> 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.
if __name__ == "__main__":
print(repr(u))
"""
Example of using BBopt with run_meta.
To run this example, just run:
> bbopt ./meta_example.py
"""
# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(file=__file__)
if __name__ == "__main__":
bb.run_meta(
algs=(
"random",
"tree_structured_parzen_estimator",
"gaussian_process",
),
meta_alg="epsilon_greedy",
)
# We set u ~ uniform(0, 1) * sin(uniform(0, 1)).
from math import sin
u = bb.random("x0") * sin(bb.random("x1"))
# Set u as the thing to minimize.
bb.minimize(u)
# Finally, we'll print out the value we used for debugging purposes.
if __name__ == "__main__":
print(repr(u))
