStr = "1 Robot"
else:
labelStr = str(robotCount) + " Robots"
rects = ax.bar(leftBorders, robotCountData, yerr=robotCountErr, width=barWidth, label=labelStr, color=colors.next())
# add value label on top of bars
for rect in rects:
barHeight = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.0, 1.0 * barHeight, '%.1f'%barHeight,
ha='center', va='bottom')
ax.set_xticks([(i + (len(data) * barWidth) / 2) for i in xrange(len(AVAILABLE_ALGORITHMS))])
ax.set_xticklabels(getAlgorithmNames())
ax.legend(loc=legendPos)
fig.set_size_inches(9.6,5.4)
if len(outfile) > 0:
plt.savefig(outfile, dpi=100)
else:
plt.show()
def plot_ranking(rankings,
optimum=0,
title="",
log=False,
save="",
y_min=0,
y_max=0,
figsize=(16, 6),
legend_ncols=4,
colors=None,
markers=None,
markersize=6,
linewidth=3):
# check if all optimizers have the same number of runs
# if np.mean([name[1] for name in name_list]) != name_list[0][1]:
# raise Exception("All optimizers must have the same numbers of "
# "experiment runs! %s" % name_list)
fig = plt.figure(dpi=600, figsize=figsize)
ax = plt.subplot(111)
if colors is None:
colors = plot_util.get_plot_colors()
if markers is None:
markers = plot_util.get_empty_iterator()
for i, optimizer in enumerate(rankings):
ax.plot(range(0, rankings[optimizer].shape[0]),
rankings[optimizer],
marker=markers.next(),
markersize=markersize,
color=colors.next(),
linewidth=linewidth,
label=optimizer.replace("\\", "").replace("learned",
"d_c").replace(
"l1", "d_p"))
ax.legend(
loc='upper center', #bbox_to_anchor=(0.5, -0.05),
fancybox=True,
shadow=True,
ncol=legend_ncols,
labelspacing=0.25,
fontsize=12)
ax.set_xlabel("#Function evaluations")
ax.set_ylabel("Average rank")
box = ax.get_position()
#ax.set_position([box.x0, box.y0 + box.height * 0.1,
# box.width, box.height * 0.9])
if save != "":
plt.savefig(save,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format=None,
transparent=False,
bbox_inches="tight",
pad_inches=0.1)
else:
plt.show()
plt.close(fig)
def plot_summed_wins_of_optimizers(trial_list_per_dataset,
name_list_per_dataset,
save="",
cut=sys.maxint,
figsize=(16, 4),
legend_ncols=3,
colors=None,
linewidth=3,
markers=None,
markersize=6):
# TODO colors should be a function handle which returns an Iterable!
# This is a hack
cut = 50
optimizers, summed_wins_of_optimizer = get_summed_wins_of_optimizers(
trial_list_per_dataset, name_list_per_dataset)
# Make a copy of the colors iterator, because we need it more than once!
if colors is not None:
if not isinstance(colors, itertools.cycle):
raise TypeError()
else:
color_values = list()
for i in range(10):
r, g, b = colors.next()
color_values.append((r, g, b))
if markers is not None:
if not isinstance(markers, itertools.cycle):
raise TypeError()
else:
marker_values = list()
for i in range(10):
marker_values.append(markers.next())
################################################################################
# Plot statistics
for opt1_idx, key in enumerate(optimizers):
fig, (ax0, ax1) = plt.subplots(nrows=2,
sharex=True,
dpi=600,
figsize=figsize)
if colors is None:
colors_ = plot_util.get_plot_colors()
else:
colors_ = itertools.cycle(color_values)
if markers is None:
markers_ = plot_util.get_empty_iterator()
else:
markers_ = itertools.cycle(marker_values)
y_max = 0.
for opt2_idx, key2 in enumerate(optimizers):
if opt1_idx == opt2_idx:
continue
y = []
y1 = []
for i in range(0, cut + 1):
y.append(summed_wins_of_optimizer[i][opt1_idx, opt2_idx] /
len(trial_list_per_dataset) * 100)
y1.append(-summed_wins_of_optimizer[i][opt2_idx, opt1_idx] /
len(trial_list_per_dataset) * 100)
y_max_tmp = max(np.max(y), np.max(np.abs(y1)))
y_max_tmp = np.ceil(y_max_tmp * 10) / 10.
y_max = max(y_max_tmp, y_max)
label = "%s vs %s" % (key, key2)
label = label.replace("learned", "d_c").replace("l1", "d_p")
color = colors_.next()
marker = markers_.next()
ax0.plot(range(0, cut + 1),
y,
color=color,
label=label,
linewidth=linewidth,
marker=marker,
markersize=markersize)
ax1.plot(range(0, cut + 1),
y1,
color=color,
label=label,
linewidth=linewidth,
marker=marker,
markersize=markersize)
#handles, labels = ax1.get_legend_handles_labels()
#fig.legend(handles, labels, loc="upper center", fancybox=True,
# ncol=legend_ncols, shadow=True)
ax0.set_xlim((0, cut))
ax0.set_ylim((0, y_max))
ax0.set_ylabel("Significant wins (%)")
ax1.set_xlim((0, cut))
ax1.set_ylim((-y_max, 0))
ax1.set_ylabel("Significant losses (%)")
yticklabels = ax1.get_yticks().tolist()
#print yticklabels, [item.get_text() for item in yticklabels]
ax1.set_yticklabels([-int(item) for item in yticklabels])
ax1.legend(loc="best", fancybox=True, ncol=legend_ncols, shadow=True)
plt.tight_layout()
#plt.subplots_adjust(top=0.85)
plt.xlabel("#Function evaluations")
if save != "":
plt.savefig(save % key,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format=None,
transparent=False,
bbox_inches="tight",
pad_inches=0.1)
else:
plt.show()
plt.close(fig)
def plot_summed_wins_of_optimizers(trial_list_per_dataset,
name_list_per_dataset,
save="", cut=sys.maxint,
figsize=(16, 4), legend_ncols=3,
colors=None, linewidth=3,
markers=None, markersize=6):
