def create_plot(filename, data, performance_object):
grdevices.png(file=filename)
(ggplot2.ggplot(data) + ggplot2.aes_string(
x="dimension", y="mean.%s" % performance_object) +
ggplot2.geom_point(ggplot2.aes_string(colour="signature")) +
ggplot2.geom_errorbar(
ggplot2.aes_string(
ymax="mean.%s+stderror.%s" %
(performance_object, performance_object),
ymin="mean.%s-stderror.%s" %
(performance_object, performance_object),
))).plot()
grdevices.dev_off()
def bargraph_language(cfg, values):
r = robjects.r
for lang in cfg.languages:
times = []
varss = []
probs = []
ses = []
for prob in cfg.problems:
for var in cfg.variations:
# we use the pretty names to make the
varss.append(pretty_varis[var])
probs.append(prob)
data = FloatVector(values[prob][var][lang][0])
times.append(r['mean'](data)[0])
t_result = r['t.test'](data, **{
" conf.level": 0.999
}).rx('conf.int')[0]
ses.append((t_result[1] - t_result[0]) / 2)
r.pdf('bargraph-executiontime-lang-' + lang + '.pdf',
height=pdf_height(),
width=pdf_width())
df = robjects.DataFrame({
'Variation': StrVector(varss),
'Problem': StrVector(probs),
'Time': FloatVector(times),
'SE': FloatVector(ses)
})
limits = ggplot2.aes(ymax='Time + SE', ymin='Time - SE')
dodge = ggplot2.position_dodge(width=0.9)
gp = ggplot2.ggplot(df)
pp = gp + \
ggplot2.aes_string (x='Problem', y='Time', fill='Variation') + \
ggplot2.geom_bar (position='dodge', stat='identity') + \
ggplot2.geom_errorbar (limits, position=dodge, width=0.25) + \
ggplot2_options () + \
ggplot2_colors () + \
robjects.r('scale_x_discrete(limits=c("randmat", "thresh", "winnow", "outer", "product", "chain"))') +\
robjects.r('ylab("Execution time (in seconds)")')
pp.plot()
r['dev.off']()
def bargraph_language (cfg, values):
r = robjects.r
for lang in cfg.languages:
times = []
varss = []
probs = []
ses = []
for prob in cfg.problems:
for var in cfg.variations:
# we use the pretty names to make the
varss.append (pretty_varis [var])
probs.append (prob)
data = FloatVector (values[prob][var][lang][0])
times.append (r['mean'] (data)[0])
t_result = r['t.test'] (data,
**{" conf.level": 0.999}).rx ('conf.int')[0]
ses.append ((t_result[1] - t_result[0])/2)
r.pdf ('bargraph-executiontime-lang-' + lang + '.pdf',
height=pdf_height (), width=pdf_width ())
df = robjects.DataFrame({'Variation': StrVector (varss),
'Problem': StrVector (probs),
'Time' : FloatVector (times),
'SE' : FloatVector (ses)
})
limits = ggplot2.aes (ymax = 'Time + SE', ymin = 'Time - SE')
dodge = ggplot2.position_dodge (width=0.9)
gp = ggplot2.ggplot (df)
pp = gp + \
ggplot2.aes_string (x='Problem', y='Time', fill='Variation') + \
ggplot2.geom_bar (position='dodge', stat='identity') + \
ggplot2.geom_errorbar (limits, position=dodge, width=0.25) + \
ggplot2_options () + \
ggplot2_colors () + \
robjects.r('scale_x_discrete(limits=c("randmat", "thresh", "winnow", "outer", "product", "chain"))') +\
robjects.r('ylab("Execution time (in seconds)")')
pp.plot ()
r['dev.off']()
def line_plot (cfg, var, control, change_name, changing, selector, base_selector, basis):
speedups = []
thrds = []
changes = []
lowers = []
uppers = []
for n in cfg.threads:
probs.append ('ideal')
langs.append ('ideal')
speedups.append (n)
thrds.append (n)
changes.append ('ideal')
