def test_rpso_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_rpso = pso.RPSO()
search_space = search.SearchSpace(n_agents=5,
n_iterations=20,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_rpso.run(search_space, new_function, pre_evaluation=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
assert len(history.local) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm rpso failed to converge.'
def test_ssd_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {'c': 2.0, 'decay': 0.3}
new_ssd = ssd.SSD(hyperparams=hyperparams)
search_space = search.SearchSpace(n_agents=50,
n_iterations=350,
n_variables=2,
lower_bound=[-100, -100],
upper_bound=[100, 100])
history = new_ssd.run(search_space, new_function, pre_evaluation=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm ssd failed to converge.'
def test_gsa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {'G': 100}
new_gsa = gsa.GSA(hyperparams=hyperparams)
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_gsa.run(search_space, new_function, pre_evaluation=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm gsa failed to converge.'
def test_discrete_search_callback_on_task_begin():
space = search.SearchSpace(1, 2, [0, 0], [1, 1])
optimizer = swarm.PSO()
func = function.Function(Sphere())
opt_model = Opytimizer(space, optimizer, func)
try:
allowed_values = [[1]]
new_discrete_search_callback = callback.DiscreteSearchCallback(
allowed_values=allowed_values)
new_discrete_search_callback.on_task_begin(opt_model)
except:
allowed_values = [[1], [1]]
new_discrete_search_callback = callback.DiscreteSearchCallback(
allowed_values=allowed_values)
new_discrete_search_callback.on_task_begin(opt_model)
try:
allowed_values = [[10], [10]]
new_discrete_search_callback = callback.DiscreteSearchCallback(
allowed_values=allowed_values)
new_discrete_search_callback.on_task_begin(opt_model)
except:
allowed_values = [[1], [1]]
new_discrete_search_callback = callback.DiscreteSearchCallback(
allowed_values=allowed_values)
new_discrete_search_callback.on_task_begin(opt_model)
def test_wdo_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {'v_max': 0.3, 'alpha': 0.8, 'g': 0.6, 'c': 1.0, 'RT': 1.5}
new_wdo = wdo.WDO(hyperparams=hyperparams)
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_wdo.run(search_space, new_function, pre_evaluation=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm wdo failed to converge.'
def test_opytimizer_update_args():
space = search.SearchSpace(1, 1, 0, 1)
func = function.Function(callable)
optimizer = pso.PSO()
new_opytimizer = opytimizer.Opytimizer(space, optimizer, func)
assert len(new_opytimizer.update_args) == 1
def test_opytimizer_start():
space = search.SearchSpace(1, 1, 0, 1)
func = function.Function(callable)
optimizer = pso.PSO()
new_opytimizer = opytimizer.Opytimizer(space, optimizer, func)
new_opytimizer.start(n_iterations=1)
def test_opytimizer_save():
space = search.SearchSpace(1, 1, 0, 1)
func = function.Function(callable)
optimizer = pso.PSO()
new_opytimizer = opytimizer.Opytimizer(space, optimizer, func)
new_opytimizer.save('out.pkl')
def test_opytimizer_history():
space = search.SearchSpace(1, 1, 0, 1)
func = function.Function(callable)
optimizer = pso.PSO()
new_opytimizer = opytimizer.Opytimizer(space, optimizer, func)
assert type(new_opytimizer.history).__name__ == 'History'
def test_opytimizer_total_iterations():
space = search.SearchSpace(1, 1, 0, 1)
func = function.Function(callable)
optimizer = pso.PSO()
new_opytimizer = opytimizer.Opytimizer(space, optimizer, func)
assert new_opytimizer.total_iterations == 0
def test_function_pointer_setter():
def square(x):
return x**2
assert square(2) == 4
new_function = function.Function(pointer=square)
assert new_function.pointer == square
def test_bmrfo_update():
new_function = function.Function(pointer=Knapsack(
values=(55, 10, 47, 5, 4), weights=(95, 4, 60, 32, 23), max_capacity=100))
new_bmrfo = bmrfo.BMRFO()
boolean_space = boolean.BooleanSpace(n_agents=100, n_variables=5)
new_bmrfo.update(boolean_space, new_function, 1, 20)
def test_function_pointer_setter():
new_function = function.Function()
try:
new_function.pointer = 'a'
except:
new_function.pointer = callable
assert new_function.pointer.__class__.__name__ == 'builtin_function_or_method' or 'builtin_function'
def test_function_name_setter():
new_function = function.Function(pointer)
try:
new_function.name = 1
except:
new_function.name = "pointer"
assert new_function.name == "pointer"
def test_function_name_setter():
new_function = function.Function()
try:
new_function.name = 1
except:
new_function.name = 'callable'
assert new_function.name == 'callable'
