"""
Example of using the mixture backend's remove_erroring_algs feature.
To run this example, just run:
> bbopt ./remove_erroring_algs_example.py
"""
# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(file=__file__)
if __name__ == "__main__":
bb.run_backend(
"mixture",
distribution=[
("gaussian_process", float("inf")),
("tree_structured_parzen_estimator", 1),
],
remove_erroring_algs=True,
)
# Set some parameters that skopt supports.
x0 = bb.randint("x0", 1, 10, guess=5)
x1 = bb.choice("x1", [-10, -1, 0, 1, 10])
# Set a parameter that only hyperopt supports.
x2 = bb.normalvariate("x2", mu=0, sigma=1)
if not bb.is_serving:
assert bb.backend.selected_alg == "tree_structured_parzen_estimator", bb.backend.selected_alg
# Set the goal.
"""
Example using a mixture distribution over many different possible algorithms.
To run this example, just run:
> bbopt ./mixture_example.py
"""
# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(file=__file__)
if __name__ == "__main__":
bb.run_backend("mixture", [
("random", 1),
("tree_structured_parzen_estimator", 1),
("annealing", 1),
("gaussian_process", 1),
("random_forest", 1),
("extra_trees", 1),
("gradient_boosted_regression_trees", 1),
])
# If we're not serving, store which algorithm the
# mixture backend has selected.
from bbopt.backends.mixture import MixtureBackend
if isinstance(bb.backend, MixtureBackend):
bb.remember({
"alg": bb.backend.selected_alg,
})
# Set up a parameter from a choice and a random sample.
xs = bb.sample("xs", range(10), 5, guess=[3, 4, 5, 6, 7])
