def test_resetting_planner(two_param_space):
planner = Genetic(pop_size=3)
planner.set_param_space(param_space=two_param_space)
# run once
obs = Observations()
for i in range(5):
param = planner.recommend(observations=obs)
obj = np.sum(param.to_array() ** 2)
value = ParameterVector(dict={'objective': obj})
obs.add_observation(param, value)
# run again from scratch
planner.reset()
obs = Observations()
for i in range(5):
param = planner.recommend(observations=obs)
obj = np.sum(param.to_array() ** 2)
value = ParameterVector(dict={'objective': obj})
obs.add_observation(param, value)
def test_declaration():
np.random.seed(100691)
param_vects = np.random.uniform(low=0, high=1, size=(3, 2))
values_vects = np.random.uniform(low=0, high=1, size=(3, 1))
params = []
values = []
for param_vect, value_vect in zip(param_vects, values_vects):
params.append(
ParameterVector().from_dict({"p0": param_vect[0], "p1": param_vect[1]})
)
values.append(ParameterVector().from_dict({"obj0": value_vect[0]}))
observations = Observations()
observations.add_observation(params, values)
test_params = [param.to_array() for param in params]
test_values = [value.to_array() for value in values]
assert np.linalg.norm(test_params - observations.get_params()) < 1e-7
assert np.linalg.norm(test_values - observations.get_values()) < 1e-7