不是像这样只运行一次配置文件:
import cProfile
def do_heavy_lifting():
for i in range(100):
print('hello')
profiller = cProfile.Profile()
profiller.enable()
do_heavy_lifting()
profiller.disable()
profiller.print_stats(sort='time')
配置文件结果如下:
502 function calls in 0.000 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
100 0.000 0.000 0.000 0.000 {built-in method builtins.print}
200 0.000 0.000 0.000 0.000 cp1252.py:18(encode)
200 0.000 0.000 0.000 0.000 {built-in method _codecs.charmap_encode}
1 0.000 0.000 0.000 0.000 test.py:2(do_heavy_lifting)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
我想运行几次并打印平均结果,以提高准确性。
这是我想到的初始脚本食谱:
import cProfile
def do_heavy_lifting():
for i in range(100):
print('hello')
def best_of_profillings(target_profile_function, count):
profile_results = []
for index in range(count):
profiller = cProfile.Profile()
profiller.enable()
target_profile_function()
profiller.disable()
profile_results.append(profiller)
profile_results /= count
return profile_results
heavy_lifting_result = best_of_profillings(do_heavy_lifting, 10)
heavy_lifting_result.print_stats(sort='time')
运行此命令后,它应该像第一个版本一样显示结果,但区别在于它们是几次运行的平均值,而不是一次运行。
草稿脚本仍然缺少profile_results /= count
部分,在所有迭代之后,我将获得所有计算结果并创建平均结果,并始终将其显示在屏幕上:
502 function calls in 0.000 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
100 0.000 0.000 0.000 0.000 {built-in method builtins.print}
200 0.000 0.000 0.000 0.000 cp1252.py:18(encode)
200 0.000 0.000 0.000 0.000 {built-in method _codecs.charmap_encode}
1 0.000 0.000 0.000 0.000 test.py:2(do_heavy_lifting)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
答案 0 :(得分:0)
我设法使用功能average()
创建了以下代码。我打开了pstats
的实现,发现有一个名为Stats.add()
的函数,它似乎只是将结果连接到当前对象中:https://docs.python.org/3.7/library/profile.html#pstats.Stats.add
import io
import pstats
import cProfile
def do_heavy_lifting():
for i in range(100):
print('hello')
def average(stats, count):
stats.total_calls /= count
stats.prim_calls /= count
stats.total_tt /= count
for func, source in stats.stats.items():
cc, nc, tt, ct, callers = source
stats.stats[func] = ( cc/count, nc/count, tt/count, ct/count, callers )
return stats
def best_of_profillings(target_profile_function, count):
output_stream = io.StringIO()
profiller_status = pstats.Stats( stream=output_stream )
for index in range(count):
profiller = cProfile.Profile()
profiller.enable()
target_profile_function()
profiller.disable()
profiller_status.add( profiller )
print( 'Profiled', '%.3f' % profiller_status.total_tt, 'seconds at', index,
'for', target_profile_function.__name__, flush=True )
average( profiller_status, count )
profiller_status.sort_stats( "time" )
profiller_status.print_stats()
return "\nProfile results for %s\n%s" % (
target_profile_function.__name__, output_stream.getvalue() )
heavy_lifting_result = best_of_profillings( do_heavy_lifting, 10 )
print( heavy_lifting_result )
结果:
Profile results for do_heavy_lifting
102.0 function calls in 0.001 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
100.0 0.001 0.000 0.001 0.000 {built-in method builtins.print}
1.0 0.000 0.000 0.001 0.001 D:\test.py:5(do_heavy_lifting)
1.0 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}