lowers.append (n)
uppers.append (n)
for c in changing:
sel = selector (c)
# sequential base
base = FloatVector (base_selector(c))
# base with p = 1
base_p1 = FloatVector (sel(1))
# use fastest sequential program
if basis == 'fastest' and mean (base_p1) < mean(base):
base = base_p1
elif basis == 'seq':
pass
elif basis == 'p1':
base = base_p1
for n in cfg.threads:
ntimes = FloatVector (sel(n))
# ratio confidence interval
labels = ['Base'] * r.length(base)[0] + ['N']*r.length (ntimes)[0]
df = DataFrame ({'Times': base + ntimes,
'Type': StrVector(labels)})
ratio_test = r['pairwiseCI'] (r('Times ~ Type'), data=df,
control='N',
method='Param.ratio',
**{'var.equal': False,
'conf.level': 0.999})[0][0]
lowers.append (ratio_test[1][0])
uppers.append (ratio_test[2][0])
mn = mean (ntimes)
speedups.append (mean(base) / mn)
# plot slowdowns
#speedups.append (-mn/base)#(base / mn)
thrds.append (n)
if change_name == 'Language':
changes.append (pretty_langs [c])
else:
changes.append (c)
df = DataFrame ({'Speedup': FloatVector (speedups),
'Threads': IntVector (thrds),
change_name: StrVector (changes),
'Lower': FloatVector (lowers),
'Upper': FloatVector (uppers)
})
ideal_changing = ['ideal']
if change_name == 'Language':
ideal_changing.extend ([pretty_langs [c] for c in changing])
else:
ideal_changing.extend (changing)
legendVec = IntVector (range (len (ideal_changing)))
legendVec.names = StrVector (ideal_changing)
gg = ggplot2.ggplot (df)
limits = ggplot2.aes (ymax = 'Upper', ymin = 'Lower')
dodge = ggplot2.position_dodge (width=0.9)
pp = gg + \
ggplot2.geom_line() + ggplot2.geom_point(size=3) +\
ggplot2.aes_string(x='Threads', y='Speedup',
group=change_name, color=change_name,
shape=change_name) + \
ggplot2.scale_shape_manual(values=legendVec) + \
ggplot2.geom_errorbar (limits, width=0.25) + \
ggplot2_options () + \
ggplot2_colors () + \
ggplot2.opts (**{'axis.title.x' : ggplot2.theme_text(family = 'serif', face = 'bold', size = 15, vjust=-0.2)}) + \
robjects.r('ylab("Speedup")') + \
robjects.r('xlab("Cores")')
# ggplot2.xlim (min(threads), max(threads)) + ggplot2.ylim(min(threads), max(threads)) +\
pp.plot()
r['dev.off']()
def as_dataframe (cfg, results, basis):
r = robjects.r
varis = []
langs = []
probs = []
times = []
threads = []
# speedups, with upper and lower bounds below
speedups = []
speedup_lowers = []
speedup_uppers = []
ses = [] # standard errors
mems = [] # memory usage
langs_ideal = list (cfg.languages)
langs_ideal.append ('ideal')
probs_ideal = list (cfg.problems)
probs_ideal.append ('ideal')
for var in cfg.variations:
for lang in langs_ideal: # cfg.languages:
for prob in probs_ideal: # cfg.problems:
for thread in cfg.threads:
if lang == 'ideal' and prob == 'ideal':
continue
elif lang == 'ideal' or prob == 'ideal':
varis.append (var)
langs.append (pretty_langs[lang])
probs.append (prob)
threads.append (thread)
speedups.append (thread)
speedup_lowers.append (thread)
speedup_uppers.append (thread)
times.append (0)
ses.append(0)
mems.append (0)
continue
varis.append (var) # pretty_varis [var])
langs.append (pretty_langs [lang])
probs.append (prob)
threads.append (thread)
if var.find('seq') >= 0:
thread = cfg.threads[-1]
vals = FloatVector (results[thread][prob][var][lang][0])
