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plot.py
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plot.py
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# make the algorithms an argument
# make the metrics an argument
import os
import sys
import json
import traceback
import random
import math
import matplotlib
import matplotlib.pyplot as plt
import pprint
colors = {'red': '#cd7058', 'blue': '#599ad3', 'orange': '#f9a65a', 'green': '#66cc66', 'black': '#000000', 'purple': '#990066'}
numbering_subplots = ['a', 'b', 'c', 'd', 'e', 'f']
def compute_average(datapoints, has_shift):
if len(datapoints) == 0:
return 0, 0, 0
num_freq = 0
sum_metric = 0
if not has_shift:
minimum = 0
else:
minimum = None
for key, value in datapoints.iteritems():
occurrence = float(key)
frequency = float(value)
num_freq += frequency
sum_metric += frequency * occurrence
if has_shift:
if minimum is None or occurrence < minimum:
minimum = occurrence
if num_freq <= 1:
return 0, 0, 0
mean = float(sum_metric) / float(num_freq)
sum_metric_squared = 0
for key, value in datapoints.iteritems():
occurrence = float(key)
frequency = float(value)
sum_metric_squared += frequency * (occurrence - mean) * (occurrence - mean)
variance = float(sum_metric_squared) / float(num_freq - 1)
standard_deviation = math.sqrt(variance)
return mean - minimum, variance, standard_deviation
def compute_median(datapoints, has_shift):
# TODO: very inefficient, could optimize this method
if len(datapoints) == 0:
return 0, 0, 0
values = []
minimum = None
for key, value in datapoints.iteritems():
occurrence = float(key)
frequency = float(value)
for i in range(int(frequency)):
values.append(occurrence)
if has_shift:
if minimum is None or occurrence < minimum:
minimum = occurrence
if not has_shift: minimum = 0
values = sorted(values)
median = values[len(values) / 2] - minimum
perc95 = values[int(float(len(values)) * .95)] - minimum
maximum = values[-1] - minimum
return median, perc95, maximum
def aggregate_datapoints(dirpath_data, testcases, algorithms, shifts):
print testcases, algorithms, shifts
aggregate = {}
for dirname, dirnames, filenames in os.walk(dirpath_data):
for filename in filenames:
basename, ext = os.path.splitext(filename)
if ext.lower() != '.json': continue
if '50000000' in filename: continue
if testcases != 'all' and not any(filename.startswith(testcase) for testcase in testcases.split(',')):
print 'skipping ' + filename
continue
if algorithms != 'all' and not any(algorithm in filename for algorithm in algorithms.split(',')):
print 'skipping ' + filename
continue
try:
filepath = os.path.join(dirname, filename)
print "Reading file [%s]" % (filepath,)
f = open(filepath, 'r')
text = f.read()
data_items = json.loads(text)
f.close()
has_shift = shifts and any(shift in filename for shift in shifts.split(','))
if not isinstance(data_items, list):
data_items = [data_items]
for data in data_items:
average, variance, stddev = compute_average(data['datapoints'], has_shift)
median, perc95, maximum = compute_median(data['datapoints'], has_shift)
ia = data['algorithm']
im = data['metric']
ib = data['parameters_hashmap_string']
ia = '%s-%s' % (ia, ib)
ii = data['instance']
ic = data['cycle']
it = data['testcase']
ip = data['parameters_testcase_string']
if '75' in ip:
print "before", ip
ip = ip.replace('lfm0.75', 'lfm0.80')
print "after", ip
it = '%s-%s' % (it, ip)
if im not in aggregate:
aggregate[im] = {}
if it not in aggregate[im]:
aggregate[im][it] = {}
if ia not in aggregate[im][it]:
aggregate[im][it][ia] = {}
if ic not in aggregate[im][it][ia]:
aggregate[im][it][ia][ic] = {}
for m in ['mean', 'median', 'perc95', 'standard_deviation', 'variance', 'maximum']:
if m not in aggregate[im][it][ia][ic]:
aggregate[im][it][ia][ic][m] = []
aggregate[im][it][ia][ic]['mean'].append(average)
aggregate[im][it][ia][ic]['standard_deviation'].append(stddev)
aggregate[im][it][ia][ic]['variance'].append(variance)
