Измерение времени выполнения кода в Python
Возникает задача измерить время, которое занимает выполнение определенного участка кода или функции в Python. Например, есть функция, которая выполняет сложные вычисления, и нужно выяснить, сколько времени она занимает.
Модуль time
Одним из способов измерения времени выполнения кода в Python является использование модуля time . Этот модуль предоставляет функцию time() , которая возвращает текущее время в секундах с момента, который называется «эпохой». Обычно это 00:00 1 января 1970 года.
import time # начальное время start_time = time.time() # код, время выполнения которого нужно измерить for i in range(0, 1000000): pass # конечное время end_time = time.time() # разница между конечным и начальным временем elapsed_time = end_time - start_time print('Elapsed time: ', elapsed_time) Модуль timeit
Другим способом является использование модуля timeit . Этот модуль предоставляет более точный способ измерения времени выполнения кода, поскольку он автоматически учитывает время, которое занимает вызов функции time() и другие факторы. Вместо этого timeit многократно выполняет код и возвращает среднее время выполнения.
import timeit # код, время выполнения которого нужно измерить code_to_test = """ a = range(1000000) b = [] for i in a: b.append(i*2) """ # вычисление времени выполнения кода elapsed_time = timeit.timeit(code_to_test, number=100)/100 print('Elapsed time: ', elapsed_time) В приведенном примере timeit() выполняет код 100 раз и возвращает общее время выполнения. Затем это время делится на 100, чтобы получить среднее время выполнения.
timeit — Measure execution time of small code snippets¶
This module provides a simple way to time small bits of Python code. It has both a Command-Line Interface as well as a callable one. It avoids a number of common traps for measuring execution times. See also Tim Peters’ introduction to the “Algorithms” chapter in the second edition of Python Cookbook, published by O’Reilly.
Basic Examples¶
The following example shows how the Command-Line Interface can be used to compare three different expressions:
$ python3 -m timeit '"-".join(str(n) for n in range(100))' 10000 loops, best of 5: 30.2 usec per loop $ python3 -m timeit '"-".join([str(n) for n in range(100)])' 10000 loops, best of 5: 27.5 usec per loop $ python3 -m timeit '"-".join(map(str, range(100)))' 10000 loops, best of 5: 23.2 usec per loop
This can be achieved from the Python Interface with:
>>> import timeit >>> timeit.timeit('"-".join(str(n) for n in range(100))', number=10000) 0.3018611848820001 >>> timeit.timeit('"-".join([str(n) for n in range(100)])', number=10000) 0.2727368790656328 >>> timeit.timeit('"-".join(map(str, range(100)))', number=10000) 0.23702679807320237
A callable can also be passed from the Python Interface :
>>> timeit.timeit(lambda: "-".join(map(str, range(100))), number=10000) 0.19665591977536678
Note however that timeit() will automatically determine the number of repetitions only when the command-line interface is used. In the Examples section you can find more advanced examples.
Python Interface¶
The module defines three convenience functions and a public class:
timeit. timeit ( stmt=’pass’ , setup=’pass’ , timer= , number=1000000 , globals=None ) ¶
Create a Timer instance with the given statement, setup code and timer function and run its timeit() method with number executions. The optional globals argument specifies a namespace in which to execute the code.
Changed in version 3.5: The optional globals parameter was added.
Create a Timer instance with the given statement, setup code and timer function and run its repeat() method with the given repeat count and number executions. The optional globals argument specifies a namespace in which to execute the code.
Changed in version 3.5: The optional globals parameter was added.
Changed in version 3.7: Default value of repeat changed from 3 to 5.
The default timer, which is always time.perf_counter() .
Changed in version 3.3: time.perf_counter() is now the default timer.
Class for timing execution speed of small code snippets.
The constructor takes a statement to be timed, an additional statement used for setup, and a timer function. Both statements default to ‘pass’ ; the timer function is platform-dependent (see the module doc string). stmt and setup may also contain multiple statements separated by ; or newlines, as long as they don’t contain multi-line string literals. The statement will by default be executed within timeit’s namespace; this behavior can be controlled by passing a namespace to globals.
To measure the execution time of the first statement, use the timeit() method. The repeat() and autorange() methods are convenience methods to call timeit() multiple times.
The execution time of setup is excluded from the overall timed execution run.
The stmt and setup parameters can also take objects that are callable without arguments. This will embed calls to them in a timer function that will then be executed by timeit() . Note that the timing overhead is a little larger in this case because of the extra function calls.
Changed in version 3.5: The optional globals parameter was added.
Time number executions of the main statement. This executes the setup statement once, and then returns the time it takes to execute the main statement a number of times, measured in seconds as a float. The argument is the number of times through the loop, defaulting to one million. The main statement, the setup statement and the timer function to be used are passed to the constructor.
By default, timeit() temporarily turns off garbage collection during the timing. The advantage of this approach is that it makes independent timings more comparable. The disadvantage is that GC may be an important component of the performance of the function being measured. If so, GC can be re-enabled as the first statement in the setup string. For example:
timeit.Timer('for i in range(10): oct(i)', 'gc.enable()').timeit()
Automatically determine how many times to call timeit() .
This is a convenience function that calls timeit() repeatedly so that the total time >= 0.2 second, returning the eventual (number of loops, time taken for that number of loops). It calls timeit() with increasing numbers from the sequence 1, 2, 5, 10, 20, 50, … until the time taken is at least 0.2 second.
