do_benchmark.py 文件源码

def measure_pattern_time_v2(iteration_number, size, pattern):
  gw      = execnet.makegateway("popen//python=python2.7")
  channel = gw.remote_exec("""
from nltk.corpus import brown
words = brown.words()[:%s]
text = ' '.join(words)
from pattern.en import parsetree
text_tree = parsetree(text,
 tokenize = True,         # Split punctuation marks from words?
     tags = True,         # Parse part-of-speech tags? (NN, JJ, ...)
   chunks = False,         # Parse chunks? (NP, VP, PNP, ...)
relations = False,        # Parse chunk relations? (-SBJ, -OBJ, ...)
  lemmata = False,        # Parse lemmata? (ate => eat)
 encoding = 'utf-8',       # Input string encoding.
   tagset = None)         # Penn Treebank II (default) or UNIVERSAL.
from pattern.search import search
def measure_pattern_search():
  global pattern_search_result    #Make measure_me able to modify the value
  pattern_search_result = search("%s", text_tree)
from timeit import Timer
pattern_search_time = Timer(measure_pattern_search)
def pattern_search_timeit():
  runtimes = [pattern_search_time.timeit(number=1) for i in range (0, %s)]
  average = sum(runtimes)/len(runtimes)
#  return ''.join(['timit: #runs=', str(%s), ' ; average=', str(average),' ; min=', str(min(runtimes))])
  return [runtimes, average, min(runtimes)]
channel.send(pattern_search_timeit())
  """ % (size, pattern, iteration_number, iteration_number))
  channel.send([])
  return channel.receive()