aByes

aByes is a Python package for Bayesian A/B Testing, which supports two main decision rules:

Region Of Practical Equivalence (as in the paper Bayesian estimation supersedes the t-test, J. K. Kruschke, Journal of Experimental Psychology, 2012)
Expected Loss (as discussed in Bayesian A/B Testing at VWO, C. Stucchio)

A lot of the underlying theory is discussed in this blog post.

Installation on a Vertex.ai Workbench

Spin up a new user managed notebook: https://console.cloud.google.com/vertex-ai/workbench/ (TODO: Terraform + docker image, this will replace the install steps below)
Connect to the notebook

Install abyes:

# Open a terminal
git clone https://github.com/cbellei/abyes.git
cd abyes
make init

Restart the notebook kernal (under Kernel menu)
To check that the package has been installed, in the Python shell type:
```
import abyes
```
If everything works correctly, the package will be imported without errors.

Dependencies

aByes is tested on Python 3.5 and depends on NumPy, Scipy, Matplotlib, Pymc3 (see requirements.txt for version

information).

How to use aByes

The main steps to run the analysis of an A/B experiment are:

Aggregate the data for the "A" and "B" variations in a List of numpy arrays
Decide how to do the analysis. Options are: 1. analytic solution; 2. MCMC solution (using PyMC3); 3. compare the analytic and MCMC solutions
Set decision rule. Options are: 1. ROPE method; 2. Expected Loss method
Set parameter to use for the decision. Options are: 1. Lift (difference in means); 2. Effect size

These and many more examples and instructions can be found in this blogpost.

Example

In IPython, type:

import abyes as ab
import numpy as np

data = [np.random.binomial(1, 0.4, size=10000), np.random.binomial(1, 0.5, size=10000)]
exp = ab.AbExp(method='analytic', decision_var = 'lift', rule='rope', rope=(-0.01,0.01), plot=True)
exp.experiment(data)

This will plot the posterior distribution:

It will then give the following result:

*** abyes ***

Method = analytic
Decision Rule = rope
Alpha = 0.95
Rope = (-0.01, 0.01)
Decision Variable = lift

Result is conclusive: B variant is winner!

There are many more examples available in the file example.py, which can be run from the root directory with the command:
```
python abyes/examples/examples.py
```

Limitations

Currently, aByes:

only focuses on conversion rate experiments
allows for only two variants at a time to be tested

These shortcomings may be improved in future versions of aByes. (Feel free to fork the project and make these improvements yourself!)

Licence

Apache License, Version 2.0

yosemsweet/abyes