Creating Continuous Search Spaces

This example illustrates several ways to create continuous spaces space.

Imports

import numpy as np

from baybe.parameters import NumericalContinuousParameter
from baybe.searchspace import SearchSpace, SubspaceContinuous

Settings

We begin by defining the continuous parameters that span our space:

DIMENSION = 4
BOUNDS = (-1, 1)

parameters = [
    NumericalContinuousParameter(name=f"x_{k+1}", bounds=BOUNDS)
    for k in range(DIMENSION)
]

From these parameter objects, we can now construct a continuous subspace. Let us draw some samples from it and verify that they are within the bounds:

subspace = SubspaceContinuous(parameters)
samples = subspace.sample_uniform(10)
print(samples)
assert np.all(samples >= BOUNDS[0]) and np.all(samples <= BOUNDS[1])

        x_1       x_2       x_3       x_4
0 -0.338737  0.120292  0.605327 -0.473937
1 -0.705866 -0.621174 -0.693795  0.284510
2 -0.206965 -0.399933  0.141555 -0.696073
3 -0.408676 -0.060086  0.055840 -0.474604
4  0.138473 -0.356156 -0.248687 -0.725217
5  0.080993 -0.547259  0.845718  0.930985
6  0.564060  0.279120 -0.159598 -0.201917
7 -0.817753  0.789918  0.208200  0.908014
8  0.343617  0.235958  0.120201  0.492254
9  0.336986 -0.596802  0.038783 -0.158671

There are several ways we can turn the above objects into a search space. This provides a lot of flexibility depending on the context:

# Using conversion:
searchspace1 = SubspaceContinuous(parameters).to_searchspace()

# Explicit attribute assignment via the regular search space constructor:
searchspace2 = SearchSpace(continuous=SubspaceContinuous(parameters))

# Using an alternative search space constructor:
searchspace3 = SearchSpace.from_product(parameters=parameters)

No matter which version we choose, we can be sure that the resulting search space objects are equivalent:

assert searchspace1 == searchspace2 == searchspace3