def print_aggregated_stats(stats_list):
"""Prints the aggregated stats of given |stats_list|."""
# Skip incomplete stats (probably crashed during this run). We collect
# enough runs to make up for an occasional missed run.
stat_list = [stats for stats in stats_list if stats.is_complete()]
raw_stats = _build_raw_stats(stats_list)
# Builds a dict from key to (median, 90-percentile).
aggregated_stats = {
key: statistics.compute_percentiles(value, (50, 90))
for key, value in raw_stats.iteritems()
}
# If there is more than 1 stats, print the VPERF= and VRAWPERF= lines.
if len(stats_list) > 1:
# Print VPERF= lines.
for name in _ALL_STAT_VARS:
unit = 'ms' if name.endswith('_ms') else 'MB'
median, p90 = aggregated_stats[name]
print 'VPERF=%(name)s: %(median).2f%(unit)s 90%%=%(p90).2f' % {
'name': name,
'unit': unit,
'median': median,
'p90': p90,
}
# Print VRAWPERF= line.
print 'VRAWPERF=%s' % dict(raw_stats)
# Note: since each value is the median for each data set, they are not
# guaranteed to add up.
print ('\nPERF=boot:%dms (preEmbed:%dms + pluginLoad:%dms + onResume:%dms),'
'\n virt:%.1fMB, res:%.1fMB, pdirt:%.1fMB, runs:%d\n' % (
aggregated_stats['boot_time_ms'][0],
aggregated_stats['pre_embed_time_ms'][0],
aggregated_stats['plugin_load_time_ms'][0],
aggregated_stats['on_resume_time_ms'][0],
aggregated_stats['app_virt_mem'][0],
aggregated_stats['app_res_mem'][0],
aggregated_stats['app_pdirt_mem'][0],
len(stat_list)))
def print_aggregated_stats(stats_list):
"""Prints the aggregated stats of given |stats_list|."""
# Skip incomplete stats (probably crashed during this run). We collect
# enough runs to make up for an occasional missed run.
stat_list = [stats for stats in stats_list if stats.is_complete()]
raw_stats = _build_raw_stats(stats_list)
# Builds a dict from key to (median, 90-percentile).
aggregated_stats = {
key: statistics.compute_percentiles(value, (50, 90))
for key, value in raw_stats.iteritems()
}
# If there is more than 1 stats, print the VPERF= and VRAWPERF= lines.
if len(stats_list) > 1:
# Print VPERF= lines.
for name in _ALL_STAT_VARS:
unit = 'ms' if name.endswith('_ms') else 'MB'
median, p90 = aggregated_stats[name]
print 'VPERF=%(name)s: %(median).2f%(unit)s 90%%=%(p90).2f' % {
'name': name,
'unit': unit,
'median': median,
'p90': p90,
}
# Print VRAWPERF= line.
print 'VRAWPERF=%s' % dict(raw_stats)
# Note: since each value is the median for each data set, they are not
# guaranteed to add up.
print(
'\nPERF=boot:%dms (preEmbed:%dms + pluginLoad:%dms + onResume:%dms),'
'\n virt:%.1fMB, res:%.1fMB, pdirt:%.1fMB, runs:%d\n' %
(aggregated_stats['boot_time_ms'][0],
aggregated_stats['pre_embed_time_ms'][0],
aggregated_stats['plugin_load_time_ms'][0],
aggregated_stats['on_resume_time_ms'][0],
aggregated_stats['app_virt_mem'][0],
aggregated_stats['app_res_mem'][0],
aggregated_stats['app_pdirt_mem'][0], len(stat_list)))
def bootstrap_estimation(
ctrl_sample, expt_sample, statistic, confidence_level):
"""Estimates confidence interval of difference of a statistic by Bootstrap.
Args:
ctrl_sample: A control sample as a list of numbers.
expt_sample: An experiment sample as a list of numbers.
statistic: A function that computes a statistic from a sample.
confidence_level: An integer that specifies requested confidence level
in percentage, e.g. 90, 95, 99.
Returns:
Estimated range as a number tuple.