aggregate[im][it][ia][ic]['median'].append(median)
aggregate[im][it][ia][ic]['perc95'].append(perc95)
aggregate[im][it][ia][ic]['maximum'].append(maximum)
except:
print 'Crashed at file: [%s/%s]' % (dirname, filename)
print traceback.print_exc()
sys.exit(1)
return aggregate
def randomized_paired_sample_t_test(reference, candidate, details):
num_items = len(reference)
random.seed(None)
population = []
print 'ref cand', reference, candidate
diff = []
for i in range(num_items):
diff.append(reference[i] - candidate[i])
num_population = 10240
for k in range(num_population):
diff_new = []
for i in range(num_items):
sign = -1 if random.random() < 0.5 else 1
diff_new.append(diff[i] * sign)
mean_new = float(sum(diff_new)) / float(num_items)
population.append(mean_new)
count_passed = 0
mean = sum(diff) / num_items
population = sorted(population)
for mean_current in population:
if (mean > 0 and mean <= mean_current) or (mean < 0 and mean < mean_currrent):
break
count_passed += 1
if mean > 0:
count_passed = num_population - count_passed
if False and details:
print "*" * 64
print "*" * 64
print "details"
print "population", population[0], population[1], population[-2], population[-1]
print "mean", mean
print "count_passed: %f" % (float(count_passed),)
print "num_pop %f" % (float(num_population), )
p_value = float(count_passed) / float(num_population)
print "passed: %f" % (p_value,)
return p_value
def add_curve_to_plot(ax, aggregates, im, it, index_testcase, statistic, algorithms_ordering, filters, numbering_subplot, includes):
names = []
lines = []
font = {'family' : 'normal',
'weight' : 'normal',
'size' : 14}
matplotlib.rc('font', **font)
algorithms = [None] * 5
for ia in aggregates[im][it].keys():
for pattern in algorithms_ordering.keys():
if pattern in ia:
order = algorithms_ordering[pattern]['order']
algorithms[order] = ia
for ia in algorithms:
if ia is None: continue
print "Generating curve for: stats:%s | metric:%s | testcase:%s | algorithm:%s" % (statistic, im, it, ia)
xs = []
ys = []
for cycle, stats in sorted(aggregates[im][it][ia].items()):
if 'loading' in it:
xs.append((cycle * 2.0) / 100.0)
else:
xs.append(cycle)
ys.append(sum(stats[statistic]) / len(stats[statistic]))
name = '[ERROR: unknown algorithm]'
color = '#000000'
linewidth = 3
zorder = 1
for k, v in filters.iteritems():
if k in ia:
name = filters[k]['name']
color = filters[k]['color']
linewidth = filters[k]['linewidth']
style = '-'
zorder = filters[k]['zorder']
break
if not any(pattern in ia for pattern in includes):
continue
line_current, = ax.plot(xs, ys, style, color=color, linewidth=linewidth, zorder=zorder)
names.append(name)
lines.append(line_current)
if 'loading' in it:
ax.set_xlabel('(%s) Load factor' % numbering_subplot)
else:
ax.set_xlabel('(%s) Iterations' % numbering_subplot)
if statistic == 'mean':
ax.set_ylabel('Mean %s' % im)
if True or 'loading' not in it:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,0,100))
elif statistic == 'variance':
ax.set_ylabel('Variance of %s' % im)
if True or 'loading' not in it:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,0,600))
elif statistic == 'standard_deviation':
ax.set_ylabel('Standard deviation of %s' % im)
elif statistic == 'median':
ax.set_ylabel('Median of %s' % im)
if True or 'loading' not in it:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,0,100))
elif statistic == 'perc95':
ax.set_ylabel('95th percentile of %s' % im)
if True or 'loading' not in it:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,0,100))
elif statistic == 'maximum':
ax.set_ylabel('Maximum %s' % im)
if True or 'loading' not in it:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,0,180))
plt.title('Test case: %s' % (it.strip('-')))
ax.grid(True)
if any(metric in im for metric in ['blocks', 'aligned']) and statistic != 'variance':
labels=['16 B', '32 B', '64 B', '128 B', '256 B', '512 B', '1 KB', '2 KB', '4 KB', '8 KB', '16 KB', '32 KB', '64 KB', '128 KB']
