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https://github.com/MSeifert04/simple_benchmark

A simple benchmarking package including visualization facilities.
https://github.com/MSeifert04/simple_benchmark

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A simple benchmarking package including visualization facilities.

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simple_benchmark

================



A simple benchmarking package including visualization facilities.



The goal of this package is to provide a simple way to compare the performance

of different approaches for different inputs and to visualize the result.



Documentation

-------------



.. image:: https://readthedocs.org/projects/simple-benchmark/badge/?version=stable

   :target: http://simple-benchmark.readthedocs.io/en/stable/?badge=stable

   :alt: Documentation Status



Downloads

---------



.. image:: https://img.shields.io/pypi/v/simple_benchmark.svg

   :target: https://pypi.python.org/pypi/simple_benchmark

   :alt: PyPI Project



.. image:: https://img.shields.io/github/release/MSeifert04/simple_benchmark.svg

   :target: https://github.com/MSeifert04/simple_benchmark/releases

   :alt: GitHub Project



Installation

------------



Using ``pip``:



.. code::



   python -m pip install simple_benchmark



Or installing the most recent version directly from ``git``:



.. code::



   python -m pip install git+https://github.com/MSeifert04/simple_benchmark.git



To utilize the all features of the library (for example visualization) you need to

install the optional dependencies:



- `NumPy `_

- `pandas `_

- `matplotlib `_



Or install them automatically using:



.. code::



   python -m pip install simple_benchmark[optional]



Getting started

---------------



Suppose you want to compare how NumPys sum and Pythons sum perform on lists

of different sizes::



    >>> from simple_benchmark import benchmark

    >>> import numpy as np

    >>> funcs = [sum, np.sum]

    >>> arguments = {i: [1]*i for i in [1, 10, 100, 1000, 10000, 100000]}

    >>> argument_name = 'list size'

    >>> aliases = {sum: 'Python sum', np.sum: 'NumPy sum'}

    >>> b = benchmark(funcs, arguments, argument_name, function_aliases=aliases)



The result can be visualized with ``pandas`` (needs to be installed)::



    >>> b

              Python sum  NumPy sum

    1       9.640884e-08   0.000004

    10      1.726930e-07   0.000004

    100     7.935484e-07   0.000008

    1000    7.040000e-06   0.000042

    10000   6.910000e-05   0.000378

    100000  6.899000e-04   0.003941



Or with ``matplotlib`` (has to be installed too)::



    >>> b.plot()



To save the plotted benchmark as PNG file::



    >>> import matplotlib.pyplot as plt

    >>> plt.savefig('sum_example.png')



.. image:: ./docs/source/sum_example.png



Command-Line interface

----------------------



.. warning::

   The command line interface is highly experimental. It's very likely to

   change its API.



It's an experiment to run it as command-line tool, especially useful if you

want to run it on multiple files and don't want the boilerplate.



File ``sum.py``::



   import numpy as np



   def bench_sum(l, func=sum):  # <-- function name needs to start with "bench_"

      return func(l)



   def bench_numpy_sum(l, func=np.sum):  # <-- using func parameter with the actual function helps

      return np.sum(l)



   def args_list_length():  # <-- function providing the argument starts with "args_"

      for i in [1, 10, 100, 1000, 10000, 100000]:

         yield i, [1] * i



Then run::



    $ python -m simple_benchmark sum.py sum.png



With a similar result:



.. image:: ./docs/source/sum_example_cli.png



Similar packages

----------------



- `perfplot `_ by Nico Schlömer.