# TODO colors should be a function handle which returns an Iterable!
# This is a hack
cut = 50
optimizers, summed_wins_of_optimizer = get_summed_wins_of_optimizers(
trial_list_per_dataset, name_list_per_dataset)
# Make a copy of the colors iterator, because we need it more than once!
if colors is not None:
if not isinstance(colors, itertools.cycle):
raise TypeError()
else:
color_values = list()
for i in range(10):
r, g, b = colors.next()
color_values.append((r, g, b))
if markers is not None:
if not isinstance(markers, itertools.cycle):
raise TypeError()
else:
marker_values = list()
for i in range(10):
marker_values.append(markers.next())
################################################################################
# Plot statistics
for opt1_idx, key in enumerate(optimizers):
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, dpi=600,
figsize=figsize)
if colors is None:
colors_ = plot_util.get_plot_colors()
else:
colors_ = itertools.cycle(color_values)
if markers is None:
markers_ = plot_util.get_empty_iterator()
else:
markers_ = itertools.cycle(marker_values)
y_max = 0.
for opt2_idx, key2 in enumerate(optimizers):
if opt1_idx == opt2_idx:
continue
y = []
y1 = []
for i in range(0, cut+1):
y.append(summed_wins_of_optimizer[i][opt1_idx, opt2_idx]
/ len(trial_list_per_dataset) * 100)
y1.append(- summed_wins_of_optimizer[i][opt2_idx, opt1_idx]
/ len(trial_list_per_dataset) * 100)
y_max_tmp = max(np.max(y), np.max(np.abs(y1)))
y_max_tmp = np.ceil(y_max_tmp * 10) / 10.
y_max = max(y_max_tmp, y_max)
label = "%s vs %s" % (key, key2)
label = label.replace("learned", "d_c").replace("l1", "d_p")
color = colors_.next()
marker = markers_.next()
ax0.plot(range(0, cut+1), y, color=color, label=label,
linewidth=linewidth, marker=marker, markersize=markersize)
ax1.plot(range(0, cut+1), y1, color=color, label=label,
linewidth=linewidth, marker=marker, markersize=markersize)
#handles, labels = ax1.get_legend_handles_labels()
#fig.legend(handles, labels, loc="upper center", fancybox=True,
# ncol=legend_ncols, shadow=True)
ax0.set_xlim((0, cut))
ax0.set_ylim((0, y_max))
ax0.set_ylabel("Significant wins (%)")
ax1.set_xlim((0, cut))
ax1.set_ylim((-y_max, 0))
ax1.set_ylabel("Significant losses (%)")
yticklabels = ax1.get_yticks().tolist()
#print yticklabels, [item.get_text() for item in yticklabels]
ax1.set_yticklabels([-int(item) for item in yticklabels])
ax1.legend(loc="best", fancybox=True, ncol=legend_ncols, shadow=True)
plt.tight_layout()
#plt.subplots_adjust(top=0.85)
plt.xlabel("#Function evaluations")
if save != "":
plt.savefig(save % key, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches="tight", pad_inches=0.1)
else:
plt.show()
plt.close(fig)
def plotBarChartPerRobotCountPerAlgorithm(data,
dataErr=None,
outfile="",
yAxLabel="",
plotTitle="",
maxVal=0,
legendPos='upper right'):
assert (len(data) == len(AVAILABLE_ROBOT_COUNTS))
colors = ColorCyle()
barWidth = 1.0 / (len(data) + 1)
fig, ax = plt.subplots()
ax.set_ylabel(yAxLabel)
ax.set_title(plotTitle)
if maxVal > 0:
ax.set_autoscaley_on(False)
ax.set_ylim([0, maxVal])
for robotNum, robotCount in enumerate(AVAILABLE_ROBOT_COUNTS):
robotCountData = data[robotCount]
robotCountErr = None
if dataErr != None:
robotCountErr = dataErr[robotCount]
leftBorders = [
i + (robotNum * barWidth) for i in xrange(len(robotCountData))
]
labelStr = ""
if robotCount == 1:
labelStr = "1 Robot"
else:
labelStr = str(robotCount) + " Robots"
rects = ax.bar(leftBorders,
robotCountData,
yerr=robotCountErr,
width=barWidth,
label=labelStr,
color=colors.next())
# add value label on top of bars
for rect in rects:
barHeight = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.0,
1.0 * barHeight,
'%.1f' % barHeight,
ha='center',
va='bottom')
ax.set_xticks([(i + (len(data) * barWidth) / 2)
for i in xrange(len(AVAILABLE_ALGORITHMS))])
ax.set_xticklabels(getAlgorithmNames())
ax.legend(loc=legendPos)
fig.set_size_inches(9.6, 5.4)
if len(outfile) > 0:
plt.savefig(outfile, dpi=100)
else:
plt.show()
