bayesian_bandit.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 matplotlib.pyplot as plt
import numpy as np
from scipy.stats import beta


NUM_TRIALS = 2000
BANDIT_PROBABILITIES = [0.2, 0.5, 0.75]


class Bandit(object):
  def __init__(self, p):
    self.p = p
    self.a = 1
    self.b = 1

  def pull(self):
    return np.random.random() < self.p

  def sample(self):
    return np.random.beta(self.a, self.b)

  def update(self, x):
    self.a += x
    self.b += 1 - x


def plot(bandits, trial):
  x = np.linspace(0, 1, 200)
  for b in bandits:
    y = beta.pdf(x, b.a, b.b)
    plt.plot(x, y, label="real p: %.4f" % b.p)
  plt.title("Bandit distributions after %s trials" % trial)
  plt.legend()
  plt.show()


def experiment():
  bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]

  sample_points = [5,10,20,50,100,200,500,1000,1500,1999]
  for i in range(NUM_TRIALS):

    # take a sample from each bandit
    bestb = None
    maxsample = -1
    allsamples = [] # let's collect these just to print for debugging
    for b in bandits:
      sample = b.sample()
      allsamples.append("%.4f" % sample)
      if sample > maxsample:
        maxsample = sample
        bestb = b
    if i in sample_points:
      print("current samples: %s" % allsamples)
      plot(bandits, i)

    # pull the arm for the bandit with the largest sample
    x = bestb.pull()

    # update the distribution for the bandit whose arm we just pulled
    bestb.update(x)


if __name__ == "__main__":
  experiment()