Comparing initial sampling methods on integer space

Holger Nahrstaedt 2020 Sigurd Carlsen October 2019

When doing baysian optimization we often want to reserve some of the early part of the optimization to pure exploration. By default the optimizer suggests purely random samples for the first n_initial_points (10 by default). The downside to this is that there is no guarantee that these samples are spread out evenly across all the dimensions.

Sampling methods as Latin hypercube, Sobol’, Halton and Hammersly take advantage of the fact that we know beforehand how many random points we want to sample. Then these points can be “spread out” in such a way that each dimension is explored.

See also the example on a real space sphx_glr_auto_examples_initial_sampling_method.py

print(__doc__)
import numpy as np

np.random.seed(1234)
import matplotlib.pyplot as plt
from scipy.spatial.distance import pdist

from skopt.sampler import Grid, Halton, Hammersly, Lhs, Sobol
from skopt.space import Space

def plot_searchspace(x, title):
    fig, ax = plt.subplots()
    plt.plot(np.array(x)[:, 0], np.array(x)[:, 1], 'bo', label='samples')
    plt.plot(np.array(x)[:, 0], np.array(x)[:, 1], 'bs', markersize=40, alpha=0.5)
    # ax.legend(loc="best", numpoints=1)
    ax.set_xlabel("X1")
    ax.set_xlim([0, 5])
    ax.set_ylabel("X2")
    ax.set_ylim([0, 5])
    plt.title(title)
    ax.grid(True)


n_samples = 10
space = Space([(0, 5), (0, 5)])

Random sampling

x = space.rvs(n_samples)
plot_searchspace(x, "Random samples")
pdist_data = []
x_label = []
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("random")

empty fields: 27

Sobol’

sobol = Sobol()
x = sobol.generate(space.dimensions, n_samples)
plot_searchspace(x, "Sobol'")
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("sobol'")

D:\git\scikit-optimize\skopt\sampler\sobol.py:521: UserWarning: The balance properties of Sobol' points require n to be a power of 2. 0 points have been previously generated, then: n=0+10=10.
  warnings.warn(
empty fields: 26

Classic latin hypercube sampling

lhs = Lhs(lhs_type="classic", criterion=None)
x = lhs.generate(space.dimensions, n_samples)
plot_searchspace(x, 'classic LHS')
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("lhs")

empty fields: 26

Centered latin hypercube sampling

lhs = Lhs(lhs_type="centered", criterion=None)
x = lhs.generate(space.dimensions, n_samples)
plot_searchspace(x, 'centered LHS')
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("center")

empty fields: 26

Maximin optimized hypercube sampling

lhs = Lhs(criterion="maximin", iterations=10000)
x = lhs.generate(space.dimensions, n_samples)
plot_searchspace(x, 'maximin LHS')
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("maximin")

empty fields: 26

Correlation optimized hypercube sampling

lhs = Lhs(criterion="correlation", iterations=10000)
x = lhs.generate(space.dimensions, n_samples)
plot_searchspace(x, 'correlation LHS')
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("corr")

empty fields: 26

Ratio optimized hypercube sampling

lhs = Lhs(criterion="ratio", iterations=10000)
x = lhs.generate(space.dimensions, n_samples)
plot_searchspace(x, 'ratio LHS')
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("ratio")

empty fields: 26

Halton sampling

halton = Halton()
x = halton.generate(space.dimensions, n_samples)
plot_searchspace(x, 'Halton')
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("halton")

empty fields: 26

Hammersly sampling

hammersly = Hammersly()
x = hammersly.generate(space.dimensions, n_samples)
plot_searchspace(x, 'Hammersly')
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("hammersly")

empty fields: 26

Grid sampling

grid = Grid(border="include", use_full_layout=False)
x = grid.generate(space.dimensions, n_samples)
plot_searchspace(x, 'Grid')
print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
pdist_data.append(pdist(x).flatten())
x_label.append("grid")

empty fields: 26

Pdist boxplot of all methods

This boxplot shows the distance between all generated points using Euclidian distance. The higher the value, the better the sampling method. It can be seen that random has the worst performance

fig, ax = plt.subplots()
ax.boxplot(pdist_data)
plt.grid(True)
plt.ylabel("pdist")
_ = ax.set_ylim(0, 6)
_ = ax.set_xticklabels(x_label, rotation=45, fontsize=8)