Timerit

A powerful multiline alternative to Python's builtin timeit module.

Docs are published at https://timerit.readthedocs.io/en/latest/ but this README and code comments contain a walkthrough.

Github	https://github.com/Erotemic/timerit
Pypi	https://pypi.org/project/timerit
ReadTheDocs	https://timerit.readthedocs.io/en/latest/

Description

Easily do robust timings on existing blocks of code by simply indenting them. There is no need to refactor into a string representation or convert to a single line.

Installation

pip install timerit

Interactive Use

The timerit library provides a succinct API for interactive use:

>>> import timerit
>>> for _ in timerit:
...     sum(range(100000))
Timed for: 288 loops, best of 5
    time per loop: best=616.740 µs, mean=668.933 ± 124.2 µs

Compare to timeit:

$ python -m timeit 'sum(range(100000))'
500 loops, best of 5: 721 usec per loop

By default, any code within the loop will be repeatedly executed until at least 200 ms have elapsed. The timing results are then printed out.

Here's what each of the numbers means:

• "288 loops": The code in the loop was run 288 times before the time limit was reached.
• "best of 5": Consider only the fastest of every 5 measured times, when calculating the mean and standard deviation. The reason for doing this is that you can get slow times if the something in the background is consuming resources, so you're generally only interested in the fastest times. This idea is also described in the timeit docs.
• "best=616.740 µs": How long the fastest iteration took to run. For the reasons described above, this is usually the most consistent number, and the primary number you should focus on.
• "mean=668.933 ± 124.2 µs": The mean and the standard deviation of the "best of 5" iterations. This statistic is usually not as robust or useful as the fastest time, but sometimes its helpful to know if there's high variance.

The loop variable can be used as a context manager to only time a part of each loop (e.g. to make the timings more accurate, or to incorporate a setup phase that is not timed):

>>> for timer in timerit:
...     n = 100 * 1000
...     with timer:
...         sum(range(n))
Timed for: 318 loops, best of 5
    time per loop: best=616.673 µs, mean=617.545 ± 0.9 µs

It is also possible to provide arguments controlling how the timing measurements are made. See the online documentation for more information on these arguments, but the snippet below runs for exactly 100 iterations, instead of however many fit in 200 ms.

>>> for _ in timerit(num=100):
...     sum(range(100000))
Timed for: 100 loops, best of 5
    time per loop: best=616.866 µs, mean=619.120 ± 5.3 µs

Automatic Import

If you want to make timerit even easier to use interactively, you can move the import to the PYTHONSTARTUP file. If defined, this environment variable gives the path to a python script that will be executed just before every interactive session. For example:

$ export PYTHONSTARTUP=~/.pythonrc
$ cat $PYTHONSTARTUP
import timerit
$ python
>>> for _ in timerit:
...     sum(range(100000))
...
Timed for: 59 loops, best of 3
    time per loop: best=2.532 ms, mean=3.309 ± 1.0 ms

Programmatic Use

The timerit library also provides a Timerit class that can be used programmatically.

>>> import math, timerit
>>> for timer in timerit:
>>>     setup_vars = 10000
>>>     with timer:
>>>         math.factorial(setup_vars)
>>> print('t1.total_time = %r' % (t1.total_time,))
Timing for 200 loops
Timed for: 200 loops, best of 3
    time per loop: best=2.064 ms, mean=2.115 ± 0.05 ms
t1.total_time = 0.4427177629695507

A common pattern is to create a single Timerit instance, then to repeatedly "reset" it with different labels to test a number of different algorithms. The labels assigned in this way will be incorporated into the report strings that the Timerit instance produces. The "Benchmark Recipe" below shows an example of this pattern. So do all of the scripts in the examples/ directory.

There is also a simple one-liner that is comparable to timeit's IPython magic:

Compare the timeit version:

>>> %timeit math.factorial(100)
564 ns ± 5.46 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

With the Timerit version:

>>> Timerit(100000).call(math.factorial, 100).print()
Timed for: 1 loops, best of 1
    time per loop: best=4.828 µs, mean=4.828 ± 0.0 µs

How it works

The timerit module defines timerit.Timerit, which is an iterable object that yields timerit.Timer context managers.

>>> import math
>>> from timerit import Timerit
>>> for timer in Timerit(num=200, verbose=2):
>>>     with timer:
>>>         math.factorial(10000)

The timer context manager measures how much time the body of it takes by "tic"-ing on __enter__ and "toc"-ing on __exit__. The parent Timerit object has access to the context manager, so it is able to read its measurement. These measurements are stored and then we compute some statistics on them. Notably the minimum, mean, and standard-deviation of grouped (batched) running times.

Using the with statement inside the loop is nice because you can run untimed setup code before you enter the context manager.

In the case where no setup code is required, a more concise version of the syntax is available.

>>> import math
>>> from timerit import Timerit
>>> for _ in Timerit(num=200, verbose=2):
>>>     math.factorial(10000)

If the context manager is never called, the Timerit object detects this and the measurement is made in the __iter__ method in the Timerit object itself. I believe that this concise method contains slightly more overhead than the with-statement version. (I have seen evidence that this might actually be more accurate, but it needs further testing).

Benchmark Recipe

import ubelt as ub
import pandas as pd
import timerit

def method1(x):
    ret = []
    for i in range(x):
        ret.append(i)
    return ret

def method2(x):
    ret = [i for i in range(x)]
    return ret

method_lut = locals()  # can populate this some other way

ti = timerit.Timerit(100, bestof=10, verbose=2)

basis = {
    'method': ['method1', 'method2'],
    'x': list(range(7)),
    # 'param_name': [param values],
}
grid_iter = ub.named_product(basis)

# For each variation of your experiment, create a row.
rows = []
for params in grid_iter:
    key = ub.repr2(params, compact=1, si=1)
    kwargs = params.copy()
    method_key = kwargs.pop('method')
    method = method_lut[method_key]
    # Timerit will run some user-specified number of loops.
    # and compute time stats with similar methodology to timeit
    for timer in ti.reset(key):
        # Put any setup logic you dont want to time here.
        # ...
        with timer:
            # Put the logic you want to time here
            method(**kwargs)
    row = {
        'mean': ti.mean(),
        'min': ti.min(),
        'key': key,
        **params,
    }
    rows.append(row)

# The rows define a long-form pandas data array.
# Data in long-form makes it very easy to use seaborn.
data = pd.DataFrame(rows)
print(data)

plot = True
if plot:
    # import seaborn as sns
    # kwplot autosns works well for IPython and script execution.
    # not sure about notebooks.
    import kwplot
    sns = kwplot.autosns()

    # Your variables may change
    ax = kwplot.figure(fnum=1, doclf=True).gca()
    sns.lineplot(data=data, x='x', y='min', hue='method', marker='o', ax=ax)
    ax.set_title('Benchmark Name')
    ax.set_xlabel('x-variable description')
    ax.set_ylabel('y-variable description')

Related Work

• timeit: The builtin timeit module for profiling single statements.
• line_profiler: Line-by-line profiling.
• ubelt: This library was original developed as a component of ubelt, but has since become a standalone module.