ab_testing_snippet.py

import random

# How many times each ad was clicked
ad_rewards = [0] * bandits
# How many times each ad was selected
ad_selection = [0] * bandits

# N: Number of users
N = df.shape[0]
# bandits: Number of ads
bandits = df.shape[1]
ads_selected = []
total_reward = 0

# For 10% of users:
for n in range(0, round(0.1*N)):
    # Choosing a random ad
    ad = random.randrange(bandits)
    ads_selected.append(ad)
    ad_selection[ad] += 1
    # Checking if ad was clicked
    reward = df.values[n, ad]
    ad_rewards[ad] += reward
    total_reward = total_reward + reward

# Finding the best ad yet, based on click %age
ad_reward_rates_yet = [i/j for i, j in zip(adwise_rewards, adwise_selection)]
best_ad_yet = ad_reward_rates_yet.index(max(ad_reward_rates_yet))

# Showing that ad to remaining users
for n in range(round(0.1*N), N):
    ad = 1
    ads_selected.append(best_ad_yet)
    reward = df.values[n, ad]
    total_reward = total_reward + reward
    
print(total_reward)