def test_opytimizer_update():
space = search.SearchSpace(1, 1, 0, 1)
func = function.Function(callable)
optimizer = pso.PSO()
callbacks = callback.CallbackVessel([])
new_opytimizer = opytimizer.Opytimizer(space, optimizer, func)
new_opytimizer.update(callbacks)
def test_function_pointer_setter():
new_function = function.Function(pointer)
try:
new_function.pointer = "a"
except:
new_function.pointer = callable
assert (new_function.pointer.__class__.__name__
== "builtin_function_or_method" or "builtin_function")
def test_function_function_setter():
def square(x):
return x**2
assert square(2) == 4
try:
new_function = function.Function(pointer=0)
except:
new_function = function.Function(pointer=square)
def square2(x, y):
return x**2 + y**2
assert square2(2, 2) == 8
try:
new_function = function.Function(pointer=square2)
except:
new_function = function.Function(pointer=square)
def test_function_call():
def square(x):
return np.sum(x**2)
assert square(2) == 4
def square2(x, y):
return x**2 + y**2
assert square2(2, 2) == 8
new_function = function.Function(square)
assert new_function(np.zeros(2)) == 0
try:
new_function = function.Function(square2)
except:
new_function = function.Function(square)
assert new_function.name == "square"
def test_function():
class Square():
def __call__(self, x):
return np.sum(x**2)
s = Square()
assert s(2) == 4
new_function = function.Function(pointer=s)
assert new_function.name == 'Square'
def test_optimizer_evaluate():
new_function = function.Function(pointer=square)
search_space = search.SearchSpace(n_agents=20, n_iterations=10,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
new_optimizer = optimizer.Optimizer()
new_optimizer._evaluate(search_space, new_function)
assert search_space.best_agent.fit < sys.float_info.max
def test_optimizer_evaluate():
def square(x):
return np.sum(x**2)
new_function = function.Function(square)
new_search_space = search.SearchSpace(n_agents=1, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_optimizer = optimizer.Optimizer()
new_optimizer.evaluate(new_search_space, new_function)
assert new_search_space.best_agent.fit < sys.float_info.max
def test_cem_update():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_cem = cem.CEM()
new_cem.compile(search_space)
new_cem.update(search_space, new_function)
def test_bpso_evaluate():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
boolean_space = boolean.BooleanSpace(n_agents=2, n_variables=5)
new_bpso = bpso.BPSO()
new_bpso.compile(boolean_space)
new_bpso.evaluate(boolean_space, new_function)
assert boolean_space.best_agent.fit < sys.float_info.max
def test_opytimizer_history_setter():
space = search.SearchSpace(1, 1, 0, 1)
func = function.Function(callable)
optimizer = pso.PSO()
hist = history.History()
new_opytimizer = opytimizer.Opytimizer(space, optimizer, func)
try:
new_opytimizer.history = 1
except:
new_opytimizer.history = hist
assert type(new_opytimizer.history).__name__ == 'History'
def test_function_penalty_setter():
new_function = function.Function()
try:
new_function.penalty = 'a'
except:
new_function.penalty = 1
try:
new_function.penalty = -1
except:
new_function.penalty = 1
assert new_function.penalty == 1
def test_opytimizer_space_setter():
space = search.SearchSpace(1, 1, 0, 1)
func = function.Function(callable)
optimizer = pso.PSO()
new_opytimizer = opytimizer.Opytimizer(space, optimizer, func)
try:
space.built = False
new_opytimizer.space = space
except:
space.built = True
new_opytimizer.space = space
assert type(new_opytimizer.space).__name__ == 'SearchSpace'
def test_opytimizer_function_setter():
space = search.SearchSpace(1, 1, 0, 1)
func = function.Function(callable)
optimizer = pso.PSO()
new_opytimizer = opytimizer.Opytimizer(space, optimizer, func)
try:
func.built = False
new_opytimizer.function = func
except:
func.built = True
new_opytimizer.function = func
assert type(new_opytimizer.function).__name__ == 'Function'
def test_cs_update():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_cs = cs.CS()
search_space = search.SearchSpace(n_agents=20, n_iterations=100,
n_variables=2, lower_bound=[-10, -10],
upper_bound=[10, 10])
new_cs._update(search_space.agents, search_space.best_agent, new_function)
assert search_space.agents[0].position[0] != 0
def test_gco_update():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_gco = gco.GCO()
search_space = search.SearchSpace(n_agents=4, n_iterations=10,
n_variables=2, lower_bound=[1, 1],
upper_bound=[10, 10])
new_gco._update(search_space.agents, new_function, np.array([70, 70, 70, 70]), np.array([1, 1, 1, 1]))
assert search_space.agents[0].position[0] != 0
