def three_d_grid_archive():
"""Deterministic archive, but there are three behavior axes of different
sizes, and some of the axes are not totally filled."""
archive = GridArchive([10, 10, 10], [(-2, 2), (-1, 1), (-2, 1)], seed=42)
archive.initialize(solution_dim=3)
add_uniform_3d_sphere(archive, (0, 2), (-1, 1), (-1, 0))
return archive
def _grid_archive():
"""Deterministically created GridArchive."""
# The archive must be low-res enough that we can tell if the number of cells
# is correct, yet high-res enough that we can see different colors.
archive = GridArchive([10, 10], [(-1, 1), (-1, 1)], seed=42)
archive.initialize(solution_dim=2)
_add_uniform_sphere(archive, (-1, 1), (-1, 1))
return archive
def setup():
archive = GridArchive((64, 64), [(-1, 1), (-1, 1)])
archive.initialize(solutions.shape[1])
# Let numba compile.
archive.add(solutions[0], objective_values[0], behavior_values[0])
return (archive, ), {}
def benchmark_get_10k_random_elites(benchmark, benchmark_data_10k):
n, solutions, objective_values, behavior_values = benchmark_data_10k
archive = GridArchive((64, 64), [(-1, 1), (-1, 1)])
archive.initialize(solutions.shape[1])
for i in range(n):
archive.add(solutions[i], objective_values[i], behavior_values[i])
@benchmark
def get_elites():
for i in range(n):
sol, obj, beh = archive.get_random_elite()
def test_dtypes(emitter_class, dtype):
archive = GridArchive([20, 20], [(-1.0, 1.0)] * 2, dtype=dtype)
archive.initialize(10)
emitter = emitter_class(archive, np.zeros(10), 1.0)
assert emitter.x0.dtype == dtype
# Try running with the negative sphere function for a few iterations.
for _ in range(10):
sols = emitter.ask()
objs = -np.sum(np.square(sols), axis=1)
bcs = sols[:, :2]
emitter.tell(sols, objs, bcs)
def benchmark_as_pandas_2025_items(benchmark):
dim = 45
archive = GridArchive((dim, dim), [(-1, 1), (-1, 1)])
archive.initialize(10)
for x in np.linspace(-1, 1, dim):
for y in np.linspace(-1, 1, dim):
sol = np.random.random(10)
sol[0] = x
sol[1] = y
archive.add(sol, 1.0, np.array([x, y]))
# Archive should be full.
assert len(archive.as_pandas()) == dim * dim
benchmark(archive.as_pandas)
def get_archive_data(name, dtype=np.float64):
"""Returns ArchiveFixtureData to use for testing each archive.
The archives vary, but there will always be an empty 2D archive, as well as
a 2D archive with a single solution added to it. This solution will have a
value of [1, 2, 3], its objective value will be 1.0, and its behavior values
will be [0.25, 0.25].
The name is the name of an archive to create. It should come from
ARCHIVE_NAMES.
"""
# Characteristics of a single solution to insert into archive_with_entry.
solution = np.array([1, 2, 3])
objective_value = 1.0
behavior_values = np.array([0.25, 0.25])
grid_indices = None
centroid = None
if name == "GridArchive":
# Grid archive with 10 bins and range (-1, 1) in first dim, and 20 bins
# and range (-2, 2) in second dim.
bins = 10 * 20
archive = GridArchive([10, 20], [(-1, 1), (-2, 2)], dtype=dtype)
archive.initialize(len(solution))
archive_with_entry = GridArchive([10, 20], [(-1, 1), (-2, 2)],
dtype=dtype)
archive_with_entry.initialize(len(solution))
grid_indices = (6, 11)
elif name.startswith("CVTArchive-"):
# CVT archive with bounds (-1,1) and (-1,1), and 4 centroids at (0.5,
# 0.5), (-0.5, 0.5), (-0.5, -0.5), and (0.5, -0.5). The entry in
# archive_with_entry should match with centroid (0.5, 0.5).
bins = 4
kd_tree = name == "CVTArchive-kd_tree"
samples = [[0.5, 0.5], [-0.5, 0.5], [-0.5, -0.5], [0.5, -0.5]]
centroid = [0.5, 0.5]
archive = CVTArchive(4, [(-1, 1), (-1, 1)],
samples=samples,
use_kd_tree=kd_tree,
dtype=dtype)
archive.initialize(len(solution))
archive_with_entry = CVTArchive(4, [(-1, 1), (-1, 1)],
samples=samples,
use_kd_tree=kd_tree,
dtype=dtype)
archive_with_entry.initialize(len(solution))
elif name == "SlidingBoundariesArchive":
# Sliding boundary archive with 10 bins and range (-1, 1) in first dim,
# and 20 bins and range (-2, 2) in second dim.
bins = 10 * 20
archive = SlidingBoundariesArchive([10, 20], [(-1, 1), (-2, 2)],
remap_frequency=100,
buffer_capacity=1000,
dtype=dtype)
archive.initialize(len(solution))
archive_with_entry = SlidingBoundariesArchive([10, 20], [(-1, 1),
(-2, 2)],
remap_frequency=100,
buffer_capacity=1000,
dtype=dtype)
archive_with_entry.initialize(len(solution))
grid_indices = (6, 11)
archive_with_entry.add(solution, objective_value, behavior_values)
return ArchiveFixtureData(
archive,
archive_with_entry,
solution,
objective_value,
behavior_values,
grid_indices,
centroid,
bins,
)
def _long_grid_archive():
"""Same as above, but the behavior space is longer in one direction."""
archive = GridArchive([10, 10], [(-2, 2), (-1, 1)], seed=42)
archive.initialize(solution_dim=2)
_add_uniform_sphere(archive, (-2, 2), (-1, 1))
return archive
def archive_fixture():
"""Provides a simple archive and initial solution."""
archive = GridArchive([10, 10], [(-1, 1), (-1, 1)])
x0 = np.array([1, 2, 3, 4])
archive.initialize(len(x0))
return archive, x0
