def test_evaluate_min_params():
res = gp_minimize(bench3, [(-2.0, 2.0)],
x0=[0.],
noise=1e-8,
n_calls=8,
n_random_starts=3,
random_state=1)
x_min, f_min = expected_minimum(res, random_state=1)
x_min2, f_min2 = expected_minimum_random_sampling(res,
n_random_starts=1000,
random_state=1)
plots.plot_gaussian_process(res)
assert _evaluate_min_params(res, params='result') == res.x
assert _evaluate_min_params(res, params=[1.]) == [1.]
assert _evaluate_min_params(res, params='expected_minimum',
random_state=1) == x_min
assert _evaluate_min_params(res,
params='expected_minimum',
n_minimum_search=20,
random_state=1) == x_min
assert _evaluate_min_params(res,
params='expected_minimum_random',
n_minimum_search=1000,
random_state=1) == x_min2
def plot_optimizer(res, n_iter, max_iters=5):
if n_iter == 0:
show_legend = True
else:
show_legend = False
ax = plt.subplot(max_iters, 2, 2 * n_iter + 1)
# Plot GP(x) + contours
ax = plot_gaussian_process(res,
ax=ax,
objective=objective_wo_noise,
noise_level=noise_level,
show_legend=show_legend,
show_title=True,
show_next_point=False,
show_acq_func=False)
ax.set_ylabel("")
ax.set_xlabel("")
if n_iter < max_iters - 1:
ax.get_xaxis().set_ticklabels([])
# Plot EI(x)
ax = plt.subplot(max_iters, 2, 2 * n_iter + 2)
ax = plot_gaussian_process(res,
ax=ax,
noise_level=noise_level,
show_legend=show_legend,
show_title=False,
show_next_point=True,
show_acq_func=True,
show_observations=False,
show_mu=False)
ax.set_ylabel("")
ax.set_xlabel("")
if n_iter < max_iters - 1:
ax.get_xaxis().set_ticklabels([])
"noise_level": noise_level,
"show_legend": True,
"show_title": True,
"show_next_point": False,
"show_acq_func": True
}
#############################################################################
# We run a an optimization loop with standard settings
for i in range(30):
next_x = opt.ask()
f_val = objective(next_x)
opt.tell(next_x, f_val)
# The same output could be created with opt.run(objective, n_iter=30)
_ = plot_gaussian_process(opt.get_result(), **plot_args)
#############################################################################
# We see that some minima is found and "exploited"
#
# Now lets try to set kappa and xi using'to other values and
# pass it to the optimizer:
acq_func_kwargs = {"xi": 10000, "kappa": 10000}
#############################################################################
opt = Optimizer([(-2.0, 2.0)],
"GP",
n_initial_points=3,
acq_optimizer="sampling",
acq_func_kwargs=acq_func_kwargs)
#############################################################################
#############################################################################
# Plot the 5 iterations following the 5 random points
for n_iter in range(5):
# Plot true function.
plt.subplot(5, 2, 2 * n_iter + 1)
if n_iter == 0:
show_legend = True
else:
show_legend = False
ax = plot_gaussian_process(res,
n_calls=n_iter,
objective=f_wo_noise,
noise_level=noise_level,
show_legend=show_legend,
show_title=False,
show_next_point=False,
show_acq_func=False)
ax.set_ylabel("")
ax.set_xlabel("")
# Plot EI(x)
plt.subplot(5, 2, 2 * n_iter + 2)
ax = plot_gaussian_process(res,
n_calls=n_iter,
show_legend=show_legend,
show_title=False,
show_mu=False,
show_acq_func=True,
show_observations=False,
show_next_point=True)
#########################################################################
# Like ***_minimize()** the first few points are suggestions from
# the initial point generator as there
# is no data yet with which to fit a surrogate model.
for i in range(9):
next_x = opt.ask()
f_val = objective(next_x)
res = opt.tell(next_x, f_val)
#########################################################################
# We can now plot the random suggestions and the first model that has been
# fit:
_ = plot_gaussian_process(res, objective=objective_wo_noise,
noise_level=noise_level,
show_next_point=False,
show_acq_func=True)
plt.show()
#########################################################################
# Let us sample a few more points and plot the optimizer again:
for i in range(10):
next_x = opt.ask()
f_val = objective(next_x)
res = opt.tell(next_x, f_val)
_ = plot_gaussian_process(res, objective=objective_wo_noise,
noise_level=noise_level,
show_next_point=True,
show_acq_func=True)