# 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.
y = x0 + x1 * x2
bb.minimize(y)
# Print out the value we used for debugging purposes.
if __name__ == "__main__":
print(repr(y))
To run this example, just run:
> bbopt ./any_fast_example.py
"""
# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(file=__file__)
if __name__ == "__main__":
bb.run() # alg="any_fast" should be the default
# We set u ~ dist(0, 1) * sin(dist(0, 1)) where dist is uniform or normal.
from math import sin
dist = bb.choice("dist", ["uniform", "normal"])
if dist == "normal":
u = bb.normalvariate("x0_n", 0, 1) * sin(bb.normalvariate("x1_n", 0, 1))
else:
u = bb.random("x0_u") * sin(bb.random("x1_u"))
# If we used hyperopt-only parameters, we shouldn't have skopt.
if hasattr(bb.backend, "selected_backend"):
bb.remember({"backend": bb.backend.selected_backend})
if dist == "normal":
assert bb.backend.selected_backend != "scikit-optimize", bb.backend.selected_backend
else:
bb.remember({"backend": bb.backend.backend_name})
# Set u as the thing to minimize.
bb.minimize(u)
# Print out the value we used for debugging purposes.
"""
Example of using the hyperopt backend with BBopt.
To run this example, just run:
> bbopt ./hyperopt_example.py
"""
# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(file=__file__)
if __name__ == "__main__":
bb.run(alg="tree_structured_parzen_estimator")
# Let's use some parameters!
x0 = bb.randint("x0", 1, 10, guess=5)
x1 = bb.normalvariate("x1", mu=0, sigma=1)
x2 = bb.choice("x2", [-10, -1, 0, 1, 10])
# And let's set our goal!
y = x0 + x1 * x2
bb.minimize(y)
# Finally, we'll print out the value we used for debugging purposes.
if __name__ == "__main__":
print(repr(y))
"""
Example of using the default "tpe_or_gp" algorithm.
To run this example, just run:
> bbopt ./tpe_or_gp_example.py
"""
# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(file=__file__)
if __name__ == "__main__":
bb.run() # alg="tpe_or_gp" should be the default
# We set u ~ normal(0, 1) * sin(normal(0, 1)).
from math import sin
u = bb.normalvariate("x0", 0, 1) * sin(bb.normalvariate("x1", 0, 1))
# Since we used hyperopt-only parameters, we shouldn't have skopt.
if hasattr(bb.backend, "selected_backend"):
bb.remember({"backend": bb.backend.selected_backend})
assert bb.backend.selected_backend != "scikit-optimize", bb.backend.selected_backend
else:
bb.remember({"backend": bb.backend.backend_name})
# Set u as the thing to minimize.
bb.minimize(u)
# Print out the value we used for debugging purposes.
if __name__ == "__main__":
print(repr(u))