"""
bootstrap_distribution = []
for _ in xrange(1000):
bootstrap_distribution.append(
statistic(bootstrap_sample(expt_sample)) -
statistic(bootstrap_sample(ctrl_sample)))
return statistics.compute_percentiles(
bootstrap_distribution, (100 - confidence_level, confidence_level))
def bootstrap_estimation(ctrl_sample, expt_sample, statistic,
confidence_level):
"""Estimates confidence interval of difference of a statistic by Bootstrap.
Args:
ctrl_sample: A control sample as a list of numbers.
expt_sample: An experiment sample as a list of numbers.
statistic: A function that computes a statistic from a sample.
confidence_level: An integer that specifies requested confidence level
in percentage, e.g. 90, 95, 99.
Returns:
Estimated range as a number tuple.
"""
bootstrap_distribution = []
for _ in xrange(1000):
bootstrap_distribution.append(
statistic(bootstrap_sample(expt_sample)) -
statistic(bootstrap_sample(ctrl_sample)))
return statistics.compute_percentiles(
bootstrap_distribution, (100 - confidence_level, confidence_level))
def test_compute_percentiles(self):
self.assertTrue(all(map(math.isnan, statistics.compute_percentiles([]))))
self.assertEquals(statistics.compute_median([5, 8]),
statistics.compute_percentiles([5, 8], [50])[0])
self.assertEquals(statistics.compute_median([2, 3, 7]),
statistics.compute_percentiles([2, 3, 7], [50])[0])
# All expected values below agree with Google Docs.
self.assertEquals((42, 42), statistics.compute_percentiles([42]))
self.assertEquals((6.5, 7.7), statistics.compute_percentiles([5, 8]))
self.assertEquals((3, 6.2), statistics.compute_percentiles([2, 3, 7]))
self.assertEquals((5, 8.4), statistics.compute_percentiles([2, 3, 7, 9]))
self.assertEquals((7, 63.6),
statistics.compute_percentiles([2, 3, 7, 9, 100]))
self.assertEquals((39.5, 43.4),
statistics.compute_percentiles([6, 7, 15, 36, 39, 40,
41, 42, 43, 47]))
self.assertEquals((40.0, 47.0),
statistics.compute_percentiles([6, 7, 15, 36, 39, 40,
41, 42, 43, 47, 49]))
self.assertEquals((37.5, 40.5),
statistics.compute_percentiles([7, 15, 36, 39, 40, 41]))
self.assertEquals((39, 42.2),
statistics.compute_percentiles([6, 7, 15, 36, 39, 40,
41, 42, 43]))
def test_compute_percentiles(self):
self.assertTrue(
all(map(math.isnan, statistics.compute_percentiles([]))))
self.assertEquals(statistics.compute_median([5, 8]),
statistics.compute_percentiles([5, 8], [50])[0])
self.assertEquals(statistics.compute_median([2, 3, 7]),
statistics.compute_percentiles([2, 3, 7], [50])[0])
# All expected values below agree with Google Docs.
self.assertEquals((42, 42), statistics.compute_percentiles([42]))
self.assertEquals((6.5, 7.7), statistics.compute_percentiles([5, 8]))
self.assertEquals((3, 6.2), statistics.compute_percentiles([2, 3, 7]))
self.assertEquals((5, 8.4),
statistics.compute_percentiles([2, 3, 7, 9]))
self.assertEquals((7, 63.6),
statistics.compute_percentiles([2, 3, 7, 9, 100]))
self.assertEquals((39.5, 43.4),
statistics.compute_percentiles(
[6, 7, 15, 36, 39, 40, 41, 42, 43, 47]))
self.assertEquals((40.0, 47.0),
statistics.compute_percentiles(
[6, 7, 15, 36, 39, 40, 41, 42, 43, 47, 49]))
self.assertEquals(
(37.5, 40.5),
statistics.compute_percentiles([7, 15, 36, 39, 40, 41]))
self.assertEquals(
(39, 42.2),
statistics.compute_percentiles([6, 7, 15, 36, 39, 40, 41, 42, 43]))
