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benchit-0.0.6rc0
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مشاهده بیشترتوضیحات
Benchmarking tools for Python
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | benchit-0.0.6rc0 |
| نام | benchit |
| نسخه کتابخانه | 0.0.6rc0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Divakar Roy |
| ایمیل نویسنده | droygatech@gmail.com |
| آدرس صفحه اصلی | https://github.com/droyed/benchit |
| آدرس اینترنتی | https://pypi.org/project/benchit/ |
| مجوز | MIT |
benchit (BENCHmark IT!)
=======================
|Py-Versions| |Py-LatestVersion| |GitHub-Releases| |PyPI-Downloads| |GitHub-License|
Tools to benchmark Python solutions on runtime performance and visualize. Based on ``timeit``, it primarily aims to functionally simulate the `timeit <https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit>`__ behaviour and hence the name! This facilitates benchmarking on multiple datasets and solutions.
Documentation
-------------
|Docs|
Installation
------------
Latest PyPI stable release :
.. code:: sh
pip install benchit
Pull latest development release on GitHub and install in the current directory :
.. code:: sh
pip install -e git+https://github.com/droyed/benchit.git@master#egg=benchit
Getting started
^^^^^^^^^^^^^^^
Consider a setup to compare NumPy ufuncs - `sum <https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html>`__, `prod <https://docs.scipy.org/doc/numpy/reference/generated/numpy.prod.html>`__, `max <https://docs.scipy.org/doc/numpy/reference/generated/numpy.amax.html>`__ on arrays varying in their sizes. To keep it simple, let's consider ``1D`` arrays. Thus, we would have :
.. code-block:: python
>>> import numpy as np
>>> funcs = [np.sum,np.prod,np.max]
>>> inputs = [np.random.rand(i) for i in 10**np.arange(5)]
>>> import benchit
>>> t = benchit.timings(funcs, inputs)
It's a *dataframe-like* object and as such we can plot it. It automatically adds in specs into the title area to convey all of available benchmarking info :
.. code-block:: python
>>> t.plot(logy=True, logx=True)
|readme_1_timings|
Multiple arguments
^^^^^^^^^^^^^^^^^^
Let's consider a setup where functions accept more than one argument. Let's take the case of computing `euclidean distances <https://en.wikipedia.org/wiki/Euclidean_distance>`__ between two ``2D`` arrays. We will feed in arrays with varying number of rows and 3 columns to represent data in ``3D`` Cartesian coordinate system and benchmark two commonly used functions in Python.
.. code-block:: python
>>> from sklearn.metrics.pairwise import pairwise_distances
>>> from scipy.spatial.distance import cdist
>>> fns = [cdist, pairwise_distances]
>>> import numpy as np
>>> in_ = {n:[np.random.rand(n,3), np.random.rand(n,3)] for n in [10,100,500,1000,4000]}
>>> t = benchit.timings(fns, in_, multivar=True, input_name='Array-length')
>>> t.plot(logx=True)
|readme_2_timings|
Multiple arguments with groupings
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We will extend the previous example to make the second argument a variable too and study the trend as we vary the number of columns, resulting in subplots.
.. code-block:: python
# Get benchmarking object (dataframe-like) and plot results
>>> R = np.random.rand
>>> in_ = {(n,W):[R(n,W), R(n,W)] for n in [10, 100, 500, 1000] for W in [3, 20, 50, 100]}
>>> t = benchit.timings(fns, in_, multivar=True, input_name=['nrows', 'ncols'])
>>> t.plot(logx=True, sp_ncols=2, sp_argID=0, sp_sharey='g')
For plotting, we are using number of rows as the x-axis base.
|readme_3_timings|
Use ``sp_argID=1`` to switch-over to use number of cols as the x-axis base instead.
Single argument with groupings
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Let's manufacture a simple forward-filling scheme based on indices of `True` values in a boolean-array :
.. code-block:: python
# Functions
def repeat(b):
idx = np.flatnonzero(np.r_[b,True])
return np.repeat(idx[:-1], np.diff(idx))
def maxaccum(b):
return np.maximum.accumulate(np.where(b,np.arange(len(b)), 0))
in_ = {(n,sf): np.random.rand(n)<(100-sf)/100. for n in [100,1000,10000,100000,1000000] for sf in [20, 40, 60, 80, 90, 95]}
t = benchit.timings([repeat, maxaccum], in_, input_name=['Array-length','Sparseness %'])
t.plot(logx=True, sp_ncols=2, save='singlegrp_id0_ffillmask_timings.png')
|readme_4_timings|
Quick Tips
----------
**1. Plotting on notebooks?**
Use ``benchit.setparams(environ='notebook')`` before plotting. Check out `sample notebook run <https://github.com/droyed/benchit/blob/master/docs/source/PlotDemo-NotebookEnv.ipynb>`__.
**2. Get a quick glance into the benchmarking trend before the actual one**
Use ``benchit.setparams(rep=1)`` before plotting. Then, use ``benchit.setparams()`` for a proper benchmarking.
**3. Get a quicker glance into plot layout and vague benchmarking trend before the actual one**
Use ``benchit.setparams(timeout=1e-5, rep=1)`` before plotting. Then, use ``benchit.setparams()`` for a proper benchmarking.
**4. Working with multi-variable datasets to study trend w.r.t. each argument?**
Use nested loops to set-up input datasets as shown earlier. More information is available in documentation.
As a general rule, it's advisable to work on Python ``3.6`` or newer for better plotting experience.
.. |Docs| image:: https://readthedocs.org/projects/benchit/badge/?version=latest
:target: https://benchit.readthedocs.io/en/latest/?badge=latest
.. |GitHub-License| image:: https://img.shields.io/github/license/droyed/benchit
:target: https://github.com/droyed/benchit/blob/master/LICENSE
.. |GitHub-Releases| image:: https://img.shields.io/github/v/release/droyed/benchit
:target: https://github.com/droyed/benchit/releases/latest
.. |PyPI-Downloads| image:: https://img.shields.io/pypi/dm/benchit.svg?label=pypi%20downloads&logo=PyPI&logoColor=white
:target: https://pypi.org/project/benchit
.. |Py-LatestVersion| image:: https://img.shields.io/pypi/v/benchit.svg
:target: https://pypi.org/project/benchit
.. |Py-Versions| image:: https://img.shields.io/pypi/pyversions/benchit.svg?logo=python&logoColor=white
:target: https://pypi.org/project/benchit
.. |readme_1_timings| image:: https://raw.githubusercontent.com/droyed/benchit/master/docs/source/readme_1_timings.png
.. |readme_2_timings| image:: https://raw.githubusercontent.com/droyed/benchit/master/docs/source/readme_2_timings.png
.. |readme_3_timings| image:: https://raw.githubusercontent.com/droyed/benchit/master/docs/source/multigrp_id0_euclidean_timings_readme.png
.. |readme_4_timings| image:: https://raw.githubusercontent.com/droyed/benchit/master/docs/source/singlegrp_id0_ffillmask_timings.png
نحوه نصب
نصب پکیج whl benchit-0.0.6rc0:
pip install benchit-0.0.6rc0.whl
نصب پکیج tar.gz benchit-0.0.6rc0:
pip install benchit-0.0.6rc0.tar.gz