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from __future__ import annotations
from pathlib import Path
import numpy as np
import pytest
from scipy.optimize import NonlinearConstraint
from bayes_opt import BayesianOptimization
from bayes_opt.util import load_logs
test_dir = Path(__file__).parent.resolve()
def test_logs():
def f(x, y):
return -(x**2) - (y - 1) ** 2 + 1
optimizer = BayesianOptimization(f=f, pbounds={"x": (-200, 200), "y": (-200, 200)})
assert len(optimizer.space) == 0
load_logs(optimizer, [str(test_dir / "test_logs.log")])
assert len(optimizer.space) == 5
load_logs(optimizer, [str(test_dir / "test_logs.log")])
assert len(optimizer.space) == 5
other_optimizer = BayesianOptimization(f=lambda x: -(x**2), pbounds={"x": (-2, 2)})
with pytest.raises(ValueError):
load_logs(other_optimizer, [str(test_dir / "test_logs.log")])
def test_logs_str():
def f(x, y):
return -(x**2) - (y - 1) ** 2 + 1
optimizer = BayesianOptimization(f=f, pbounds={"x": (-200, 200), "y": (-200, 200)})
assert len(optimizer.space) == 0
load_logs(optimizer, str(test_dir / "test_logs.log"))
assert len(optimizer.space) == 5
def test_logs_bounds():
def f(x, y):
return x + y
optimizer = BayesianOptimization(f=f, pbounds={"x": (-2, 2), "y": (-2, 2)})
with pytest.warns(UserWarning):
load_logs(optimizer, [str(test_dir / "test_logs_bounds.log")])
assert len(optimizer.space) == 5
def test_logs_constraint():
def f(x, y):
return -(x**2) - (y - 1) ** 2 + 1
def c(x, y):
return x**2 + y**2
constraint = NonlinearConstraint(c, -np.inf, 3)
optimizer = BayesianOptimization(f=f, pbounds={"x": (-200, 200), "y": (-200, 200)}, constraint=constraint)
with pytest.raises(KeyError):
load_logs(optimizer, [str(test_dir / "test_logs.log")])
load_logs(optimizer, [str(test_dir / "test_logs_constrained.log")])
assert len(optimizer.space) == 7
def test_logs_constraint_new_array():
def f(x, y):
return -(x**2) - (y - 1) ** 2 + 1
def c(x, y):
return np.array(
[-np.cos(x) * np.cos(y) + np.sin(x) * np.sin(y), -np.cos(x) * np.cos(-y) + np.sin(x) * np.sin(-y)]
)
constraint_lower = np.array([-np.inf, -np.inf])
constraint_upper = np.array([0.6, 0.6])
constraint = NonlinearConstraint(c, constraint_lower, constraint_upper)
optimizer = BayesianOptimization(f=f, pbounds={"x": (-200, 200), "y": (-200, 200)}, constraint=constraint)
with pytest.raises(KeyError):
load_logs(optimizer, [str(test_dir / "test_logs.log")])
with pytest.raises(ValueError):
load_logs(optimizer, [str(test_dir / "test_logs_constrained.log")])
load_logs(optimizer, [str(test_dir / "test_logs_multiple_constraints.log")])
print(optimizer.space)
assert len(optimizer.space) == 12
if __name__ == "__main__":
r"""
CommandLine:
python tests/test_target_space.py
"""
pytest.main([__file__])
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