def mediator_triage(mediator_id, base_params_dict):
# Mediator id 1 is the optimal
if mediator_id == 1:
return OptimalMediator(**base_params_dict)
elif 2 <= mediator_id <= 4:
# Poly Mediator Individual. Need to add an additional parameter for the degree of the polynomial.
poly_mediator_params = base_params_dict.copy()
poly_mediator_params['degree'] = mediator_id
return PolyMediatorIndividual(**poly_mediator_params)
elif mediator_id == 5:
# Default Bayes Mediator. Does not take in any additional parameters.
return BayesMediatorSocial(**base_params_dict)
elif 6 <= mediator_id <= 8:
# Polynomial Bayes Mediator, Social.
base_mediator_params = base_params_dict.copy()
base_mediator_params['base_estimator'] = \
GaussianProcessRegressor(
kernel=Exponentiation(Sum(Product(ConstantKernel(), DotProduct()), ConstantKernel(1.0, (0.01, 1000.0))), float(mediator_id) - 4.0),
normalize_y=True,
noise="gaussian",
n_restarts_optimizer=2)
return BayesMediatorSocial(**base_mediator_params)
elif mediator_id == 9:
# GP Mediator Individual with default kernel, which is the same as that used by BayesMediator (Mattern kernel).
return GPMediatorIndividual(**base_params_dict)
elif mediator_id == 10:
# GP Mediator Social with default kernel, which is the same as that used by BayesMediator (Mattern kernel).
return GPMediatorSocial(**base_params_dict)
elif 11 <= mediator_id <= 13:
poly_mediator_params = base_params_dict.copy()
poly_mediator_params['degree'] = mediator_id - 9
return PolyMediatorSocial(**poly_mediator_params)
else:
raise Exception('Unknown mediator with id = ', mediator_id)
# TODO: data structure for bounds are inconsistent among multiple mediators. Should have the same data structure everywhere.
lower_bounds = [
float(scenario.getroot()[0][i].attrib['lowerbound'])
for i in range(0, num_issues)
]
upper_bounds = [
float(scenario.getroot()[0][i].attrib['upperbound'])
for i in range(0, num_issues)
]
# Run the mediator
num_init_random_points = 1
num_random_restarts = 5
base_estimator = GaussianProcessRegressor(kernel=Exponentiation(
Sum(Product(ConstantKernel(), DotProduct()),
ConstantKernel(1.0, (0.01, 1000.0))), 2.0),
normalize_y=True,
noise="gaussian",
n_restarts_optimizer=2)
base_estimator = None
bayes_mediator_social = BayesMediatorSocial(
num_issues=num_issues,
num_agents=2,
u_funcs=u_funcs,
lower_bounds=lower_bounds,
upper_bounds=upper_bounds,
num_init_random_points=num_init_random_points,
num_random_restarts=num_random_restarts,
base_estimator=base_estimator,
plot_mediator=num_issues == 1,
for length_scale in [np.arange(1, 6), [0.2, 0.3, 0.5, 0.6, 0.1]]:
KERNELS.extend([
RBF(length_scale=length_scale),
Matern(length_scale=length_scale, nu=0.5),
Matern(length_scale=length_scale, nu=1.5),
Matern(length_scale=length_scale, nu=2.5),
RationalQuadratic(alpha=2.0, length_scale=2.0),
ExpSineSquared(length_scale=2.0, periodicity=3.0),
ConstantKernel(constant_value=1.0),
WhiteKernel(noise_level=2.0),
Matern(length_scale=length_scale, nu=2.5)**3.0,
RBF(length_scale=length_scale) +
Matern(length_scale=length_scale, nu=1.5),
RBF(length_scale=length_scale) *
Matern(length_scale=length_scale, nu=1.5),
DotProduct(sigma_0=2.0)
])
# Copied (shamelessly) from sklearn.gaussian_process.kernels
def _approx_fprime(xk, f, epsilon, args=()):
f0 = f(*((xk, ) + args))
grad = np.zeros((f0.shape[0], f0.shape[1], len(xk)), float)
ei = np.zeros((len(xk), ), float)
for k in range(len(xk)):
ei[k] = 1.0
d = epsilon * ei
grad[:, :, k] = (f(*((xk + d, ) + args)) - f0) / d[k]
ei[k] = 0.0
return grad