def test_load_with_params():
config = {
"type": "bolero.optimizer.CMAESOptimizer",
"variance": 10.0,
"n_samples_per_update": 10
}
optimizer = from_dict(config)
assert_true(isinstance(optimizer, CMAESOptimizer))
assert_equal(optimizer.variance, 10.0)
assert_equal(optimizer.n_samples_per_update, 10)
def test_load_cpp_lib():
opt = from_dict({"Optimizer": {"type": "pso_optimizer"}})["Optimizer"]
assert_true(hasattr(opt, "get_next_parameters"))
def test_load_tuple():
opt_config = {"type": "bolero.optimizer.CMAESOptimizer"}
config = {"optimizers": (opt_config, opt_config)}
result = from_dict(config)
assert_true(isinstance(result["optimizers"][0], CMAESOptimizer))
assert_true(isinstance(result["optimizers"][1], CMAESOptimizer))
def test_load_missing_type():
config = {"key": "value"}
config2 = from_dict(config)
assert_equal(config, config2)
def test_load_explicit_package():
config = {"package": "bolero.optimizer", "type": "CMAESOptimizer"}
optimizer = from_dict(config)
assert_true(isinstance(optimizer, CMAESOptimizer))
"""
=======================
Load Module from Config
=======================
In bolero, we can load modules (optimizers, behavior search methods, behaviors,
environments) either from configuration dictionaries or from YAML files. This
example shows how we can load an optimizer from a configuration dictionary.
"""
print(__doc__)
from bolero.utils import from_dict
config = {
"type": "bolero.optimizer.CMAESOptimizer",
"variance": 10.0,
}
optimizer = from_dict(config)
optimizer.init(2)
params = [0, 0]
optimizer.get_next_parameters(params)
print(params)