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Bayesian A/B test code
from matplotlib import use
from pylab import *
from scipy.stats import beta, norm, uniform
from random import random
from numpy import *
import numpy as np
import os
# Input data
prior_params = [ (1, 1), (1,1) ]
threshold_of_caring = 0.001
N = array([ 200, 204 ])
s = array([ 16, 36 ])
#Don't edit anything past here
def bayesian_expected_error(N,s):
degrees_of_freedom = len(prior_params)
posteriors = []
for i in range(degrees_of_freedom):
posteriors.append( beta(prior_params[i][0] + s[i] - 1, prior_params[i][1] + N[i] - s[i] - 1) )
xgrid_size = 1024
x = mgrid[0:xgrid_size,0:xgrid_size] / float(xgrid_size)
# Compute joint posterior, which is a product distribution
pdf_arr = posteriors[0].pdf(x[1]) * posteriors[1].pdf(x[0])
pdf_arr /= pdf_arr.sum() # normalization
expected_error_dist = maximum(x[0]-x[1],0.0) * pdf_arr
return expected_error_dist.sum()
# Code
degrees_of_freedom = len(prior_params)
posteriors = []
for i in range(degrees_of_freedom):
posteriors.append( beta(prior_params[i][0] + s[i] - 1, prior_params[i][1] + N[i] - s[i] - 1) )
if degrees_of_freedom == 2:
xgrid_size = 1024
x = mgrid[0:xgrid_size,0:xgrid_size] / float(xgrid_size)
# Compute joint posterior, which is a product distribution
pdf_arr = posteriors[0].pdf(x[1]) * posteriors[1].pdf(x[0])
pdf_arr /= pdf_arr.sum() # normalization
prob_error = zeros(shape=x[0].shape)
if (s[1] / float(N[1])) > (s[0] / float(N[0])):
prob_error[where(x[1] > x[0])] = 1.0
else:
prob_error[where(x[0] > x[1])] = 1.0
expected_error = maximum(abs(x[0]-x[1]),0.0)
expected_err_scalar = (expected_error * prob_error * pdf_arr).sum()
if (expected_err_scalar < threshold_of_caring):
if (s[1] / float(N[1])) > (s[0] / float(N[0])):
print "Probability that version B is larger is " + str((prob_error*pdf_arr).sum())
print "Terminate test. Choose version B. Expected error is " + str(expected_err_scalar)
else:
print "Probability that version A is larger is " + str((prob_error*pdf_arr).sum())
print "Terminate test. Choose version A. Expected error is " + str(expected_err_scalar)
else:
print "Probability that version B is larger is " + str((prob_error*pdf_arr).sum())
print "Continue test. Expected error was " + str(expected_err_scalar) + " > " + str(threshold_of_caring)

Looks like the bayesian_expected_error function is never called. Is there a reason you left it in?

Large numbers seem to be breaking the code for some reason, e.g. I've entered in the following for N and s

N = array([ 15709030, 1448607 ])
s = array([ 955, 157 ])

This results in:
Probability that version B is larger is nan
Continue test. Expected error was nan > 0.001
/Users/jhanlon/Dropbox/BayesianStat.py:44: RuntimeWarning: invalid value encountered in divide
pdf_arr /= pdf_arr.sum() # normalization

I haven't touched the priors or delta

arnov commented Feb 13, 2015

I've implemented a python version for this as well. It's a bit simpler, it closely follows Evan Millar's explanation.

https://gist.github.com/arnov/60de0b1ad62d329bc222

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