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_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_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_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_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_bound_maxmin_fail(self):
"""Tests if exception is thrown when min max of the bound is
wrong."""
bounds_1 = (np.array([5,5]), np.array([-5,-5]))
bounds_2 = (np.array([5,-5]), np.array([-5,5]))
with self.assertRaises(ValueError):
optimizer = LocalBestPSO(5,2, bounds=bounds_1, options=self.options)
with self.assertRaises(ValueError):
optimizer = LocalBestPSO(5,2, bounds=bounds_2, options=self.options)
def test_k_fail(self):
"""Tests if exception is thrown when feeding an invalid k."""
k_less_than_min = {'c1':0.5, 'c2':0.7, 'w':0.5, 'k':-1, 'p':2}
k_more_than_max = {'c1':0.5, 'c2':0.7, 'w':0.5, 'k':6, 'p':2}
with self.assertRaises(ValueError):
optimizer = LocalBestPSO(5,2, bounds=self.safe_bounds, options=k_less_than_min)
with self.assertRaises(ValueError):
optimizer = LocalBestPSO(5,2, bounds=self.safe_bounds, options=k_more_than_max)
def test_vclamp_shape_fail(self):
"""Tests if exception is thrown when velocity_clamp is not equal to 2"""
velocity_clamp = (1, 1, 1)
with self.assertRaises(IndexError):
optimizer = LocalBestPSO(5,
2,
velocity_clamp=velocity_clamp,
options=self.options)
def test_keyword_check_fail(self):
"""Tests if exceptions are thrown when keywords are missing"""
check_c1 = {'c2': 0.7, 'w': 0.5, 'k': 2, 'p': 2}
check_c2 = {'c1': 0.5, 'w': 0.5, 'k': 2, 'p': 2}
check_m = {'c1': 0.5, 'c2': 0.7, 'k': 2, 'p': 2}
check_k = {'c1': 0.5, 'c2': 0.7, 'w': 0.5, 'p': 2}
check_p = {'c1': 0.5, 'c2': 0.7, 'w': 0.5, 'k': 2}
with self.assertRaises(KeyError):
optimizer = LocalBestPSO(5, 2, options=check_c1)
with self.assertRaises(KeyError):
optimizer = LocalBestPSO(5, 2, options=check_c2)
with self.assertRaises(KeyError):
optimizer = LocalBestPSO(5, 2, options=check_m)
with self.assertRaises(KeyError):
optimizer = LocalBestPSO(5, 2, options=check_k)
with self.assertRaises(KeyError):
optimizer = LocalBestPSO(5, 2, options=check_p)
def test_init_pos_type_fail(self):
"""Tests if exception is thrown when init_pos is not a list."""
init_pos = (0.1, 1.5)
with self.assertRaises(TypeError):
optimizer = LocalBestPSO(5,
2,
init_pos=init_pos,
options=self.options)
def test_vclamp_minmax_fail(self):
"""Tests if exception is thrown when velocity_clamp's minmax is wrong"""
velocity_clamp = (3, 2)
with self.assertRaises(ValueError):
optimizer = LocalBestPSO(5,
2,
velocity_clamp=velocity_clamp,
options=self.options)
def test_vclamp_type_fail(self):
"""Tests if exception is thrown when velocity_clamp is not a tuple."""
velocity_clamp = [1, 3]
with self.assertRaises(TypeError):
optimizer = LocalBestPSO(5,
2,
velocity_clamp=velocity_clamp,
options=self.options)
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_init_pos_shape_fail(self):
"""Tests if exception is thrown when init_pos dimension is not equal
to dimensions"""
init_pos = [1.5, 3.2, 2.5]
with self.assertRaises(IndexError):
optimizer = LocalBestPSO(5,
2,
init_pos=init_pos,
options=self.options)
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_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 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_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(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_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 optimizer_reset(self, options):
opt = LocalBestPSO(10, 2, options)
opt.optimize(sphere, 10)
opt.reset()
return opt
def test_keyword_exception(options):
"""Tests if exceptions are thrown when keywords are missing"""
with pytest.raises(KeyError):
LocalBestPSO(5, 2, options)
def test_vclamp_maxmin_exception(velocity_clamp, options):
"""Tests if the max velocity_clamp is less than min velocity_clamp and
vice-versa"""
with pytest.raises(ValueError):
LocalBestPSO(5, 2, velocity_clamp=velocity_clamp, options=options)
def optimizer_history(self, options):
opt = LocalBestPSO(10, 2, options)
opt.optimize(sphere, 1000)
return opt
def test_vclamp_shape_exception(velocity_clamp, options):
"""Tests if exception is raised when velocity_clamp's size is not equal
to 2"""
with pytest.raises(IndexError):
LocalBestPSO(5, 2, velocity_clamp=velocity_clamp, options=options)
def test_bound_type_exception(bounds, options):
"""Tests if exception is raised when bound type is not a tuple"""
with pytest.raises(TypeError):
LocalBestPSO(5, 2, options=options, bounds=bounds)
def test_bounds_maxmin_exception(bounds, options):
"""Tests if the max bounds is less than min bounds and vice-versa"""
with pytest.raises(ValueError):
LocalBestPSO(5, 2, options=options, bounds=bounds)
def test_bounds_size_exception(bounds, options):
"""Tests if exceptions are raised when bound sizes are wrong"""
with pytest.raises(IndexError):
LocalBestPSO(5, 2, options=options, bounds=bounds)
def test_invalid_k_or_p_values(options):
"""Tests if exception is thrown when passing
an invalid value for k or p"""
with pytest.raises(ValueError):
LocalBestPSO(5, 2, options)