Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
MCMC for simple marketing data
import pymc, pymc.graph
import matplotlib.pyplot as plt
import numpy as np
import sys
channels = [
('A', 2292.04, 9),
('B', 1276.85, 2),
('C', 139.59, 3),
('D', 954.98, 5)
]
n_burn, n_samples = 1000, int(sys.argv[1])
for channel, spend_obs, transactions_obs in channels:
# Let's say the cost per lead is uniform between 0 and 100000
# pymc forces you to pick an upper bound, so let's pick a silly one
c = pymc.Uniform('c', 0, 100000)
# Now let's assume the *expected* number of transactions is really just
# the total number of money spent divided by the cost per lead
@pymc.deterministic
def e(c=c):
return spend_obs / c
# The observed number of transactions is a Poisson with mu set to the expectations
a = pymc.Poisson('a', mu=e, observed=True, value=transactions_obs)
model = pymc.MCMC([c, e, a])
graph = pymc.graph.graph(model)
graph.write_png("graph.png")
model.sample(n_samples, n_burn)
ys, xs = np.histogram(model.trace('c')[:], range=(0, 500), bins=100)
xs = (xs[1:] + xs[:-1])/2
plt.plot(xs, ys)
plt.legend([c for c, _, _ in channels])
plt.gca()
plt.title('%d samples (%d burn)' % (n_samples, n_burn))
plt.xlabel('Cost per transaction')
plt.ylabel('Probability density')
plt.savefig('marketing_mc_%09d.png' % n_samples)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment