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import argparse
import numpy as np
from skopt import dummy_minimize, forest_minimize, gbrt_minimize, gp_minimize
from skopt.benchmarks import branin
def run(n_calls=200, n_runs=10, acq_optimizer="lbfgs"):
bounds = [(-5.0, 10.0), (0.0, 15.0)]
optimizers = [
("gp_minimize", gp_minimize),
("forest_minimize", forest_minimize),
("gbrt_minimize", gbrt_minimize),
("dummy_minimize", dummy_minimize),
]
for name, optimizer in optimizers:
print(name)
results = []
min_func_calls = []
for random_state in range(n_runs):
if name == "gp_minimize":
res = optimizer(
branin,
bounds,
random_state=random_state,
n_calls=n_calls,
noise=1e-10,
verbose=True,
acq_optimizer=acq_optimizer,
n_jobs=-1,
)
elif name == "dummy_minimize":
res = optimizer(
branin, bounds, random_state=random_state, n_calls=n_calls
)
else:
res = optimizer(
branin,
bounds,
random_state=random_state,
n_calls=n_calls,
acq_optimizer=acq_optimizer,
)
results.append(res)
func_vals = np.round(res.func_vals, 3)
min_func_calls.append(np.argmin(func_vals) + 1)
optimal_values = [result.fun for result in results]
mean_optimum = np.mean(optimal_values)
std = np.std(optimal_values)
best = np.min(optimal_values)
print("Mean optimum: " + str(mean_optimum))
print("Std of optimal values" + str(std))
print("Best optima:" + str(best))
mean_fcalls = np.mean(min_func_calls)
std_fcalls = np.std(min_func_calls)
best_fcalls = np.min(min_func_calls)
print("Mean func_calls to reach min: " + str(mean_fcalls))
print("Std func_calls to reach min: " + str(std_fcalls))
print("Fastest no of func_calls to reach min: " + str(best_fcalls))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
'--n_calls', nargs="?", default=50, type=int, help="Number of function calls."
)
parser.add_argument(
'--n_runs', nargs="?", default=5, type=int, help="Number of runs."
)
parser.add_argument(
'--acq_optimizer',
nargs="?",
default="lbfgs",
type=str,
help="Acquistion optimizer.",
)
args = parser.parse_args()
run(args.n_calls, args.n_runs, args.acq_optimizer)
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