class KernelExpLiteGaussianLowRankAdaptive(KernelExpLiteGaussianLowRank):
def __init__(self,
sigma,
lmbda,
D,
N,
eta=0.1,
cg_tol=1e-3,
cg_maxiter=None,
num_initial_evaluations=3,
num_evaluations=3,
minimum_size_learning=100,
num_initial_evaluations_relearn=1,
num_evaluations_relearn=1,
param_bounds={'sigma': [-3, 3]}):
KernelExpLiteGaussianLowRank.__init__(self, sigma, lmbda, D, N, eta,
cg_tol, cg_maxiter)
self.bo = None
self.param_bounds = param_bounds
self.num_initial_evaluations = num_initial_evaluations
self.num_iter = num_evaluations
self.minimum_size_learning = minimum_size_learning
self.n_initial_relearn = num_initial_evaluations_relearn
self.n_iter_relearn = num_evaluations_relearn
self.learning_parameters = False
def fit(self, X):
# avoid infinite recursion from x-validation fit call
if not self.learning_parameters and len(
X) >= self.minimum_size_learning:
self.learning_parameters = True
if self.bo is None:
logger.info("Bayesian optimisation from scratch.")
self.bo = BayesOptSearch(
self,
X,
self.param_bounds,
n_initial=self.num_initial_evaluations)
best_params = self.bo.optimize(self.num_iter)
else:
logger.info("Bayesian optimisation using prior model.")
self.bo.re_initialise(X, self.n_initial_relearn)
best_params = self.bo.optimize(self.n_iter_relearn)
self.set_parameters_from_dict(best_params)
self.learning_parameters = False
logger.info("Learnt %s" % str(self.get_parameters()))
# standard fit call from superclass
KernelExpLiteGaussianLowRank.fit(self, X)
class KernelExpLiteGaussianLowRankAdaptive(KernelExpLiteGaussianLowRank):
def __init__(
self,
sigma,
lmbda,
D,
N,
eta=0.1,
cg_tol=1e-3,
cg_maxiter=None,
num_initial_evaluations=3,
num_evaluations=3,
minimum_size_learning=100,
num_initial_evaluations_relearn=1,
num_evaluations_relearn=1,
param_bounds={"sigma": [-3, 3]},
):
KernelExpLiteGaussianLowRank.__init__(self, sigma, lmbda, D, N, eta, cg_tol, cg_maxiter)
self.bo = None
self.param_bounds = param_bounds
self.num_initial_evaluations = num_initial_evaluations
self.num_iter = num_evaluations
self.minimum_size_learning = minimum_size_learning
self.n_initial_relearn = num_initial_evaluations_relearn
self.n_iter_relearn = num_evaluations_relearn
self.learning_parameters = False
def fit(self, X):
# avoid infinite recursion from x-validation fit call
if not self.learning_parameters and len(X) >= self.minimum_size_learning:
self.learning_parameters = True
if self.bo is None:
logger.info("Bayesian optimisation from scratch.")
self.bo = BayesOptSearch(self, X, self.param_bounds, n_initial=self.num_initial_evaluations)
best_params = self.bo.optimize(self.num_iter)
else:
logger.info("Bayesian optimisation using prior model.")
self.bo.re_initialise(X, self.n_initial_relearn)
best_params = self.bo.optimize(self.n_iter_relearn)
self.set_parameters_from_dict(best_params)
self.learning_parameters = False
logger.info("Learnt %s" % str(self.get_parameters()))
# standard fit call from superclass
KernelExpLiteGaussianLowRank.fit(self, X)
est.set_parameters_from_dict(best_params)
est.fit(X)
visualise_fit_2d(est, X)
plt.suptitle("Original fit %s\nOptimised over: %s" %
(str(est.get_parameters()), str(param_bounds)))
if len(param_bounds) == 1:
plt.figure()
plot_bayesopt_model_1d(bo)
plt.title("Objective")
# now change data, with different length scale
X = np.random.randn(200, D) * .1
# reset optimiser, which but initialise from old model, sample 3 random point to update
best_params = bo.re_initialise(new_data=X, num_initial_evaluations=3)
# this optimisation now runs on the "new" objective
best_params = bo.optimize(num_iter=3)
est.set_parameters_from_dict(best_params)
est.fit(X)
visualise_fit_2d(est, X)
plt.suptitle("New fit %s\nOptimised over: %s" %
(str(est.get_parameters()), str(param_bounds)))
if len(param_bounds) == 1:
plt.figure()
plot_bayesopt_model_1d(bo)
plt.title("New objective")
est.set_parameters_from_dict(best_params)
est.fit(X)
visualise_fit_2d(est, X)
plt.suptitle("Original fit %s\nOptimised over: %s" %
(str(est.get_parameters()), str(param_bounds)))
if len(param_bounds) == 1:
plt.figure()
plot_bayesopt_model_1d(bo)
plt.title("Objective")
# now change data, with different length scale
X = np.random.randn(200, D) * .1
# reset optimiser, which but initialise from old model, sample 3 random point to update
best_params = bo.re_initialise(new_data=X, num_initial_evaluations=3)
# this optimisation now runs on the "new" objective
best_params = bo.optimize(num_iter=3)
est.set_parameters_from_dict(best_params)
est.fit(X)
visualise_fit_2d(est, X)
plt.suptitle("New fit %s\nOptimised over: %s" %
(str(est.get_parameters()), str(param_bounds)))
if len(param_bounds) == 1:
plt.figure()
plot_bayesopt_model_1d(bo)
plt.title("New objective")