time = mean (vals)
times.append (time)
#
# time confidence interval
#
t_result = r['t.test'] (FloatVector(vals),
**{" conf.level": 0.999}).rx ('conf.int')[0]
ses.append ((t_result[1] - t_result[0])/2)
#
# memory usage
#
mem_filename = get_mem_output (lang, prob, var)
with open (mem_filename, 'r') as mem_file:
mem = mem_file.readline()
mems.append (float (mem))
# we include dummy data for the sequential case to avoid the
# speedup calculation below
if var.find('seq') >= 0:
speedups.append (1)
speedup_lowers.append (1)
speedup_uppers.append (1)
continue
#
# speedup values and confidence intervals
#
seq_vals = results[cfg.threads[-1]][prob][var.replace ('par', 'seq')][lang][0]
# sequential base
base = FloatVector (seq_vals)
# base with p = 1
base_p1 = FloatVector (results[1][prob][var][lang][0])
# use fastest sequential program
if basis == 'fastest' and mean (base_p1) < mean(base):
base = base_p1
elif basis == 'seq':
pass
elif basis == 'p1':
base = base_p1
labels = ['Base'] * r.length(base)[0] + ['N']*r.length (vals)[0]
df = DataFrame ({'Times': base + vals,
'Type': StrVector(labels)})
ratio_test = r['pairwiseCI'] (r('Times ~ Type'), data=df,
control='N',
method='Param.ratio',
**{'var.equal': False})[0][0]
speedups.append (mean(base) / time)
speedup_lowers.append (ratio_test[1][0])
speedup_uppers.append (ratio_test[2][0])
df = robjects.DataFrame({'Language': StrVector (langs),
'Problem': StrVector (probs),
'Variation' : StrVector (varis),
'Threads': IntVector (threads),
'Time': FloatVector (times),
'SE': FloatVector (ses),
'Speedup': FloatVector (speedups),
'SpeedupLower': FloatVector (speedup_lowers),
'SpeedupUpper': FloatVector (speedup_uppers),
'Mem' : FloatVector (mems)
})
r.assign ('df', df)
r ('save (df, file="performance.Rda")')
# reshape the data to make variation not a column itself, but a part of
# the other columns describe ie, time, speedup, etc.
#
# also, remove the 'ideal' problem as we don't want it in this plot.
df = r('''
redf = reshape (df,
timevar="Variation",
idvar = c("Language","Problem","Threads"),
direction="wide")
redf$Problem <- factor(redf$Problem, levels = c("randmat","thresh","winnow","outer","product","chain"))
redf[which(redf$Problem != "ideal"),]
''')
r.pdf ('speedup-expertpar-all.pdf',
height=6.5, width=10)
change_name = 'Language'
legendVec = IntVector (range (len (langs_ideal)))
legendVec.names = StrVector (langs_ideal)
gg = ggplot2.ggplot (df)
limits = ggplot2.aes (ymax = 'SpeedupUpper.expertpar', ymin = 'SpeedupLower.expertpar')
dodge = ggplot2.position_dodge (width=0.9)
pp = gg + \
ggplot2.geom_line() + ggplot2.geom_point(size=2.5) +\
robjects.r('scale_color_manual(values = c("#ffcb7e", "#1da06b", "#b94646", "#00368a", "#CCCCCC"))') +\
ggplot2.aes_string(x='Threads', y='Speedup.expertpar',
group=change_name, color=change_name,
shape=change_name) + \
ggplot2.geom_errorbar (limits, width=0.25) + \
ggplot2.opts (**{'axis.title.x' : ggplot2.theme_text(family = 'serif', face = 'bold', size = 10, vjust=-0.2),
'axis.title.y' : ggplot2.theme_text(family = 'serif', face = 'bold', size = 10, angle=90, vjust=0.2),
'axis.text.x' : ggplot2.theme_text(family = 'serif', size = 10),
'axis.text.y' : ggplot2.theme_text(family = 'serif', size = 10),
'legend.title' : ggplot2.theme_text(family = 'serif', face = 'bold', size = 10),
'legend.text' : ggplot2.theme_text(family = 'serif', size = 10),
'strip.text.x' : ggplot2.theme_text(family = 'serif', size = 10),
'aspect.ratio' : 1,
}) + \
robjects.r('ylab("Speedup")') + \
robjects.r('xlab("Number of cores")') + \
ggplot2.facet_wrap ('Problem', nrow = 2)
pp.plot()
r['dev.off']()
def bargraph_variation (cfg, values):
r = robjects.r
for var in cfg.variations:
# each variation gets plot
avgs = []
ses = []
# normalized values
navgs = []
nses = []
langs = []
probs = []
for prob in cfg.problems:
# aggregate by problems
lavgs = []
lses = []
for lang in cfg.languages:
# each problem displays a list of language times for that problem
data = FloatVector (values[prob][var][lang][0])
langs.append (pretty_langs [lang])
probs.append (prob)
mean = r['mean'] (data)[0]
lavgs.append (mean)
t_result = r['t.test'] (data, **{"conf.level": 0.999}).rx ('conf.int')[0]
lses.append ((t_result[1] - t_result[0])/2)
avgs.extend (lavgs)
ses.extend (lses)
lmin = min (lavgs)
navgs.extend ([la/lmin for la in lavgs])
nses.extend ([ls/lmin for ls in lses])
df = robjects.DataFrame({'Language': StrVector (langs),
'Problem': StrVector (probs),
'Time' : FloatVector (avgs),
'SE' : FloatVector (ses),
'NormTime' : FloatVector (navgs),
'NormSE' : FloatVector (nses),
'TimeLabel' : StrVector ([str(round(time, 1)) + "s" for time in avgs])
})
# plot histogram of actual times
r.pdf ('bargraph-executiontime-var-' + var + '.pdf', height=pdf_height (), width=pdf_width ())
limits = ggplot2.aes (ymax = 'Time + SE', ymin = 'Time - SE')
dodge = ggplot2.position_dodge (width=0.9)
gp = ggplot2.ggplot (df)
pp = gp + \
ggplot2.aes_string (x='Problem', y='Time', fill='Language') + \
ggplot2.geom_bar (position='dodge', stat='identity') + \
ggplot2.geom_errorbar (limits, position=dodge, width=0.25) + \
ggplot2_options () + \
ggplot2_colors () + \
robjects.r('scale_x_discrete(limits=c("randmat", "thresh", "winnow", "outer", "product", "chain"))') +\
robjects.r('ylab("Execution time (in seconds)")')
pp.plot ()
# plot histogram of times normalized with respect to fastest time for a problem
r.pdf ('bargraph-executiontime-var-norm-' + var + '.pdf', height=pdf_height (), width=pdf_width ())
limits = ggplot2.aes (ymax = 'NormTime + NormSE', ymin = 'NormTime - NormSE')
dodge = ggplot2.position_dodge (width=0.9)
gp = ggplot2.ggplot (df)
pp = gp + \
ggplot2.aes_string (x='Problem', y='NormTime', fill='Language') + \
ggplot2.geom_bar (position='dodge', stat='identity') + \
ggplot2.geom_errorbar (limits, position=dodge, width=0.25) +\
ggplot2_options () + \
ggplot2_colors () + \
robjects.r('scale_x_discrete(limits=c("randmat", "thresh", "winnow", "outer", "product", "chain"))') +\
robjects.r('ylab("Execution time (normalized to fastest)")')
#ggplot2.geom_text(data=df,
# mapping = ggplot2.aes_string (x='Problem',
# y='NormTime + NormSE + 0.1',
# label='TimeLabel')
pp.plot ()
r['dev.off']()
annotate1 = r('annotate("text", x = '+str(max(areas)-30)+', y = 0.5, color = "red", label = "Mean Annual", parse=FALSE)')
annotate2 = r('annotate("text", x = '+str(max(areas)-30)+', y = 0.42, label = "'+r_sq_lab+'", color = "red", parse=TRUE)')
