timerit 1.0.1

A powerful multiline alternative to timeit

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.

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

Examples

The quick and dirty way just requires one indent.

>>> import math
>>> from timerit import Timerit
>>> for _ in Timerit(num=200, verbose=2):
>>>     math.factorial(10000)
Timing for 200 loops
Timed for: 200 loops, best of 3
    time per loop: best=2.469 ms, mean=2.49 ± 0.037 ms

Use the loop variable as a context manager for more accurate timings or to incorporate a setup phase that is not timed. You can also access properties of the Timerit class to programmatically use results.

>>> import math
>>> from timerit import Timerit
>>> t1 = Timerit(num=200, verbose=2)
>>> for timer in t1:
>>>     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

There is also a simple one-liner that is comparable to 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 synax 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')
    ax.set_xlabel('A better x-variable description')
    ax.set_ylabel('A better y-variable description')