# Exploitation vs Exploration¶

In [1]:
%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt

from bayes_opt import BayesianOptimization


# Target function¶

In [2]:
np.random.seed(42)
xs = np.linspace(-2, 10, 10000)

def f(x):
return np.exp(-(x - 2) ** 2) + np.exp(-(x - 6) ** 2 / 10) + 1/ (x ** 2 + 1)

plt.plot(xs, f(xs))
plt.show()


## Utility function for plotting¶

In [3]:
def plot_bo(f, bo):
x = np.linspace(-2, 10, 10000)
mean, sigma = bo._gp.predict(x.reshape(-1, 1), return_std=True)

plt.figure(figsize=(16, 9))
plt.plot(x, f(x))
plt.plot(x, mean)
plt.fill_between(x, mean + sigma, mean - sigma, alpha=0.1)
plt.scatter(bo.space.params.flatten(), bo.space.target, c="red", s=50, zorder=10)
plt.show()


# Acquisition Function "Upper Confidence Bound"¶

## Prefer exploitation (kappa=0.1)¶

Note that most points are around the peak(s).

In [4]:
bo = BayesianOptimization(
f=f,
pbounds={"x": (-2, 10)},
verbose=0,
random_state=987234,
)

bo.maximize(n_iter=10, acq="ucb", kappa=0.1)

plot_bo(f, bo)


## Prefer exploration (kappa=10)¶

Note that the points are more spread out across the whole range.

In [5]:
bo = BayesianOptimization(
f=f,
pbounds={"x": (-2, 10)},
verbose=0,
random_state=987234,
)

bo.maximize(n_iter=10, acq="ucb", kappa=10)

plot_bo(f, bo)


# Acquisition Function "Expected Improvement"¶

## Prefer exploitation (xi=0.0)¶

Note that most points are around the peak(s).

In [6]:
bo = BayesianOptimization(
f=f,
pbounds={"x": (-2, 10)},
verbose=0,
random_state=987234,
)

bo.maximize(n_iter=10, acq="ei", xi=1e-4)

plot_bo(f, bo)


## Prefer exploration (xi=0.1)¶

Note that the points are more spread out across the whole range.

In [7]:
bo = BayesianOptimization(
f=f,
pbounds={"x": (-2, 10)},
verbose=0,
random_state=987234,
)

bo.maximize(n_iter=10, acq="ei", xi=1e-1)

plot_bo(f, bo)


# Acquisition Function "Probability of Improvement"¶

## Prefer exploitation (xi=0.0)¶

Note that most points are around the peak(s).

In [8]:
bo = BayesianOptimization(
f=f,
pbounds={"x": (-2, 10)},
verbose=0,
random_state=987234,
)

bo.maximize(n_iter=10, acq="poi", xi=1e-4)

plot_bo(f, bo)


## Prefer exploration (xi=0.1)¶

Note that the points are more spread out across the whole range.

In [9]:
bo = BayesianOptimization(
f=f,
pbounds={"x": (-2, 10)},
verbose=0,
random_state=987234,
)

bo.maximize(n_iter=10, acq="poi", xi=1e-1)

plot_bo(f, bo)