If callback is given and is not None , it will be called after each trial with two arguments: callback(number, time_taken) .
This is a convenience function that calls the timeit() repeatedly, returning a list of results. The first argument specifies how many times to call timeit() . The second argument specifies the number argument for timeit() .
It’s tempting to calculate mean and standard deviation from the result vector and report these. However, this is not very useful. In a typical case, the lowest value gives a lower bound for how fast your machine can run the given code snippet; higher values in the result vector are typically not caused by variability in Python’s speed, but by other processes interfering with your timing accuracy. So the min() of the result is probably the only number you should be interested in. After that, you should look at the entire vector and apply common sense rather than statistics.
Changed in version 3.7: Default value of repeat changed from 3 to 5.
Helper to print a traceback from the timed code.
t = Timer(. ) # outside the try/except try: t.timeit(. ) # or t.repeat(. ) except Exception: t.print_exc()
The advantage over the standard traceback is that source lines in the compiled template will be displayed. The optional file argument directs where the traceback is sent; it defaults to sys.stderr .
Command-Line Interface¶
When called as a program from the command line, the following form is used:
python -m timeit [-n N] [-r N] [-u U] [-s S] [-h] [statement . ]
Where the following options are understood:
how many times to execute ‘statement’
how many times to repeat the timer (default 5)
statement to be executed once initially (default pass )
measure process time, not wallclock time, using time.process_time() instead of time.perf_counter() , which is the default
specify a time unit for timer output; can select nsec , usec , msec , or sec
print raw timing results; repeat for more digits precision
print a short usage message and exit
A multi-line statement may be given by specifying each line as a separate statement argument; indented lines are possible by enclosing an argument in quotes and using leading spaces. Multiple -s options are treated similarly.
If -n is not given, a suitable number of loops is calculated by trying increasing numbers from the sequence 1, 2, 5, 10, 20, 50, … until the total time is at least 0.2 seconds.
default_timer() measurements can be affected by other programs running on the same machine, so the best thing to do when accurate timing is necessary is to repeat the timing a few times and use the best time. The -r option is good for this; the default of 5 repetitions is probably enough in most cases. You can use time.process_time() to measure CPU time.
There is a certain baseline overhead associated with executing a pass statement. The code here doesn’t try to hide it, but you should be aware of it. The baseline overhead can be measured by invoking the program without arguments, and it might differ between Python versions.
Examples¶
It is possible to provide a setup statement that is executed only once at the beginning:
$ python -m timeit -s 'text = "sample string"; char = "g"' 'char in text' 5000000 loops, best of 5: 0.0877 usec per loop $ python -m timeit -s 'text = "sample string"; char = "g"' 'text.find(char)' 1000000 loops, best of 5: 0.342 usec per loop
In the output, there are three fields. The loop count, which tells you how many times the statement body was run per timing loop repetition. The repetition count (‘best of 5’) which tells you how many times the timing loop was repeated, and finally the time the statement body took on average within the best repetition of the timing loop. That is, the time the fastest repetition took divided by the loop count.
>>> import timeit >>> timeit.timeit('char in text', setup='text = "sample string"; char = "g"') 0.41440500499993504 >>> timeit.timeit('text.find(char)', setup='text = "sample string"; char = "g"') 1.7246671520006203
The same can be done using the Timer class and its methods:
>>> import timeit >>> t = timeit.Timer('char in text', setup='text = "sample string"; char = "g"') >>> t.timeit() 0.3955516149999312 >>> t.repeat() [0.40183617287970225, 0.37027556854118704, 0.38344867356679524, 0.3712595970846668, 0.37866875250654886]
The following examples show how to time expressions that contain multiple lines. Here we compare the cost of using hasattr() vs. try / except to test for missing and present object attributes:
$ python -m timeit 'try:' ' str.__bool__' 'except AttributeError:' ' pass' 20000 loops, best of 5: 15.7 usec per loop $ python -m timeit 'if hasattr(str, "__bool__"): pass' 50000 loops, best of 5: 4.26 usec per loop $ python -m timeit 'try:' ' int.__bool__' 'except AttributeError:' ' pass' 200000 loops, best of 5: 1.43 usec per loop $ python -m timeit 'if hasattr(int, "__bool__"): pass' 100000 loops, best of 5: 2.23 usec per loop
>>> import timeit >>> # attribute is missing >>> s = """\ . try: . str.__bool__ . except AttributeError: . pass . """ >>> timeit.timeit(stmt=s, number=100000) 0.9138244460009446 >>> s = "if hasattr(str, '__bool__'): pass" >>> timeit.timeit(stmt=s, number=100000) 0.5829014980008651 >>> >>> # attribute is present >>> s = """\ . try: . int.__bool__ . except AttributeError: . pass . """ >>> timeit.timeit(stmt=s, number=100000) 0.04215312199994514 >>> s = "if hasattr(int, '__bool__'): pass" >>> timeit.timeit(stmt=s, number=100000) 0.08588060699912603
To give the timeit module access to functions you define, you can pass a setup parameter which contains an import statement:
def test(): """Stupid test function""" L = [i for i in range(100)] if __name__ == '__main__': import timeit print(timeit.timeit("test()", setup="from __main__ import test"))
Another option is to pass globals() to the globals parameter, which will cause the code to be executed within your current global namespace. This can be more convenient than individually specifying imports:
def f(x): return x**2 def g(x): return x**4 def h(x): return x**8 import timeit print(timeit.timeit('[func(42) for func in (f,g,h)]', globals=globals()))