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'''
Created on May 19, 2011
@author: bungeman
'''
import re
import math
class BenchDataPoint:
"""A single data point produced by bench.
(str, str, str, float, {str:str})"""
def __init__(self, bench, config, time_type, time, settings):
self.bench = bench
self.config = config
self.time_type = time_type
self.time = time
self.settings = settings
def __repr__(self):
return "BenchDataPoint(%s, %s, %s, %s, %s)" % (
str(self.bench),
str(self.config),
str(self.time_type),
str(self.time),
str(self.settings),
)
class _ExtremeType(object):
"""Instances of this class compare greater or less than other objects."""
def __init__(self, cmpr, rep):
object.__init__(self)
self._cmpr = cmpr
self._rep = rep
def __cmp__(self, other):
if isinstance(other, self.__class__) and other._cmpr == self._cmpr:
return 0
return self._cmpr
def __repr__(self):
return self._rep
Max = _ExtremeType(1, "Max")
Min = _ExtremeType(-1, "Min")
class _ListAlgorithm(object):
"""Algorithm for selecting the representation value from a given list.
representation is one of 'avg', 'min', 'med', '25th' (average, minimum,
median, 25th percentile)"""
def __init__(self, data, representation=None):
if not representation:
representation = 'avg' # default algorithm is average
self._data = data
self._len = len(data)
if representation == 'avg':
self._rep = sum(self._data) / self._len
else:
self._data.sort()
if representation == 'min':
self._rep = self._data[0]
else:
# for percentiles, we use the value below which x% of values are
# found, which allows for better detection of quantum behaviors.
if representation == 'med':
x = int(round(0.5 * self._len + 0.5))
elif representation == '25th':
x = int(round(0.25 * self._len + 0.5))
else:
raise Exception("invalid representation algorithm %s!" %
representation)
self._rep = self._data[x - 1]
def compute(self):
return self._rep
def parse(settings, lines, representation='avg'):
"""Parses bench output into a useful data structure.
({str:str}, __iter__ -> str) -> [BenchDataPoint]
representation should match one of those defined in class _ListAlgorithm."""
benches = []
current_bench = None
setting_re = '([^\s=]+)(?:=(\S+))?'
settings_re = 'skia bench:((?:\s+' + setting_re + ')*)'
bench_re = 'running bench (?:\[\d+ \d+\] )?\s*(\S+)'
time_re = '(?:(\w*)msecs = )?\s*((?:\d+\.\d+)(?:,\d+\.\d+)*)'
config_re = '(\S+): ((?:' + time_re + '\s+)+)'
for line in lines:
#see if this line is a settings line
settingsMatch = re.search(settings_re, line)
if (settingsMatch):
settings = dict(settings)
for settingMatch in re.finditer(setting_re, settingsMatch.group(1)):
if (settingMatch.group(2)):
settings[settingMatch.group(1)] = settingMatch.group(2)
else:
settings[settingMatch.group(1)] = True
#see if this line starts a new bench
new_bench = re.search(bench_re, line)
if new_bench:
current_bench = new_bench.group(1)
#add configs on this line to the current bench
if current_bench:
for new_config in re.finditer(config_re, line):
current_config = new_config.group(1)
times = new_config.group(2)
for new_time in re.finditer(time_re, times):
current_time_type = new_time.group(1)
iters = [float(i) for i in
new_time.group(2).strip().split(',')]
benches.append(BenchDataPoint(
current_bench
, current_config
, current_time_type
, _ListAlgorithm(iters, representation).compute()
, settings))
return benches
class LinearRegression:
"""Linear regression data based on a set of data points.
([(Number,Number)])
There must be at least two points for this to make sense."""
def __init__(self, points):
n = len(points)
max_x = Min
min_x = Max
Sx = 0.0
Sy = 0.0
Sxx = 0.0
Sxy = 0.0
Syy = 0.0
for point in points:
x = point[0]
y = point[1]
max_x = max(max_x, x)
min_x = min(min_x, x)
Sx += x
Sy += y
Sxx += x*x
Sxy += x*y
Syy += y*y
denom = n*Sxx - Sx*Sx
if (denom != 0.0):
B = (n*Sxy - Sx*Sy) / denom
else:
B = 0.0
a = (1.0/n)*(Sy - B*Sx)
se2 = 0
sB2 = 0
sa2 = 0
if (n >= 3 and denom != 0.0):
se2 = (1.0/(n*(n-2)) * (n*Syy - Sy*Sy - B*B*denom))
sB2 = (n*se2) / denom
sa2 = sB2 * (1.0/n) * Sxx
self.slope = B
self.intercept = a
self.serror = math.sqrt(max(0, se2))
self.serror_slope = math.sqrt(max(0, sB2))
self.serror_intercept = math.sqrt(max(0, sa2))
self.max_x = max_x
self.min_x = min_x
def __repr__(self):
return "LinearRegression(%s, %s, %s, %s, %s)" % (
str(self.slope),
str(self.intercept),
str(self.serror),
str(self.serror_slope),
str(self.serror_intercept),
)
def find_min_slope(self):
"""Finds the minimal slope given one standard deviation."""
slope = self.slope
intercept = self.intercept
error = self.serror
regr_start = self.min_x
regr_end = self.max_x
regr_width = regr_end - regr_start
if slope < 0:
lower_left_y = slope*regr_start + intercept - error
upper_right_y = slope*regr_end + intercept + error
return min(0, (upper_right_y - lower_left_y) / regr_width)
elif slope > 0:
upper_left_y = slope*regr_start + intercept + error
lower_right_y = slope*regr_end + intercept - error
return max(0, (lower_right_y - upper_left_y) / regr_width)
return 0
def CreateRevisionLink(revision_number):
"""Returns HTML displaying the given revision number and linking to
that revision's change page at code.google.com, e.g.
http://code.google.com/p/skia/source/detail?r=2056
"""
return '<a href="http://code.google.com/p/skia/source/detail?r=%s">%s</a>'%(
revision_number, revision_number)
def main():
foo = [[0.0, 0.0], [0.0, 1.0], [0.0, 2.0], [0.0, 3.0]]
LinearRegression(foo)
if __name__ == "__main__":
main()
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