annotate3 = r('annotate("text", x = '+str(max(areas)-30)+', y = 0.34, label = "slope~'+sl+'", color = "red", parse=TRUE)')
annotate4 = r('annotate("text", x = '+str(max(areas)-150)+', y = 0.7, color = "blue", label = "LGM", parse=FALSE)')
annotate5 = r('annotate("text", x = '+str(max(areas)-150)+', y = 0.6, color = "blue", label = "'+r_sq_lab_lgm+'", parse=TRUE)')
annotate6 = r('annotate("text", x = '+str(max(areas)-150)+', y = 0.5, color = "blue", label = "slope~'+sl_lgm+'", parse=TRUE)')
pp = ggplot2.ggplot(dat_frame) + \
ggplot2.aes_string(y='discharge', x='areas') + \
ggplot2.ggtitle('Area vs. Sediment Flux') + \
ggplot2.scale_x_log10(x_lab) + \
ggplot2.theme_bw() + \
ggplot2.stat_smooth(method = "lm", formula = 'y ~ x') + \
ggplot2.scale_y_log10(y_lab) + \
annotate1 + \
annotate2 + \
annotate3 + \
annotate4 + \
annotate5 + \
annotate6 + \
ggplot2.geom_point(color='blue') + \
ggplot2.geom_errorbar(ggplot2.aes_string(ymin='min',ymax='max'), data=dat_frame, width=.02, alpha=.3) + \
ggplot2.geom_point(data=dat_frame2,color='red',show_guide='FALSE' ) + \
ggplot2.stat_smooth(data=dat_frame2, method = "lm", formula = 'y ~ x', color='red')
grdevices = importr('grDevices')
grdevices.pdf(file="area_qs.pdf")
pp.plot()
grdevices.dev_off()
def line_plot(cfg, var, control, change_name, changing, selector,
base_selector, basis):
speedups = []
thrds = []
changes = []
lowers = []
uppers = []
for n in cfg.threads:
probs.append('ideal')
langs.append('ideal')
speedups.append(n)
thrds.append(n)
changes.append('ideal')
lowers.append(n)
uppers.append(n)
for c in changing:
sel = selector(c)
# sequential base
base = FloatVector(base_selector(c))
# base with p = 1
base_p1 = FloatVector(sel(1))
# use fastest sequential program
if basis == 'fastest' and mean(base_p1) < mean(base):
base = base_p1
elif basis == 'seq':
pass
elif basis == 'p1':
base = base_p1
for n in cfg.threads:
ntimes = FloatVector(sel(n))
# ratio confidence interval
labels = ['Base'] * r.length(base)[0] + ['N'] * r.length(ntimes)[0]
df = DataFrame({'Times': base + ntimes, 'Type': StrVector(labels)})
ratio_test = r['pairwiseCI'](r('Times ~ Type'),
data=df,
control='N',
method='Param.ratio',
**{
'var.equal': False,
'conf.level': 0.999
})[0][0]
lowers.append(ratio_test[1][0])
uppers.append(ratio_test[2][0])
mn = mean(ntimes)
speedups.append(mean(base) / mn)
# plot slowdowns
#speedups.append (-mn/base)#(base / mn)
thrds.append(n)
if change_name == 'Language':
changes.append(pretty_langs[c])
else:
changes.append(c)
df = DataFrame({
'Speedup': FloatVector(speedups),
'Threads': IntVector(thrds),
change_name: StrVector(changes),
'Lower': FloatVector(lowers),
'Upper': FloatVector(uppers)
})
ideal_changing = ['ideal']
if change_name == 'Language':
ideal_changing.extend([pretty_langs[c] for c in changing])
else:
ideal_changing.extend(changing)
legendVec = IntVector(range(len(ideal_changing)))
legendVec.names = StrVector(ideal_changing)
gg = ggplot2.ggplot(df)
limits = ggplot2.aes(ymax='Upper', ymin='Lower')
dodge = ggplot2.position_dodge(width=0.9)
pp = gg + \
ggplot2.geom_line() + ggplot2.geom_point(size=3) +\
ggplot2.aes_string(x='Threads', y='Speedup',
group=change_name, color=change_name,
shape=change_name) + \
