interpolation='bicubic',
origin='lower',
cmap=plt.get_cmap('hot'),
norm=matplotlib.colors.LogNorm(),
aspect='auto')
resampledLimits = (resampled.min(), resampled.max())
plt.colorbar(implt, ax=ax1)
# Add optimum
ax1.plot(xactions, yactions_exact)
# Create predicted contour plot
x = np.linspace(0, 1, num=50)
y = np.linspace(0, 1, num=50)
X, Y = np.meshgrid(x, y)
# Predict mean
mean, var = model.predict(
np.transpose(np.array((X.flatten(order='C'), Y.flatten(order='C')))))
Z = np.reshape(mean, X.shape, order='C')
CS = ax2.contour(X,
Y,
np.maximum(Z, 1e-3),
levels=[
1e-3, 3e-3, 1e-2, 3e-2, 1e-1, 3e-1, 1e0, 3e0, 1e1, 3e1,
1e2, 3e2, 1e3, 3e3, 1e4
],
norm=matplotlib.colors.LogNorm())
ax2.clabel(CS, inline=1, fontsize=10)
ax2.set_title('predicted function (payoff)')
ax2.set_xlabel('context')
ax2.set_ylabel('action')
ax2.set_ylim((0, 1))
# This is the main Bayesian optimization loop
for i in xrange(10):
# Fit the model on the data we observed so far
model.train(X, Y)
# Update the acquisition function model with the retrained model
acquisition_func.update(model)
# Optimize the acquisition function to obtain a new point
new_x = maximizer(acquisition_func, X_lower, X_upper)
# Evaluate the point and add the new observation to our set of previous seen points
new_y = objective_function(np.array(new_x))
X = np.append(X, new_x, axis=0)
Y = np.append(Y, new_y, axis=0)
# Visualize the objective function, model and the acquisition function
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
plotting_range = np.linspace(X_lower[0], X_upper[0], num=1000)
ax1.plot(plotting_range, objective_function(plotting_range[:, np.newaxis]), color='b', linestyle="--")
_min_y1, _max_y1 = ax1.get_ylim()
model.visualize(ax1, X_lower[0], X_upper[0])
_min_y2, _max_y2 = ax1.get_ylim()
ax1.set_ylim(min(_min_y1, _min_y2), max(_max_y1, _max_y2))
mu, var = model.predict(new_x)
ax1.plot(new_x[0], mu[0], "r.", markeredgewidth=5.0)
ax2 = acquisition_func.plot(fig, X_lower[0], X_upper[0], plot_attr={"color": "red"}, resolution=1000)
plt.show(block=True)
ax1.set_ylim((0, 1))
imx, imy = np.mgrid[0:1:100j, 0:1:100j]
resampled = griddata((X.flatten(), Y.flatten()), Z.flatten(), (imx, imy))
implt = ax1.imshow(resampled.T, extent=(0, 1, 0, 1), interpolation='bicubic', origin='lower', cmap=plt.get_cmap('hot'),
norm=matplotlib.colors.LogNorm(), aspect='auto')
resampledLimits = (resampled.min(), resampled.max())
plt.colorbar(implt, ax=ax1)
# Add optimum
ax1.plot(xactions, yactions_exact)
# Create predicted contour plot
x = np.linspace(0, 1, num=50)
y = np.linspace(0, 1, num=50)
X, Y = np.meshgrid(x, y)
# Predict mean
mean, var = model.predict(np.transpose(np.array((X.flatten(order='C'), Y.flatten(order='C')))))
Z = np.reshape(mean, X.shape, order='C')
CS = ax2.contour(X, Y, np.maximum(Z, 1e-3), levels=[1e-3, 3e-3, 1e-2, 3e-2, 1e-1, 3e-1, 1e0, 3e0, 1e1, 3e1, 1e2, 3e2,
1e3, 3e3, 1e4], norm=matplotlib.colors.LogNorm())
ax2.clabel(CS, inline=1, fontsize=10)
ax2.set_title('predicted function (payoff)')
ax2.set_xlabel('context')
ax2.set_ylabel('action')
ax2.set_ylim((0, 1))
imx, imy = np.mgrid[0:1:100j, 0:1:100j]
resampled = griddata((X.flatten(), Y.flatten()), Z.flatten(), (imx, imy))
implt = ax2.imshow(resampled.T, extent=(0, 1, 0, 1), interpolation='bicubic', origin='lower', cmap=plt.get_cmap('hot'),
norm=matplotlib.colors.LogNorm(), aspect='auto', vmin=resampledLimits[0], vmax=resampledLimits[1])
plt.colorbar(implt, ax=ax2)
ax2.plot(bo.X[:, 0], bo.X[:, 1], 'x')
# Optimize the acquisition function to obtain a new point
new_x = maximizer(acquisition_func, X_lower, X_upper)
# Evaluate the point and add the new observation to our set of previous seen points
new_y = objective_function(np.array(new_x))
X = np.append(X, new_x, axis=0)
Y = np.append(Y, new_y, axis=0)
# Visualize the objective function, model and the acquisition function
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
plotting_range = np.linspace(X_lower[0], X_upper[0], num=1000)
ax1.plot(plotting_range,
objective_function(plotting_range[:, np.newaxis]),
color='b',
linestyle="--")
_min_y1, _max_y1 = ax1.get_ylim()
model.visualize(ax1, X_lower[0], X_upper[0])
_min_y2, _max_y2 = ax1.get_ylim()
ax1.set_ylim(min(_min_y1, _min_y2), max(_max_y1, _max_y2))
mu, var = model.predict(new_x)
ax1.plot(new_x[0], mu[0], "r.", markeredgewidth=5.0)
ax2 = acquisition_func.plot(fig,
X_lower[0],
X_upper[0],
plot_attr={"color": "red"},
resolution=1000)
plt.show(block=True)
