ex_chisq.py

# From the course: Bayesin Machine Learning in Python: A/B Testing
# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future


import numpy as np
import pandas as pd
from scipy.stats import chi2, chi2_contingency

# contingency table
#        click       no click
#------------------------------
# ad A |   a            b
# ad B |   c            d


def get_p_value(T):
  # same as scipy.stats.chi2_contingency(T, correction=False)
  det = T[0,0]*T[1,1] - T[0,1]*T[1,0]
  c2 = float(det) / T[0].sum() * det / T[1].sum() * T.sum() / T[:,0].sum() / T[:,1].sum()
  p = 1 - chi2.cdf(x=c2, df=1)
  return p

# get data
df = pd.read_csv('advertisement_clicks.csv')
a = df[df['advertisement_id'] == 'A']
b = df[df['advertisement_id'] == 'B']
a = a['action']
b = b['action']

A_clk = a.sum()
A_noclk = a.size - a.sum()
B_clk = b.sum()
B_noclk = b.size - b.sum()

T = np.array([[A_clk, A_noclk], [B_clk, B_noclk]])

print(get_p_value(T))