Bayesian A/B testing
bayesian_testing is a small package for a quick evaluation of A/B (or A/B/C/...) tests using Bayesian approach.
The package currently supports these data inputs:
- binary data (
[0, 1, 0, ...]) - convenient for conversion-like A/B testing - normal data with unknown variance - convenient for normal data A/B testing
- delta-lognormal data (lognormal data with zeros) - convenient for revenue-like A/B testing
The core evaluation metric of the approach is Probability of Being Best (i.e. "being larger" from data point of view) which is calculated using simulations from posterior distributions (considering given data).
Installation
bayesian_testing can be installed using pip:
pip install bayesian_testing
Alternatively, you can clone the repository and use poetry manually:
cd bayesian_testing
pip install poetry
poetry install
poetry shell
Basic Usage
The primary features are BinaryDataTest, NormalDataTest and DeltaLognormalDataTest classes.
In all cases, there are two methods to insert data:
add_variant_data- adding raw data for a variant as a list of numbers (or numpy 1-D array)add_variant_data_agg- adding aggregated variant data (this can be practical for large data, as the aggregation can be done on a database level)
Both methods for adding data are allowing specification of prior distribution using default parameters (see details in respective docstrings). Default prior setup should be sufficient for most of the cases (e.g. in cases with unknown priors or large amounts of data).
To get the results of the test, simply call method evaluate, or probabs_of_being_best for returning just the probabilities.
Probabilities of being best are approximated using simulations, hence evaluate can return slightly different values for different runs. To stabilize it, you can set sim_count parameter of evaluate to higher value (default value is 20K), or even use seed parameter to fix it completely.
BinaryDataTest
Class for Bayesian A/B test for binary-like data (e.g. conversions, successes, etc.).
import numpy as np
from bayesian_testing.experiments import BinaryDataTest
# generating some random data
rng = np.random.default_rng(52)
# random 1x1500 array of 0/1 data with 5.2% probability for 1:
data_a = rng.binomial(n=1, p=0.052, size=1500)
# random 1x1200 array of 0/1 data with 6.7% probability for 1:
data_b = rng.binomial(n=1, p=0.067, size=1200)
# initialize a test
test = BinaryDataTest()
# add variant using raw data (arrays of zeros and ones):
test.add_variant_data("A", data_a)
test.add_variant_data("B", data_b)
# priors can be specified like this (default for this test is a=b=1/2):
# test.add_variant_data("B", data_b, a_prior=1, b_prior=20)
# add variant using aggregated data (same as raw data with 950 zeros and 50 ones):
test.add_variant_data_agg("C", totals=1000, positives=50)
# evaluate test
test.evaluate()
[{'variant': 'A',
'totals': 1500,
'positives': 80,
'conv_rate': 0.05333,
'prob_being_best': 0.06625},
{'variant': 'B',
'totals': 1200,
'positives': 80,
'conv_rate': 0.06667,
'prob_being_best': 0.89005},
{'variant': 'C',
'totals': 1000,
'positives': 50,
'conv_rate': 0.05,
'prob_being_best': 0.0437}]
NormalDataTest
Class for Bayesian A/B test for normal data.
import numpy as np
from bayesian_testing.experiments import NormalDataTest
# generating some random data
rng = np.random.default_rng(21)
data_a = rng.normal(7.2, 2, 1000)
data_b = rng.normal(7.1, 2, 800)
data_c = rng.normal(7.0, 4, 500)
# initialize a test
test = NormalDataTest()
# add variant using raw data:
test.add_variant_data("A", data_a)
test.add_variant_data("B", data_b)
# test.add_variant_data("C", data_c)
# add variant using aggregated data:
test.add_variant_data_agg("C", len(data_c), sum(data_c), sum(np.square(data_c)))
# evaluate test
test.evaluate(sim_count=20000, seed=52)
[{'variant': 'A',
'totals': 1000,
'sum_values': 7294.67901,
'avg_values': 7.29468,
'prob_being_best': 0.1707},
{'variant': 'B',
'totals': 800,
'sum_values': 5685.86168,
'avg_values': 7.10733,
'prob_being_best': 0.00125},
{'variant': 'C',
'totals': 500,
'sum_values': 3736.91581,
'avg_values': 7.47383,
'prob_being_best': 0.82805}]
DeltaLognormalDataTest
Class for Bayesian A/B test for delta-lognormal data (log-normal with zeros). Delta-lognormal data is typical case of revenue per session data where many sessions have 0 revenue but non-zero values are positive numbers with possible log-normal distribution. To handle this data, the calculation is combining binary Bayes model for zero vs non-zero "conversions" and log-normal model for non-zero values.
import numpy as np
from bayesian_testing.experiments import DeltaLognormalDataTest
test = DeltaLognormalDataTest()
data_a = [7.1, 0.3, 5.9, 0, 1.3, 0.3, 0, 0, 0, 0, 0, 1.5, 2.2, 0, 4.9, 0, 0, 0, 0, 0]
data_b = [4.0, 0, 3.3, 19.3, 18.5, 0, 0, 0, 12.9, 0, 0, 0, 0, 0, 0, 0, 0, 3.7, 0, 0]
# adding variant using raw data
test.add_variant_data("A", data_a)
# alternatively, variant can be also added using aggregated data:
test.add_variant_data_agg(
name="B",
totals=len(data_b),
positives=sum(x > 0 for x in data_b),
sum_values=sum(data_b),
sum_logs=sum([np.log(x) for x in data_b if x > 0]),
sum_logs_2=sum([np.square(np.log(x)) for x in data_b if x > 0])
)
test.evaluate(seed=21)
[{'variant': 'A',
'totals': 20,
'positives': 8,
'sum_values': 23.5,
'avg_values': 1.175,
'avg_positive_values': 2.9375,
'prob_being_best': 0.18915},
{'variant': 'B',
'totals': 20,
'positives': 6,
'sum_values': 61.7,
'avg_values': 3.085,
'avg_positive_values': 10.28333,
'prob_being_best': 0.81085}]
Development
To set up development environment use Poetry and pre-commit:
pip install poetry
poetry install
poetry run pre-commit install
Roadmap
Test classes to be added:
PoissonDataTestExponentialDataTest
Metrics to be added:
Expected LossPotential Value Remaining
References
bayesian_testingpackage itself is dependent only on numpy package.- Work on this package (including default priors selection) was inspired mainly by Coursera course Bayesian Statistics: From Concept to Data Analysis.
4k Jan 5, 2023
1.2k Jan 3, 2023
3k Jan 4, 2023
909 Dec 15, 2022
3.5k Jan 4, 2023
139 Sep 21, 2022
1.6k Dec 30, 2022
122 Nov 22, 2022
802 Dec 31, 2022
64 Dec 12, 2022
20.4k Jan 04, 2023
1 Nov 29, 2021
4 Mar 30, 2022
2 Jun 17, 2022
65 Nov 10, 2022
9 Dec 09, 2022
2 Jan 31, 2022
4 Nov 27, 2022
1 Nov 01, 2021
2.2k Jan 03, 2023
276 Mar 03, 2021
1 Dec 24, 2021
8 Jul 07, 2022
4 Jul 23, 2022
871 Dec 25, 2022
4 Jan 30, 2022
3 Aug 04, 2021
10 Jul 21, 2022
25.7k Jan 04, 2023