ggplot2.scale_shape_manual(values=legendVec) + \
ggplot2.geom_errorbar (limits, width=0.25) + \
ggplot2_options () + \
ggplot2_colors () + \
ggplot2.opts (**{'axis.title.x' : ggplot2.theme_text(family = 'serif', face = 'bold', size = 15, vjust=-0.2)}) + \
robjects.r('ylab("Speedup")') + \
robjects.r('xlab("Cores")')
# ggplot2.xlim (min(threads), max(threads)) + ggplot2.ylim(min(threads), max(threads)) +\
pp.plot()
r['dev.off']()
def as_dataframe(cfg, results, basis):
r = robjects.r
varis = []
langs = []
probs = []
times = []
threads = []
# speedups, with upper and lower bounds below
speedups = []
speedup_lowers = []
speedup_uppers = []
ses = [] # standard errors
mems = [] # memory usage
langs_ideal = list(cfg.languages)
langs_ideal.append('ideal')
probs_ideal = list(cfg.problems)
probs_ideal.append('ideal')
for var in cfg.variations:
for lang in langs_ideal: # cfg.languages:
for prob in probs_ideal: # cfg.problems:
for thread in cfg.threads:
if lang == 'ideal' and prob == 'ideal':
continue
elif lang == 'ideal' or prob == 'ideal':
varis.append(var)
langs.append(pretty_langs[lang])
probs.append(prob)
threads.append(thread)
speedups.append(thread)
speedup_lowers.append(thread)
speedup_uppers.append(thread)
times.append(0)
ses.append(0)
mems.append(0)
continue
varis.append(var) # pretty_varis [var])
langs.append(pretty_langs[lang])
probs.append(prob)
threads.append(thread)
if var.find('seq') >= 0:
thread = cfg.threads[-1]
vals = FloatVector(results[thread][prob][var][lang][0])
time = mean(vals)
times.append(time)
#
# time confidence interval
#
t_result = r['t.test'](FloatVector(vals), **{
" conf.level": 0.999
}).rx('conf.int')[0]
ses.append((t_result[1] - t_result[0]) / 2)
#
# memory usage
#
mem_filename = get_mem_output(lang, prob, var)
with open(mem_filename, 'r') as mem_file:
mem = mem_file.readline()
mems.append(float(mem))
# we include dummy data for the sequential case to avoid the
# speedup calculation below
if var.find('seq') >= 0:
speedups.append(1)
speedup_lowers.append(1)
speedup_uppers.append(1)
continue
#
# speedup values and confidence intervals
#
seq_vals = results[cfg.threads[-1]][prob][var.replace(
'par', 'seq')][lang][0]
# sequential base
base = FloatVector(seq_vals)
# base with p = 1
base_p1 = FloatVector(results[1][prob][var][lang][0])
# use fastest sequential program
if basis == 'fastest' and mean(base_p1) < mean(base):
base = base_p1
elif basis == 'seq':
pass
elif basis == 'p1':
base = base_p1
labels = ['Base'
] * r.length(base)[0] + ['N'] * r.length(vals)[0]
df = DataFrame({
'Times': base + vals,
'Type': StrVector(labels)
})
ratio_test = r['pairwiseCI'](r('Times ~ Type'),
data=df,
control='N',
method='Param.ratio',
**{
'var.equal': False
})[0][0]
speedups.append(mean(base) / time)
speedup_lowers.append(ratio_test[1][0])
speedup_uppers.append(ratio_test[2][0])
df = robjects.DataFrame({
'Language': StrVector(langs),
'Problem': StrVector(probs),
'Variation': StrVector(varis),
'Threads': IntVector(threads),
'Time': FloatVector(times),
'SE': FloatVector(ses),
'Speedup': FloatVector(speedups),
'SpeedupLower': FloatVector(speedup_lowers),
'SpeedupUpper': FloatVector(speedup_uppers),
'Mem': FloatVector(mems)
})
r.assign('df', df)
r('save (df, file="performance.Rda")')
