def test_tell_inserts_solutions_with_multiple_emitters(tell_metadata):
archive = GridArchive([100, 100], [(-1, 1), (-1, 1)])
emitters = [
GaussianEmitter(archive, [0.0, 0.0], 1, batch_size=1),
GaussianEmitter(archive, [0.5, 0.5], 1, batch_size=2),
GaussianEmitter(archive, [-0.5, -0.5], 1, batch_size=3),
]
optimizer = Optimizer(archive, emitters)
_ = optimizer.ask()
behavior_values = [[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0],
[0.0, 0.0], [0.0, 1.0]]
metadata = [f"metadata_{i}" for i in range(6)] if tell_metadata else None
expected_metadata = metadata if tell_metadata else [None] * 6
# The sum of all the emitters' batch sizes is 6.
optimizer.tell(
objective_values=np.ones(6),
behavior_values=behavior_values,
metadata=metadata,
)
# Note: This assumes data() returns entries in order of insertion, but this
# may change in the future.
all_sols, all_objs, all_behs, _, all_meta = optimizer.archive.data()
assert len(all_sols) == 6
assert (behavior_values == all_behs).all()
assert (all_objs == np.ones(6)).all()
assert (expected_metadata == all_meta).all()
def test_from_config_with_valid_input(use_toml, tmp_path):
seed = 42
batch_size = 4
archive = GridArchive([64, 64], [(-1, 1), (-1, 1)], seed=seed)
emitters = [
GaussianEmitter(archive, [0.0, 0.0],
0.1,
batch_size=batch_size,
seed=seed)
]
optimizer = Optimizer(archive, emitters)
config_dict = {
"archive": {
"type": "GridArchive",
"dims": [64, 64],
"ranges": [(-1, 1), (-1, 1)],
"seed": seed,
},
"emitters": [{
"type": "GaussianEmitter",
"x0": [0.0, 0.0],
"sigma0": 0.1,
"batch_size": batch_size,
"seed": seed,
}],
"optimizer": {
"type": "Optimizer",
},
}
if use_toml:
config_path = tmp_path / "config.toml"
with config_path.open("w") as file:
toml.dump(config_dict, file)
created_optimizer = ribs.factory.from_config(config_path)
else:
created_optimizer = ribs.factory.from_config(config_dict)
# Check types.
assert isinstance(created_optimizer, Optimizer)
assert isinstance(created_optimizer.archive, GridArchive)
assert len(created_optimizer.emitters) == 1
assert isinstance(created_optimizer.emitters[0], GaussianEmitter)
# Check results from ask() and tell() -- since seeds are the same, all
# results should be the same.
optimizer_sols = optimizer.ask()
created_optimizer_sols = created_optimizer.ask()
assert len(optimizer_sols) == batch_size
assert (optimizer_sols == created_optimizer_sols).all()
objective_values = [0.0] * batch_size
behavior_values = np.array([[1, 1], [-1, 1], [-1, -1], [1, -1]])
optimizer.tell(objective_values, behavior_values)
created_optimizer.tell(objective_values, behavior_values)
assert (optimizer.archive.as_pandas() ==
created_optimizer.archive.as_pandas()).all(None)
def test_tell_inserts_solutions_with_multiple_emitters():
archive = GridArchive([100, 100], [(-1, 1), (-1, 1)])
emitters = [
GaussianEmitter(archive, [0.0, 0.0], 1, batch_size=1),
GaussianEmitter(archive, [0.5, 0.5], 1, batch_size=2),
GaussianEmitter(archive, [-0.5, -0.5], 1, batch_size=3),
]
optimizer = Optimizer(archive, emitters)
_ = optimizer.ask()
# The sum of all the emitters' batch sizes is 6.
optimizer.tell(
objective_values=[1.0] * 6,
behavior_values=[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0],
[0.0, 0.0], [0.0, 1.0]],
)
assert len(optimizer.archive.as_pandas()) == 6
num_samples=opt.num_samples)
for solution in solutions
]
bc0 = 0
bc1 = 0
end_states = []
for result in results:
bcs.append([result[1], result[2]])
objectives.append(result[0])
bc0 += result[1]
bc1 += result[2]
end_states.append(result[3])
optimizer.tell(objectives, bcs)
score_obj = max(objectives)
bc0 = bc0 / float(len(objectives))
bc1 = bc1 / float(len(objectives))
tb_logging(archive, i, start_time, logdir, score_obj, bc0, bc1,
end_states)
if i % 100 == 0:
#generate a heatmap
plt.figure(figsize=(8, 6))
#plt.figure(figsize=(8,2))
grid_archive_heatmap(archive)
plt.title(obj_names[opt.obj])
plt.xlabel(bc_names[opt.bcs[0]]) #objective 0