plt.axis((x1,x2,4,4+len(labels)))
ax.set_yticks(range(4,4+len(labels)))
ax.set_yticklabels(labels)
plt.legend(lines, names).set_visible(False)
return names, lines
def plot_algorithms(aggregates):
for index_stat, statistic in enumerate(['mean', 'median', 'perc95', 'maximum', 'variance']):
for index_metric, im in enumerate(aggregates.keys()):
fig = plt.figure((index_stat+1) * 10000 + (index_metric+1) * 100 + 1)
legend = None
for index_testcase, it in enumerate(sorted(aggregates[im].keys())):
ax = fig.add_subplot(2, 2, index_testcase+1)
lines = []
names = []
names_temp, lines_temp = add_curve_to_plot(
ax=ax,
aggregates=aggregates,
im=im,
it=it,
index_testcase=index_testcase,
statistic=statistic,
algorithms_ordering = {
'linear': {'order': 0},
'backshift': {'order': 1},
'tombstone': {'order': 2},
'shadow': {'order': 3},
'bitmap': {'order': 4},
},
filters = {
'linear': { 'color': colors['blue'], 'name': 'Linear probing', 'linewidth': 8, 'zorder': 1 },
'backshift': { 'color': colors['orange'], 'name': 'Robin Hood (backward shift)', 'linewidth': 6, 'zorder': 2 },
'tombstone': { 'color': colors['red'], 'name': 'Robin Hood (tombstone)', 'linewidth': 4.5, 'zorder': 3 },
'shadow': { 'color': colors['green'], 'name': 'Hopscotch (shadow)', 'linewidth': 3, 'zorder': 4 },
'bitmap': { 'color': colors['black'], 'name': 'Hopscotch (bitmap)', 'linewidth': 1.75, 'zorder': 5 },
},
numbering_subplot=numbering_subplots[index_testcase],
includes=['10000-'],
)
names.extend(names_temp)
lines.extend(lines_temp)
legend = plt.legend(lines, names, prop={'size':12}, bbox_to_anchor=(0.2, -0.3))
if not os.path.isdir('plots/algorithms'):
os.mkdir('plots/algorithms')
fig.set_size_inches(10, 7.5)
plt.tight_layout()
plt.savefig('plots/algorithms/%s_%s.png' % (im.lower(), statistic), dpi=72, bbox_extra_artists=(legend,), bbox_inches='tight')
def plot_robinhood(aggregates):
for index_metric, im in enumerate(aggregates.keys()):
fig = plt.figure((index_metric+1) * 100 + 1)
for index_stat, statistic in enumerate(['mean', 'median', 'perc95', 'maximum', 'variance']):
ax = fig.add_subplot(3, 2, index_stat+1)
lines = []
names = []
for index_testcase, it in enumerate(sorted(aggregates[im].keys())):
names_temp, lines_temp = add_curve_to_plot(
ax=ax,
aggregates=aggregates,
im=im,
it=it,
index_testcase=index_testcase,
statistic=statistic,
algorithms_ordering = {
'10000-': {'order': 0},
'100000-': {'order': 1},
'1000000-': {'order': 2},
'10000000-': {'order': 3},
'50000000-': {'order': 4},
},
filters = {
'10000-': { 'color': colors['blue'], 'name': 'Robin Hood (backward shift, 10k)', 'linewidth': 8, 'zorder': 1 },
'100000-': { 'color': colors['orange'], 'name': 'Robin Hood (backward shift, 100k)', 'linewidth': 6, 'zorder': 2 },
'1000000-': { 'color': colors['red'], 'name': 'Robin Hood (backward shift, 1M)', 'linewidth': 4.5, 'zorder': 3 },
'10000000-': { 'color': colors['green'], 'name': 'Robin Hood (backward shift, 10M)', 'linewidth': 3, 'zorder': 4 },
'50000000-': { 'color': colors['black'], 'name': 'Robin Hood (backward shift, 50M)', 'linewidth': 1.75, 'zorder': 5 },
'100000000-': { 'color': colors['black'], 'name': 'Robin Hood (backward shift, 100M)', 'linewidth': 1.75, 'zorder': 5 },
},
numbering_subplot=numbering_subplots[index_stat],
includes=['backshift'],
)
names.extend(names_temp)
lines.extend(lines_temp)
legend = plt.legend(lines, names, prop={'size':12}, bbox_to_anchor=(2.10, 0.75))
fig.set_size_inches(10, 11.25)
plt.tight_layout()
if not os.path.isdir('plots/robinhood-backshift'):
os.mkdir('plots/robinhood-backshift')
plt.savefig('plots/robinhood-backshift/%s.png' % (im.lower()), dpi=72, bbox_extra_artists=(legend,), bbox_inches='tight')
if __name__=="__main__":
shifts = ""
if len(sys.argv) == 5:
shifts = sys.argv[4]
agg = aggregate_datapoints(dirpath_data=sys.argv[1],
testcases=sys.argv[2],
algorithms=sys.argv[3],
shifts=shifts)
plot_algorithms(agg)
plot_robinhood(agg)