# reshape the data to make variation not a column itself, but a part of
# the other columns describe ie, time, speedup, etc.
#
# also, remove the 'ideal' problem as we don't want it in this plot.
df = r('''
redf = reshape (df,
timevar="Variation",
idvar = c("Language","Problem","Threads"),
direction="wide")
redf$Problem <- factor(redf$Problem, levels = c("randmat","thresh","winnow","outer","product","chain"))
redf[which(redf$Problem != "ideal"),]
''')
r.pdf('speedup-expertpar-all.pdf', height=6.5, width=10)
change_name = 'Language'
legendVec = IntVector(range(len(langs_ideal)))
legendVec.names = StrVector(langs_ideal)
gg = ggplot2.ggplot(df)
limits = ggplot2.aes(ymax='SpeedupUpper.expertpar',
ymin='SpeedupLower.expertpar')
dodge = ggplot2.position_dodge(width=0.9)
pp = gg + \
ggplot2.geom_line() + ggplot2.geom_point(size=2.5) +\
robjects.r('scale_color_manual(values = c("#ffcb7e", "#1da06b", "#b94646", "#00368a", "#CCCCCC"))') +\
ggplot2.aes_string(x='Threads', y='Speedup.expertpar',
group=change_name, color=change_name,
shape=change_name) + \
ggplot2.geom_errorbar (limits, width=0.25) + \
ggplot2.opts (**{'axis.title.x' : ggplot2.theme_text(family = 'serif', face = 'bold', size = 10, vjust=-0.2),
'axis.title.y' : ggplot2.theme_text(family = 'serif', face = 'bold', size = 10, angle=90, vjust=0.2),
'axis.text.x' : ggplot2.theme_text(family = 'serif', size = 10),
'axis.text.y' : ggplot2.theme_text(family = 'serif', size = 10),
'legend.title' : ggplot2.theme_text(family = 'serif', face = 'bold', size = 10),
'legend.text' : ggplot2.theme_text(family = 'serif', size = 10),
'strip.text.x' : ggplot2.theme_text(family = 'serif', size = 10),
'aspect.ratio' : 1,
}) + \
robjects.r('ylab("Speedup")') + \
robjects.r('xlab("Number of cores")') + \
ggplot2.facet_wrap ('Problem', nrow = 2)
pp.plot()
r['dev.off']()
def bargraph_variation(cfg, values):
r = robjects.r
for var in cfg.variations:
# each variation gets plot
avgs = []
ses = []
# normalized values
navgs = []
nses = []
langs = []
probs = []
for prob in cfg.problems:
# aggregate by problems
lavgs = []
lses = []
for lang in cfg.languages:
# each problem displays a list of language times for that problem
data = FloatVector(values[prob][var][lang][0])
langs.append(pretty_langs[lang])
probs.append(prob)
mean = r['mean'](data)[0]
lavgs.append(mean)
t_result = r['t.test'](data, **{
"conf.level": 0.999
}).rx('conf.int')[0]
lses.append((t_result[1] - t_result[0]) / 2)
avgs.extend(lavgs)
ses.extend(lses)
lmin = min(lavgs)
navgs.extend([la / lmin for la in lavgs])
nses.extend([ls / lmin for ls in lses])
df = robjects.DataFrame({
'Language':
StrVector(langs),
'Problem':
StrVector(probs),
'Time':
FloatVector(avgs),
'SE':
FloatVector(ses),
'NormTime':
FloatVector(navgs),
'NormSE':
FloatVector(nses),
'TimeLabel':
StrVector([str(round(time, 1)) + "s" for time in avgs])
})
# plot histogram of actual times
r.pdf('bargraph-executiontime-var-' + var + '.pdf',
