To compare the two algorithms, we're going to build a distribution that represents the payoffs of each algorithm, and do a quick test to see if the RPMBandit is, in fact, better than the SimpleBandit algorithm.
The following is a simulation harness that I've built to compare the two:
def run_bandit_sim(bandit_algorithm):
simulated_experiment = BanditScenario({
'A': {
'conversion_rate': 1,
'order_average': 35.00
},
'B':{
'conversion_rate': 1,
'order_average': 50.00
}
})
simple_bandit = bandit_algorithm
for visitor_i in range(500):
treatment = simple_bandit.choose_treatment()
payout = simulated_experiment.next_visitor(treatment)
simple_bandit.log_payout(treatment, payout)
return sum(simulated_experiment._bandit_payoffs)
simple_bandit_results = np.array([run_bandit_sim(SimpleBandit(['A', 'B'])) for i in range(300)])
rpm_bandit_results = np.array([run_bandit_sim...