def test_store_best_agent_only():
pso = PSO()
n_iters = 10
target_fn = Function(lambda x: x**2)
space = SearchSpace(lower_bound=[-10],
upper_bound=[10],
n_iterations=n_iters)
history = Opytimizer(space, pso, target_fn).start(store_best_only=True)
assert not hasattr(history, 'agents')
assert hasattr(history, 'best_agent')
assert len(history.best_agent) == n_iters
def test_store_all_agents():
pso = PSO()
n_iters = 10
n_agents = 2
target_fn = Function(lambda x: x**2)
space = SearchSpace(lower_bound=[-10],
upper_bound=[10],
n_iterations=n_iters,
n_agents=n_agents)
history = Opytimizer(space, pso, target_fn).start()
assert hasattr(history, 'agents')
# Ensuring that the amount of entries is the same as the amount of iterations and
# that for each iteration all agents are kept
assert len(history.agents) == n_iters
assert all(len(iter_agents) == n_agents for iter_agents in history.agents)
assert hasattr(history, 'best_agent')
assert len(history.best_agent) == n_iters
def test_hook():
pso = PSO()
n_iters = 10
counter = 0
target_fn = Function(lambda x: x**2)
space = SearchSpace(lower_bound=[-10],
upper_bound=[10],
n_iterations=n_iters,
n_agents=15)
def eval_hook(arg_opt, arg_space, arg_target_fn):
assert arg_opt is pso
assert arg_space is space
assert arg_target_fn is target_fn
nonlocal counter
counter += 1
Opytimizer(space, pso, target_fn).start(pre_evaluation_hook=eval_hook)
# The hook is evaluated for each iteration plus initialization
assert counter == n_iters + 1
# Number of decision variables
n_variables = 1
# Number of running iterations
n_iterations = 100
# Lower and upper bounds (has to be the same size as n_variables)
lower_bound = [0.00001]
upper_bound = [10]
# Creating the SearchSpace class
s = SearchSpace(n_agents=n_agents, n_iterations=n_iterations,
n_variables=n_variables, lower_bound=lower_bound,
upper_bound=upper_bound)
# Hyperparameters for the optimizer
hyperparams = {
'w': 0.7,
'c1': 1.7,
'c2': 1.7
}
# Creating PSO's optimizer
p = PSO(hyperparams=hyperparams)
# Finally, we can create an Opytimizer class
o = Opytimizer(space=s, optimizer=p, function=f)
# Running the optimization task
history = o.start()