height=pdf_height(),
width=pdf_width())
limits = ggplot2.aes(ymax='Time + SE', ymin='Time - SE')
dodge = ggplot2.position_dodge(width=0.9)
gp = ggplot2.ggplot(df)
pp = gp + \
ggplot2.aes_string (x='Problem', y='Time', fill='Language') + \
ggplot2.geom_bar (position='dodge', stat='identity') + \
ggplot2.geom_errorbar (limits, position=dodge, width=0.25) + \
ggplot2_options () + \
ggplot2_colors () + \
robjects.r('scale_x_discrete(limits=c("randmat", "thresh", "winnow", "outer", "product", "chain"))') +\
robjects.r('ylab("Execution time (in seconds)")')
pp.plot()
# plot histogram of times normalized with respect to fastest time for a problem
r.pdf('bargraph-executiontime-var-norm-' + var + '.pdf',
height=pdf_height(),
width=pdf_width())
limits = ggplot2.aes(ymax='NormTime + NormSE',
ymin='NormTime - NormSE')
dodge = ggplot2.position_dodge(width=0.9)
gp = ggplot2.ggplot(df)
pp = gp + \
ggplot2.aes_string (x='Problem', y='NormTime', fill='Language') + \
ggplot2.geom_bar (position='dodge', stat='identity') + \
ggplot2.geom_errorbar (limits, position=dodge, width=0.25) +\
ggplot2_options () + \
ggplot2_colors () + \
robjects.r('scale_x_discrete(limits=c("randmat", "thresh", "winnow", "outer", "product", "chain"))') +\
robjects.r('ylab("Execution time (normalized to fastest)")')
#ggplot2.geom_text(data=df,
# mapping = ggplot2.aes_string (x='Problem',
# y='NormTime + NormSE + 0.1',
# label='TimeLabel')
pp.plot()
r['dev.off']()
base = importr("base")
grdevices = importr("grDevices")
r = ro.r
df = ro.DataFrame(
{
"x": ro.FloatVector(x),
"y": ro.FloatVector(y),
"e": ro.FloatVector(e),
"ymin": ro.FloatVector(y - e),
"ymax": ro.FloatVector(y + e),
}
)
rprint(df)
gp = ggplot2.ggplot(df)
pp = gp + ggplot2.aes_string(x="x", y="y", ymin="ymin", ymax="ymax") + ggplot2.geom_point() + ggplot2.geom_errorbar()
grdevices.png(file="data.png", width=512, height=512)
print (pp)
grdevices.dev_off()
from scipy import optimize
def squared_loss(theta, x=x, y=y, e=e):
dy = y - theta[0] - theta[1] * x
return np.sum(0.5 * (dy / e) ** 2)
theta1 = optimize.fmin(squared_loss, [0, 0], disp=False)
rprint = ro.globalenv.get("print")
base = importr('base')
grdevices = importr('grDevices')
r = ro.r
df = ro.DataFrame({'x': ro.FloatVector(x), \
'y': ro.FloatVector(y), \
'e': ro.FloatVector(e), \
'ymin': ro.FloatVector(y-e), \
'ymax': ro.FloatVector(y+e)})
rprint(df)
gp = ggplot2.ggplot(df)
pp = gp + \
ggplot2.aes_string(x='x', y='y', ymin='ymin', ymax='ymax') + \
ggplot2.geom_point() + \
ggplot2.geom_errorbar()
grdevices.png(file="data.png", width=512, height=512)
print(pp)
grdevices.dev_off()
from scipy import optimize
def squared_loss(theta, x=x, y=y, e=e):
dy = y - theta[0] - theta[1] * x
return np.sum(0.5 * (dy / e)**2)
theta1 = optimize.fmin(squared_loss, [0, 0], disp=False)
# theta will be an array of length 2 + N, where N is the number of points
