A/B tests (a.k.a. Randomized Controlled Trials or Experiments) have been widely applied in different industries to optimize business processes and user experience. ExpAn (Experiment Analysis) is a Python library developed for the statistical analysis of such experiments and to standardise the data structures used.
The data structures and functionality of ExpAn are generic such that they can be used by both data scientists optimizing a user interface and biologists running wet-lab experiments. The library is also standalone and can be imported and used from within other projects and from the command line.
Major statistical functionalities include:
- feature check
- delta
- subgroup analysis
- trend
To install you can simply run (pip >= 8.1.1 and setuptools >= 21.0.0 are required):
pip install expan
An alternative way to install is it to clone the repo and run:
python2 setup.py build python2 setup.py install
And to test run:
python2 setup.py test
from expan.core.experiment import Experiment
from tests.tests_core.test_data import generate_random_data
exp = Experiment('B', *generate_random_data())
exp.delta()
ExpAn Description - details about the concept of the library and data structures.
ExpAn Introduction - a full jupyter (iPython) notebook. You can view it as slides with jupyter:
sh serve_intro_slides
There may be alternative libraries providing similar functionality, and these should be collected here. Very incomplete list so far...
Not an alternative, the Python part of this is simply a collection of some functions to handle binomial distributions.
We follow PEP8 standards with the following exceptions:
- Use tabs instead of spaces - this allows all individuals to have visual depth of indentation they prefer, without changing the source code at all, and it is simply smaller
We currently use the gitflow workflow. Feature branches are created from
and merged back to the dev branch, and the master branch stores
snapshots/releases of the dev branch.
See also the much simpler github flow here
For the sake of reproducibility, always be sure to work with a release when doing the analysis!
We use semantic versioning (http://semver.org), and the current version of ExpAn is: v0.2.5.
The version is maintained in setup.cfg, and propagated from there to various files
by the bumpversion program. The most important propagation destination is
in version.py where it is held in the string __version__ with
the form:
'{major}.{minor}.{patch}'
The __version__ string and a version() function is imported by
core.__init__ and so is accessible to imported functions in expan.
The version(format_str) function generates version strings of any
form. It can use git's commit count and revision number to generate a
long version string which may be useful for pip versioning? Examples:
NB: caution using this... it won't work if not in the original git
repository.
>>> import core.binning
>>> core.version()
'v0.2.5'
>>> core.version('{major}.{minor}..{commits}')
'0.0..176'
>>> core.version('{commit}')
'a24730a42a4b5ae01bbdb05f6556dedd453c1767'
See: StackExchange 151558
Can use bumpversion to maintain the __version__ in version.py:
$ bumpversion patch or $ bumpversion minor
This will update the version number, create a new tag in git, and commit the changes with a standard commit message.
When you have done this, you must push the commit and new tag to the repository with:
$ git push --tags
We use Travis CI for testing builds and deploying our PyPI package.
A build and test is triggered when a commit is pushed to either
- dev,
- master
- or a pull request branch to dev or master.
A build, test and push to PyPI is triggered when a tagged commit is pushed to master.
If you wish to skip triggering a CI task (for example when you only perform changes on documentation), please include [ci skip] in your commit message.
- parallelization, eg. for the bootstrapping code
- Bayesian updating/early stopping
- multiple comparison correction, definitely relevant for delta and SGA, have to think about how to correct for time dependency in the trend analysis
- implement from_json and to_json methods in the Binning class, in order to convert the Python object to a json format for persisting in the Results metadata and reloading from a script
The MIT License (MIT)
Copyright © [2016] Zalando SE, https://tech.zalando.com
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