def lbest_reset():
"""Returns a LocalBestPSO instance that has been run and reset to check
default value"""
pso = LocalBestPSO(10, 2, {'c1': 0.5, 'c2': 0.7, 'w': 0.5, 'k': 2, 'p': 2})
pso.optimize(sphere_func, 10, verbose=0)
pso.reset()
return pso
def lbest_reset():
"""Returns a LocalBestPSO instance that has been run and reset to check
default value"""
pso = LocalBestPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2})
pso.optimize(sphere_func, 10, verbose=0)
pso.reset()
return pso
def test_ftol_effect(self):
"""Check if setting ftol breaks the optimization process
accordingly."""
# Perform a simple optimization
optimizer = LocalBestPSO(10, 2, options=self.options, ftol=1e-1)
optimizer.optimize(sphere_func, 5000, verbose=0)
cost_hist = optimizer.get_cost_history
self.assertNotEqual(cost_hist.shape, (5000, ))
def test_ftol_effect(self):
"""Check if setting ftol breaks the optimization process
accordingly."""
# Perform a simple optimization
optimizer = LocalBestPSO(10,2, options=self.options, ftol=1e-1)
optimizer.optimize(sphere_func, 5000, verbose=0)
cost_hist = optimizer.get_cost_history
self.assertNotEqual(cost_hist.shape, (5000, ))
def test_reset_best_cost_inf(self):
"""Tests if best cost is set to infinity when reset() is called"""
# Perform a simple optimization
optimizer = LocalBestPSO(5, 2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
optimizer.reset()
self.assertEqual(optimizer.best_cost, np.inf)
def test_reset_best_pos_none(self):
"""Tests if best pos is set to NoneType when reset() is called"""
# Perform a simple optimization
optimizer = LocalBestPSO(5,2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
optimizer.reset()
self.assertIsNone(optimizer.best_pos)
def test_reset_best_cost_inf(self):
"""Tests if best cost is set to infinity when reset() is called"""
# Perform a simple optimization
optimizer = LocalBestPSO(5,2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
optimizer.reset()
self.assertEqual(optimizer.best_cost, np.inf)
def test_reset_best_pos_none(self):
"""Tests if best pos is set to NoneType when reset() is called"""
# Perform a simple optimization
optimizer = LocalBestPSO(5, 2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
optimizer.reset()
self.assertIsNone(optimizer.best_pos)
def test_reset(self):
"""Tests if the reset method resets the attributes required"""
# Perform a simple optimization
optimizer = LocalBestPSO(5,2, options=self.options)
optimizer.optimize(sphere_func, 100, verbose=0)
# Reset the attributes
optimizer.reset()
# Perform testing
self.assertEqual(optimizer.best_cost, np.inf)
self.assertIsNone(optimizer.best_pos)
def test_local_kwargs(func):
"""Tests if kwargs are passed properly to the objective function for when kwargs are present"""
# setup optimizer
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
x_max = 10 * np.ones(2)
x_min = -1 * x_max
bounds = (x_min, x_max)
opt_ps = LocalBestPSO(n_particles=100,
dimensions=2,
options=options,
bounds=bounds)
# run it
cost, pos = opt_ps.optimize(func,
1000,
print_step=10,
verbose=3,
a=1,
b=100)
assert np.isclose(cost, 0, rtol=1e-03)
assert np.isclose(pos[0], 1.0, rtol=1e-03)
assert np.isclose(pos[1], 1.0, rtol=1e-03)
def test_local_correct_pos(self, options):
""" Test to check local optimiser returns the correct position corresponding to the best cost """
opt = LocalBestPSO(n_particles=10, dimensions=2, options=options)
cost, pos = opt.optimize(sphere, iters=5)
# find best pos from history
min_cost_idx = np.argmin(opt.cost_history)
min_pos_idx = np.argmin(sphere(opt.pos_history[min_cost_idx]))
assert np.array_equal(opt.pos_history[min_cost_idx][min_pos_idx], pos)
def test_local_missed_kwargs(func):
"""Tests kwargs are passed the objective function for when kwargs do not exist"""
# setup optimizer
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 2, 'p': 2}
x_max = 10 * np.ones(2)
x_min = -1 * x_max
bounds = (x_min, x_max)
opt_ps = LocalBestPSO(n_particles=100,
dimensions=2,
options=options,
bounds=bounds)
# run it
with pytest.raises(TypeError) as excinfo:
cost, pos = opt_ps.optimize(func, 1000, print_step=10, verbose=3, a=1)
assert 'missing 1 required positional argument' in str(excinfo.value)
def test_local_missed_kwargs(func):
"""Tests kwargs are passed the objective function for when kwargs do not exist"""
# setup optimizer
options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
x_max = 10 * np.ones(2)
x_min = -1 * x_max
bounds = (x_min, x_max)
opt_ps = LocalBestPSO(n_particles=100,
dimensions=2,
options=options,
bounds=bounds)
# run it
with pytest.raises(TypeError) as excinfo:
cost, pos = opt_ps.optimize(func, 1000, a=1)
assert "missing 1 required positional argument" in str(excinfo.value)
def test_local_wrong_kwargs(func):
"""Tests kwargs are passed the objective function for when kwargs do not exist"""
# setup optimizer
options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
x_max = 10 * np.ones(2)
x_min = -1 * x_max
bounds = (x_min, x_max)
opt_ps = LocalBestPSO(n_particles=100,
dimensions=2,
options=options,
bounds=bounds)
# run it
with pytest.raises(TypeError) as excinfo:
cost, pos = opt_ps.optimize(func, 1000, print_step=10, c=1, d=100)
assert "unexpected keyword" in str(excinfo.value)
def test_local_kwargs(func):
"""Tests if kwargs are passed properly to the objective function for when kwargs are present"""
# setup optimizer
options = {"c1": 0.5, "c2": 0.3, "w": 0.9, "k": 2, "p": 2}
x_max = 10 * np.ones(2)
x_min = -1 * x_max
bounds = (x_min, x_max)
opt_ps = LocalBestPSO(n_particles=100,
dimensions=2,
options=options,
bounds=bounds)
# run it
cost, pos = opt_ps.optimize(func, 1000, a=1, b=100)
assert np.isclose(cost, 0, rtol=1e-03)
assert np.isclose(pos[0], 1.0, rtol=1e-03)
assert np.isclose(pos[1], 1.0, rtol=1e-03)
def optimizer_reset(self, options):
opt = LocalBestPSO(10, 2, options)
opt.optimize(sphere, 10)
opt.reset()
return opt
def optimizer_history(self, options):
opt = LocalBestPSO(10, 2, options)
opt.optimize(sphere, 1000)
return opt
def test_ftol_effect(options):
"""Test if setting the ftol breaks the optimization process accodingly"""
pso = LocalBestPSO(10, 2, options=options, ftol=1e-1)
pso.optimize(sphere_func, 2000, verbose=0)
assert np.array(pso.cost_history).shape != (2000,)
def lbest_history():
"""Returns a LocalBestPSO instance run for 1000 iterations for checking
history"""
pso = LocalBestPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2})
pso.optimize(sphere_func, 1000, verbose=0)
return pso
def lbest_history():
"""Returns a LocalBestPSO instance run for 1000 iterations for checking
history"""
pso = LocalBestPSO(10, 2, {'c1': 0.5, 'c2': 0.7, 'w': 0.5, 'k': 2, 'p': 2})
pso.optimize(sphere_func, 1000, verbose=0)
return pso