| ''' |
| Created on May 19, 2011 |
| |
| @author: bungeman |
| ''' |
| |
| import re |
| import math |
| |
| # bench representation algorithm constant names |
| ALGORITHM_AVERAGE = 'avg' |
| ALGORITHM_MEDIAN = 'med' |
| ALGORITHM_MINIMUM = 'min' |
| ALGORITHM_25TH_PERCENTILE = '25th' |
| |
| 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 the ALGORITHM_XXX representation types.""" |
| def __init__(self, data, representation=None): |
| if not representation: |
| representation = ALGORITHM_AVERAGE # default algorithm |
| self._data = data |
| self._len = len(data) |
| if representation == ALGORITHM_AVERAGE: |
| self._rep = sum(self._data) / self._len |
| else: |
| self._data.sort() |
| if representation == ALGORITHM_MINIMUM: |
| 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 == ALGORITHM_MEDIAN: |
| x = int(round(0.5 * self._len + 0.5)) |
| elif representation == ALGORITHM_25TH_PERCENTILE: |
| 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 _ParseAndStoreTimes(config_re, time_re, line, bench, dic, |
| representation=None): |
| """Parses given bench time line with regex and adds data to the given dic. |
| config_re: regular expression for parsing the config line. |
| time_re: regular expression for parsing bench time. |
| line: input string line to parse. |
| bench: name of bench for the time values. |
| dic: dictionary to store bench values. See bench_dic in parse() below. |
| representation: should match one of the ALGORITHM_XXX types.""" |
| |
| for config in re.finditer(config_re, line): |
| current_config = config.group(1) |
| if config_re.startswith(' tile_'): # per-tile bench, add name prefix |
| current_config = 'tile_' + current_config |
| times = 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(',')] |
| dic.setdefault(bench, {}).setdefault(current_config, {}).setdefault( |
| current_time_type, []).append(_ListAlgorithm( |
| iters, representation).compute()) |
| |
| def parse(settings, lines, representation=None): |
| """Parses bench output into a useful data structure. |
| |
| ({str:str}, __iter__ -> str) -> [BenchDataPoint] |
| representation is one of the ALGORITHM_XXX types.""" |
| |
| benches = [] |
| current_bench = None |
| bench_dic = {} # [bench][config][time_type] -> [list of bench values] |
| 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+)*)' |
| # non-per-tile benches have configs that don't end with ']' |
| config_re = '(\S+[^\]]): ((?:' + time_re + '\s+)+)' |
| # per-tile bench lines are in the following format. Note that there are |
| # non-averaged bench numbers in separate lines, which we ignore now due to |
| # their inaccuracy. |
| tile_re = (' tile_(\S+): tile \[\d+,\d+\] out of \[\d+,\d+\] <averaged>:' |
| ' ((?:' + 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 bench_dic |
| if current_bench: |
| for regex in [config_re, tile_re]: |
| _ParseAndStoreTimes(regex, time_re, line, current_bench, |
| bench_dic, representation) |
| |
| # append benches to list, use the total time as final bench value. |
| for bench in bench_dic: |
| for config in bench_dic[bench]: |
| for time_type in bench_dic[bench][config]: |
| benches.append(BenchDataPoint( |
| bench, |
| config, |
| time_type, |
| sum(bench_dic[bench][config][time_type]), |
| 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() |
