def gbest_reset(): """Returns a GlobalBestPSO instance that has been run and reset to check default value""" pso = GlobalBestPSO(10, 2, {'c1': 0.5, 'c2': 0.7, 'w': 0.5}) 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 = GlobalBestPSO(5,2, options=self.options) optimizer.optimize(sphere_func, 100, verbose=0) optimizer.reset() self.assertIsNone(optimizer.best_pos)
def test_ftol_effect(self): """Check if setting ftol breaks the optimization process accordingly.""" # Perform a simple optimization optimizer = GlobalBestPSO(10,2, options=self.options, ftol=1e-1) optimizer.optimize(sphere_func, 2000, verbose=0) cost_hist = optimizer.get_cost_history self.assertNotEqual(cost_hist.shape, (2000, ))
def test_reset_best_cost_inf(self): """Tests if best cost is set to infinity when reset() is called""" # Perform a simple optimization optimizer = GlobalBestPSO(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 = GlobalBestPSO(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_global_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 = GlobalBestPSO(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_global_correct_pos(self, options): """ Test to check global optimiser returns the correct position corresponding to the best cost """ opt = GlobalBestPSO(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_orct12_optimization(self): bayer = self.datasetUtils.readCFAImages() twoComplement = self.datasetUtils.twoComplementMatrix(bayer) twoComplement = twoComplement.astype("float32") options = {'c1': 0.5, 'c2': 0.1, 'w': 0.9} optimizer = GlobalBestPSO(n_particles=10, dimensions=4, options=options) costFunction = partial(self.opt_func, twoComplement=twoComplement) cost, pos = optimizer.optimize(costFunction, iters=30) pass
def test_global_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 = GlobalBestPSO(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_global_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 = GlobalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds) # run it with pytest.raises(TypeError) as excinfo: cost, pos = opt_ps.optimize(func, 1000, c=1, d=100) assert "unexpected keyword" in str(excinfo.value)
def test_global_no_kwargs(func): """Tests if args are passed properly to the objective function for when no args 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 = GlobalBestPSO(n_particles=100, dimensions=2, options=options, bounds=bounds) # run it cost, pos = opt_ps.optimize(func, 1000) 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 run(self, particles, print_step=100, iters=1000, verbose=3): # options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9} # Call instance of PSO optimizer = GlobalBestPSO(n_particles=particles, dimensions=len(self.hyperparameters), options=self.options, bounds=(np.array(self.param_minimums), np.array(self.param_maximums)), init_pos=self.position) # Perform optimization best_cost, best_position = optimizer.optimize(self.cost_func, print_step=print_step, iters=iters, verbose=verbose) if best_cost < self.cutoff: self.save_position(best_cost, best_position) else: # doesnt score well enough print('not high')
def test_global_kwargs_without_named_arguments(func): """Tests if kwargs are passed properly to the objective function for when kwargs are present and other named arguments are not passed, such as print_step""" # 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 = GlobalBestPSO(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_history(self, options): opt = GlobalBestPSO(10, 2, options=options) opt.optimize(sphere, 1000) return opt
def test_ftol_effect(options): """Test if setting the ftol breaks the optimization process accodingly""" pso = GlobalBestPSO(10, 2, options=options, ftol=1e-1) pso.optimize(sphere_func, 2000, verbose=0) assert np.array(pso.cost_history).shape != (2000, )
class ParticleSwarms(AbstractPlanner): def __init__(self, goal='minimize', max_iters=10**8, options={ 'c1': 0.5, 'c2': 0.3, 'w': 0.9 }, particles=10): """ Particle swarm optimizer. Args: goal (str): The optimization goal, either 'minimize' or 'maximize'. Default is 'minimize'. max_iters (int): The maximum number of iterations for the swarm to search. options (dict): ??? particles (int): The number of particles in the swarm. """ AbstractPlanner.__init__(**locals()) self.has_optimizer = False self.is_converged = False def _set_param_space(self, param_space): self.param_space = param_space def _tell(self, observations): self._params = observations.get_params(as_array=False) self._values = observations.get_values(as_array=True, opposite=self.flip_measurements) if len(self._values) > 0: self.RECEIVED_VALUES.append(self._values[-1]) def _priv_evaluator(self, params_array): for params in params_array: params = self._project_into_domain(params) self.SUBMITTED_PARAMS.append(params) while len(self.RECEIVED_VALUES) < self.particles: time.sleep(0.1) measurements = np.array(self.RECEIVED_VALUES) self.RECEIVED_VALUES = [] return measurements @daemon def create_optimizer(self): from pyswarms.single import GlobalBestPSO self.optimizer = GlobalBestPSO(n_particles=self.particles, options=self.options, dimensions=len(self.param_space)) cost, pos = self.optimizer.optimize(self._priv_evaluator, iters=self.max_iters) self.is_converged = True def _ask(self): if self.has_optimizer is False: self.create_optimizer() self.has_optimizer = True while len(self.SUBMITTED_PARAMS) == 0: time.sleep(0.1) if self.is_converged: return ParameterVector().from_dict(self._params[-1]) params = self.SUBMITTED_PARAMS.pop(0) return ParameterVector().from_array(params, self.param_space)
def trained_optimizer(): """Returns a trained optimizer instance with 100 iterations""" options = {"c1": 0.5, "c2": 0.3, "w": 0.9} optimizer = GlobalBestPSO(n_particles=10, dimensions=2, options=options) optimizer.optimize(sphere, iters=100) return optimizer
def gbest_history(): """Returns a GlobalBestPSO instance run for 1000 iterations for checking history""" pso = GlobalBestPSO(10, 2, {'c1': 0.5, 'c2': 0.7, 'w': 0.5}) pso.optimize(sphere_func, 1000, verbose=0) return pso
def optimizer_reset(self, options): opt = GlobalBestPSO(10, 2, options=options) opt.optimize(sphere, 10) opt.reset() return opt