param_bounds,
objective_log=False,
objective_log_bound=100,
num_initial_evaluations=5)
# optimisation starts here, use results and apply to model
best_params = bo.optimize(num_iter=5)
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" %
est = KernelExpLiteGaussianAdaptive(sigma=1., lmbda=.001, D=D, N=N,
# these paramters are all optional, to controll Bayesian opt.
num_initial_evaluations=3, num_evaluations=3, minimum_size_learning=100,
# these depend on how much data changes between the "fit" calls
num_initial_evaluations_relearn=3, num_evaluations_relearn=3,
# this can be used to adjust search spaces or include more parameters
# by default, only sigma is optimised
param_bounds={'sigma': [-3, 3]}
)
# automatically sets parameters
est.fit(X)
# only for illustration purpose
plt.figure()
plot_bayesopt_model_1d(est.bo)
plt.title("Original fit")
visualise_fit_2d(est, X)
plt.suptitle("Original fit")
# now change data, with different length scale
X = np.random.randn(N, D) * .1
# re-learns parameters, but starts from previous ones
est.fit(X)
visualise_fit_2d(est, X)
plt.suptitle("New fit")
# only for illustration purpose
num_initial_evaluations=3,
num_evaluations=3,
minimum_size_learning=100,
# these depend on how much data changes between the "fit" calls
num_initial_evaluations_relearn=3,
num_evaluations_relearn=3,
# this can be used to adjust search spaces or include more parameters
# by default, only sigma is optimised
param_bounds={'sigma': [-3, 3]})
# automatically sets parameters
est.fit(X)
# only for illustration purpose
plt.figure()
plot_bayesopt_model_1d(est.bo)
plt.title("Original fit")
visualise_fit_2d(est, X)
plt.suptitle("Original fit")
# now change data, with different length scale
X = np.random.randn(N, D) * .1
# re-learns parameters, but starts from previous ones
est.fit(X)
visualise_fit_2d(est, X)
plt.suptitle("New fit")
# only for illustration purpose
# oop interface for optimising and using results
# objective is not put through log here, if it is, might want to bound away from zero
bo = BayesOptSearch(est, X, param_bounds, objective_log=False, objective_log_bound=100,
num_initial_evaluations=5)
# optimisation starts here, use results and apply to model
best_params = bo.optimize(num_iter=5)
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" %
