def test_init_correct_dimensions_best_score_glob():
"""Test if the initialized best score of all particles have the correct dimension."""
pso = ParticleSwarmOptimizer(func=opt_func, maximize=False, particles=20)
params = {'x': (-1, 1), 'y': (-1, 1)}
pso.init(params=params, random_state=1)
print('best score', pso.score)
assert np.shape(pso.score) == ()
def test_init_correct_dimensions_best_scores():
"""Test if the initialized best scores of each particle have the correct dimensions."""
pso = ParticleSwarmOptimizer(func=opt_func, maximize=False, particles=20)
params = {'x': (-1, 1), 'y': (-1, 1)}
pso.init(params=params, random_state=1)
assert len(pso._score_all) == 20
def test_init_correct_dimensions_best_coords_glob():
"""Test if the initialized best coordinates of all particles combined have the correct dimensions."""
pso = ParticleSwarmOptimizer(func=opt_func, maximize=False, particles=20)
params = {'x': (-1, 1), 'y': (-1, 1)}
pso.init(params=params, random_state=1)
assert pso.coords.shape == (2,)
def test_init_correct_dimensions_coords():
"""Test if the initialized coordinates have the correct dimension."""
pso = ParticleSwarmOptimizer(func=opt_func, maximize=False, particles=20)
params = {'x': (-1, 1), 'y': (-1, 1)}
pso.init(params=params, random_state=1)
assert pso._coords_all.shape == (20, 2)
def test_init_different_random_state():
"""Test if the initialized coordinates are not deterministic if random state is not fixed."""
pso = ParticleSwarmOptimizer(func=opt_func, maximize=False, particles=20)
params = {'x': (-1, 1), 'y': (-1, 1)}
pso.init(params=params, random_state=1)
coords0 = pso._coords_all
pso.init(params=params, random_state=2)
coords1 = pso._coords_all
assert any(val0 != val1 for row0, row1 in zip(coords0, coords1) for val0, val1 in zip(row0, row1))
def test_update_monotonic_best_score_glob_maximize():
"""Test if the particle swarm optimizer monotonically converges for maximization problems."""
pso = ParticleSwarmOptimizer(func=opt_func_inv, maximize=True, particles=20)
params = {'x': (-1, 1), 'y': (-1, 1)}
pso.init(params=params, random_state=1)
scores = [pso.score]
for i in range(100):
pso.update(params)
scores.append(pso.score)
assert all(scores[i+1] >= scores[i] for i in range(len(scores)-1))
def test_update_monotonic_best_scores_maximize():
"""Test if each particle of the particle swarm optimizer monotonically converges for maximization problems."""
pso = ParticleSwarmOptimizer(func=opt_func_inv, maximize=True, particles=20)
params = {'x': (-1, 1), 'y': (-1, 1)}
pso.init(params=params, random_state=1)
scores = {p: [pso._score_all[p]] for p in range(20)}
for i in range(100):
pso.update(params)
for particle in range(20):
scores[particle] = scores[particle] + [pso._score_all[particle]]
assert all(all(scores[particle][i+1] >= scores[particle][i] for i in range(len(scores[particle])-1))
for particle in range(20))