def main():
spaceopt = SpaceOpt(search_space=search_space(),
target_name='y',
objective='min')
print(spaceopt)
best_spoint = None
best_y = 123456789.0
for iteration in range(1, spaceopt.space.size + 1):
if iteration <= 10:
spoint = spaceopt.get_random(num_spoints=1, sample_size=1000)
spoint_type = 'random'
else:
spoint = spaceopt.fit_predict(num_spoints=1, sample_size=1000)
spoint_type = 'fit_predict'
spoint['y'] = evaluation_function(spoint)
spaceopt.append_evaluated_spoint(spoint)
if spoint['y'] < best_y:
best_y = spoint['y']
best_spoint = spoint
print(
f'{iteration}, y={round(best_y, 6)}, {spoint_type}, {best_spoint}'
)
if best_y < -6.289:
print('global minimum has been found\n'
f'{iteration}/{spaceopt.space.size} = '
f'{iteration / spaceopt.space.size}')
return
def test_SpaceOpt_get_random():
spaceopt = SpaceOpt(search_space=search_space(),
target_name='y',
objective='min')
try:
spaceopt.get_random(num_spoints=1.0)
except Exception as e:
assert isinstance(e, TypeError)
assert str(
e
) == "num_spoints is of type <class 'float'>, but it should be of type <class 'int'>."
try:
spaceopt.get_random(num_spoints=0)
except Exception as e:
assert isinstance(e, ValueError)
assert str(e) == "num_spoints should be greater than 0."
try:
spaceopt.get_random(sample_size=100.0)
except Exception as e:
assert isinstance(e, TypeError)
assert str(
e
) == "sample_size is of type <class 'float'>, but it should be of type <class 'int'>."
try:
spaceopt.get_random(sample_size=0)
except Exception as e:
assert isinstance(e, ValueError)
assert str(e) == "sample_size should be greater than 0."
spoints = spaceopt.get_random(num_spoints=5, sample_size=1000)
assert isinstance(spoints, list)
assert len(spoints) == 5
spoint = spaceopt.get_random(num_spoints=1, sample_size=10)
assert isinstance(spoint, dict)
assert set(spoint.keys()) == set(spoints[-1].keys())