def test_grad_log_likelihood_pings(self):
"""Ping test (compare analytic result to finite difference) the log likelihood gradient wrt hyperparameters."""
numpy.random.seed(2014)
h = 2.0e-4
tolerance = 5.0e-6
for num_sampled in self.num_sampled_list:
self.gp_test_environment_input.num_sampled = num_sampled
_, gaussian_process = self._build_gaussian_process_test_data(self.gp_test_environment_input)
python_cov, historical_data = gaussian_process.get_core_data_copy()
lml = GaussianProcessLogMarginalLikelihood(python_cov, historical_data)
analytic_grad = lml.compute_grad_log_likelihood()
for k in xrange(lml.num_hyperparameters):
hyperparameters_old = lml.hyperparameters
# hyperparamter + h
hyperparameters_p = numpy.copy(hyperparameters_old)
hyperparameters_p[k] += h
lml.hyperparameters = hyperparameters_p
cov_p = lml.compute_log_likelihood()
lml.hyperparameters = hyperparameters_old
# hyperparamter - h
hyperparameters_m = numpy.copy(hyperparameters_old)
hyperparameters_m[k] -= h
lml.hyperparameters = hyperparameters_m
cov_m = lml.compute_log_likelihood()
lml.hyperparameters = hyperparameters_old
# calculate finite diff
fd_grad = (cov_p - cov_m) / (2.0 * h)
self.assert_scalar_within_relative(fd_grad, analytic_grad[k], tolerance)
def test_evaluate_log_likelihood_at_points(self):
"""Check that ``evaluate_log_likelihood_at_hyperparameter_list`` computes and orders results correctly."""
num_sampled = 5
self.gp_test_environment_input.num_sampled = num_sampled
_, gaussian_process = self._build_gaussian_process_test_data(self.gp_test_environment_input)
python_cov, historical_data = gaussian_process.get_core_data_copy()
lml = GaussianProcessLogMarginalLikelihood(python_cov, historical_data)
num_to_eval = 10
domain_bounds = [self.gp_test_environment_input.hyperparameter_interval] * self.gp_test_environment_input.num_hyperparameters
domain = TensorProductDomain(domain_bounds)
hyperparameters_to_evaluate = domain.generate_uniform_random_points_in_domain(num_to_eval)
test_values = evaluate_log_likelihood_at_hyperparameter_list(lml, hyperparameters_to_evaluate)
for i, value in enumerate(test_values):
lml.hyperparameters = hyperparameters_to_evaluate[i, ...]
truth = lml.compute_log_likelihood()
assert value == truth
import matplotlib.pyplot as plt
from moe.optimal_learning.python.python_version.log_likelihood import GaussianProcessLogMarginalLikelihood
print "prior mean\n{0}\nprior sig diag\n{1}".format(prior_mean,
numpy.diag(prior_sig))
print "num_is {0}".format(obj_func_max.getNumIS() - 1 + separateIS0)
hyperparam_search_domain = pythonTensorProductDomain(
[ClosedInterval(bound[0], bound[1]) for bound in hyper_bounds])
print "hyper bounds\n{0}".format(hyper_bounds)
cov = MixedSquareExponential(hyperparameters=prior_mean,
total_dim=obj_func_max.getDim() + 1,
num_is=obj_func_max.getNumIS() - 1 + separateIS0)
gp_likelihood = GaussianProcessLogMarginalLikelihood(cov, historical_data)
with PdfPages('posterior_plot_1.pdf') as pdf:
for d in range(len(prior_mean)):
x_list = numpy.linspace(hyper_bounds[d][0], hyper_bounds[d][1], 100)
y_list = numpy.zeros(len(x_list))
x = numpy.copy(prior_mean)
for i, e in enumerate(x_list):
print "plot {0}, {1}th pt".format(d, i)
x[d] = e
gp_likelihood.set_hyperparameters(x)
y_list[i] = gp_likelihood.compute_log_likelihood(
) + prior.compute_log_likelihood(x)
plt.figure()
plt.plot(x_list, y_list, 'r-o')
plt.title(str(d))
pdf.savefig()
plt.close()
