def run_example():
pso = PSO(save_sampled=False, verbose=True, num_proc=4)
pso.set_cost_function(likelihood)
pso.set_start_position(xnominal)
pso.set_bounds(2)
pso.set_speed(-.25, .25)
pso.run(25, 100)
display(pso.best)
def run_example():
pso = PSO(save_sampled=False, verbose=True, num_proc=4)
pso.set_cost_function(likelihood)
pso.set_start_position(xnominal)
pso.set_bounds(lower=lower, upper=upper)
pso.set_speed(-.25, .25)
pso.run(25, 200)
display(pso.best)
np.save('calibrated_pars_pso1', pso.best)
def __call__(self, pso_kwargs=None,
cost_type='norm_logpdf', custom_cost=None):
"""Call the SwarmIt instance to construct to instance of the NestedSampling object.
Args:
pso_kwargs (dict): Dictionary of any additional optional keyword
arguments to pass to the PSO object constructor.
Defaults to dict().
cost_type (str): Define the type of cost estimator
to use. Options are 'norm_logpdf'=>Compute the cost using
the normal distribution estimator, 'mse'=>Compute the
cost using the negative mean squared error estimator,
'sse'=>Compute the cost using the negative sum of
squared errors estimator. Defaults to 'norm_logpdf'.
Returns:
type: Description of returned object.
"""
if pso_kwargs is None:
pso_kwargs = dict()
# self.ns_version = ns_version
self._pso_kwargs = pso_kwargs
#population_size = pso_population_size
if cost_type == 'mse':
cost = self.mse_cost
elif cost_type == 'sse':
cost = self.sse_cost
elif (cost_type == 'custom') and (custom_cost is not None):
self.set_custom_cost(custom_cost)
cost = self.custom_cost
else:
cost = self.norm_logpdf_cost
# Construct the PSO
if 'save_sampled' not in pso_kwargs.keys():
pso_kwargs['save_sampled'] = False
if 'verbose' not in pso_kwargs.keys():
pso_kwargs['verbose'] = False
pso = PSO(**pso_kwargs)
pso.set_start_position(self._starting_position)
pso.set_cost_function(cost)
pso.set_bounds(lower=self._lower, upper=self._upper)
pso.set_speed(-.25, .25)
return pso
def run_example():
# Runs the cost function to calculate error between model and data
print("Error at start = {}".format(likelihood(starting_position)[0]))
# Displays the model with defaul positions
display(starting_position, save_name='starting_position')
# create PSO object
pso = PSO(save_sampled=False, verbose=True, num_proc=4)
pso.set_cost_function(likelihood)
pso.set_start_position(starting_position)
# allows particles to move +/- 2 orders of magnitude
pso.set_bounds(2)
# sets maximum speed that a particle can travel
pso.set_speed(-.25, .25)
pso.run(num_particles=25, num_iterations=50, stop_threshold=1e-5)
display(pso.best, save_name='best_fit')
np.savetxt("pso_fit_for_model.csv", pso.best)
def run_example():
# Runs the cost function to calculate error between model and data
print("Error at start = {}".format(likelihood(starting_position)[0]))
# Displays the model with defaul positions
display(starting_position, save_name='starting_position')
# create PSO object
pso = PSO(save_sampled=False, verbose=True, num_proc=4)
pso.set_cost_function(likelihood)
pso.set_start_position(starting_position)
# allows particles to move +/- 2 orders of magnitude
pso.set_bounds(2)
# sets maximum speed that a particle can travel
pso.set_speed(-.25, .25)
pso.run(num_particles=25, num_iterations=50, stop_threshold=1e-5)
display(pso.best, save_name='best_fit')
np.savetxt("pso_fit_for_model.csv", pso.best)
def run_pso(run, iterations, bd):
pso = PSO(save_sampled=False,
verbose=True,
shrink_steps=False,
num_proc=14)
pso.set_cost_function(costfunction)
pso.set_start_position(starting_position)
pso.set_bounds(bd)
pso.set_speed(-.1, .1)
pso.run(num_particles=200, num_iterations=iterations, stop_threshold=1e-5)
#print('best pos: ', pso.best.pos)
print('history ', pso.history)
print('run ', run)
print('fit ', pso.best.fitness)
print('all fitness ', pso.values)
np.savetxt("H841_params_" + run + ".txt", 10**pso.history, delimiter=",")
np.savetxt("H841_fit_" + run + ".txt", pso.values, delimiter=",")
def run_example_multiple():
best_pars = np.zeros((100, len(model.parameters)))
counter = 0
for i in range(100):
pso = PSO(save_sampled=False, verbose=False, num_proc=4)
pso.set_cost_function(likelihood)
nominal_random = xnominal + np.random.uniform(-1, 1, len(xnominal))
pso.set_start_position(nominal_random)
pso.set_bounds(2.5)
pso.set_speed(-.25, .25)
pso.run(25, 100)
if pso.best.fitness.values[0] < 0.066:
Y = np.copy(pso.best)
param_values[rates_of_interest_mask] = 10**Y
best_pars[counter] = param_values
counter += 1
print(i, counter)
# display(pso.best)
np.save('jnk3_noASK1_ncalibrated_pars_1h', best_pars)
# USER-Set: must appropriately update cost function!
def cost(position):
Y = np.copy(position)
param_values[calibrate_mask] = 10**Y
sim = solver.run(param_values=param_values).all
logp_data = np.sum(like_data.logpdf(sim['observable']))
if np.isnan(logp_data):
logp_data = np.inf
return -logp_data,
# Setup the particle swarm optimization run
# Set the number of particles in the swarm.
num_particles = 25
# Set the number of iterations for PSO run.
num_iterations = 50
# Construct the optimizer
pso = PSO(save_sampled=False, verbose=True, num_procs=1)
pso.set_cost_function(cost)
starting_position = swarm_param.centers()
pso.set_start_position(starting_position)
pso.set_bounds(lower=swarm_param.lower(), upper=swarm_param.upper())
# sets maximum speed that a particle can travel
pso.set_speed(-.25, .25)
# run it
pso.run(num_particles, num_iterations, stop_threshold=1e-5)
print("Best parameters: ", pso.best)