if __name__ == '__main__':
# set up the model
# the assumption is that the optimal price is somewhere between 0 and 100
param = {'price': ('cont', [0, 100])}
sexp = squaredExponential()
gp = GaussianProcessMCMC(sexp, step=pm.Slice)
acq = Acquisition(mode='IntegratedExpectedImprovement')
gpgo = GPGO(gp, acq, data.revenue, param)
# We have no history at all at day 0
# but we do know that at 0, I make no revenue,
# so we can consider that as a valid history
gp.fit(np.array([[0]]), np.array([0]))
gpgo.tau = 0
gpgo.init_evals = 1
# run 10 days, starting from day 0
gpgo.run(max_iter=10, resume=True)
fig = plt.figure()
# show some samples from the posterior
ax = plt.subplot(3, 1, 1)
plt.xlim(0, 100)
Z = np.linspace(0, 100, 100)[:, None]
post_mean, post_var = gpgo.GP.predict(Z, return_std=True, nsamples=200)
for i in range(200):
plt.plot(Z.flatten(), post_mean[i], linewidth=0.4)
if __name__ == '__main__':
# set up the model
sexp = squaredExponential()
gp = GaussianProcessMCMC(sexp, step=pm.Slice)
param = {'price': ('cont', [0, 100])}
acq = Acquisition(mode='IntegratedExpectedImprovement')
gpgo = GPGO(gp, acq, dummy, param)
# incorporate already known data
x = data[:, 0]
y = data[:, 1]
# reshape so that we get [[1],[2],[3]...]
reshapedx = np.expand_dims(x, 1)
gp.fit(reshapedx, y)
gpgo.tau = np.max(y)
gpgo.init_evals = len(x)
# find the next price point to test
gpgo._optimizeAcq()
best = gpgo.best
print("The next price to test is", best)
# show sample
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
plt.plot(x, y, "o", label="Observed revenue")
plt.axvline(x=best, label="Next price point")
plt.legend()
