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Use different base estimators for optimization¶
Sigurd Carlen, September 2019. Reformatted by Holger Nahrstaedt 2020
To use different base_estimator or create a regressor with different parameters, we can create a regressor object and set it as kernel.
This example uses plots.plot_gaussian_process which is available
since version 0.8.
print(__doc__)
import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
from skopt.plots import plot_gaussian_process
from skopt import Optimizer
Toy example¶
Let assume the following noisy function \(f\):
noise_level = 0.1
# Our 1D toy problem, this is the function we are trying to
# minimize
def objective(x, noise_level=noise_level):
return np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2))\
+ np.random.randn() * noise_level
def objective_wo_noise(x):
return objective(x, noise_level=0)
opt_gp = Optimizer([(-2.0, 2.0)], base_estimator="GP", n_initial_points=5,
acq_optimizer="sampling", random_state=42)
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([])
GP kernel¶
fig = plt.figure()
fig.suptitle("Standard GP kernel")
for i in range(10):
next_x = opt_gp.ask()
f_val = objective(next_x)
res = opt_gp.tell(next_x, f_val)
if i >= 5:
plot_optimizer(res, n_iter=i-5, max_iters=5)
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.plot()
Out:
[]
Test different kernels¶
from skopt.learning import GaussianProcessRegressor
from skopt.learning.gaussian_process.kernels import ConstantKernel, Matern
# Gaussian process with Matérn kernel as surrogate model
from sklearn.gaussian_process.kernels import (RBF, Matern, RationalQuadratic,
ExpSineSquared, DotProduct,
ConstantKernel)
kernels = [1.0 * RBF(length_scale=1.0, length_scale_bounds=(1e-1, 10.0)),
1.0 * RationalQuadratic(length_scale=1.0, alpha=0.1),
1.0 * ExpSineSquared(length_scale=1.0, periodicity=3.0,
length_scale_bounds=(0.1, 10.0),
periodicity_bounds=(1.0, 10.0)),
ConstantKernel(0.1, (0.01, 10.0))
* (DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2),
1.0 * Matern(length_scale=1.0, length_scale_bounds=(1e-1, 10.0),
nu=2.5)]
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel, alpha=noise_level ** 2,
normalize_y=True, noise="gaussian",
n_restarts_optimizer=2
)
opt = Optimizer([(-2.0, 2.0)], base_estimator=gpr, n_initial_points=5,
acq_optimizer="sampling", random_state=42)
fig = plt.figure()
fig.suptitle(repr(kernel))
for i in range(10):
next_x = opt.ask()
f_val = objective(next_x)
res = opt.tell(next_x, f_val)
if i >= 5:
plot_optimizer(res, n_iter=i - 5, max_iters=5)
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
Total running time of the script: ( 0 minutes 8.790 seconds)
Estimated memory usage: 12 MB
