def test_sample():
graph = Graph()
x = array([1, 2, 3])[:, None]
# Test that it produces random samples. Not sure how to test for
# correctness.
f1, e1 = GP(EQ(), graph=graph), GP(1e-1 * Delta(), graph=graph)
f2, e2 = GP(EQ(), graph=graph), GP(1e-1 * Delta(), graph=graph)
gpar = GPAR().add_layer(lambda: (f1, e1)).add_layer(lambda: (f2, e2))
yield ge, B.sum(B.abs(gpar.sample(x) - gpar.sample(x))), 1e-3
yield ge, \
B.sum(B.abs(gpar.sample(x, latent=True) -
gpar.sample(x, latent=True))), \
1e-3
# Test that posterior latent samples are around the data that is
# conditioned on.
graph = Graph()
f1, e1 = GP(EQ(), graph=graph), GP(1e-8 * Delta(), graph=graph)
f2, e2 = GP(EQ(), graph=graph), GP(1e-8 * Delta(), graph=graph)
gpar = GPAR().add_layer(lambda: (f1, e1)).add_layer(lambda: (f2, e2))
y = gpar.sample(x, latent=True)
gpar = gpar | (x, y)
yield approx, gpar.sample(x), y, 3
yield approx, gpar.sample(x, latent=True), y, 3
def test_logpdf():
graph = Graph()
f1, e1 = GP(EQ(), graph=graph), GP(2e-1 * Delta(), graph=graph)
f2, e2 = GP(Linear(), graph=graph), GP(1e-1 * Delta(), graph=graph)
gpar = GPAR().add_layer(lambda: (f1, e1)).add_layer(lambda: (f2, e2))
# Sample some data from GPAR.
x = B.linspace(0, 2, 10, dtype=torch.float64)[:, None]
y = gpar.sample(x, latent=True)
# Compute logpdf.
logpdf1 = (f1 + e1)(x).logpdf(y[:, 0])
logpdf2 = (f2 + e2)(B.concat([x, y[:, 0:1]], axis=1)).logpdf(y[:, 1])
# Test computation of GPAR.
yield eq, gpar.logpdf(x, y), logpdf1 + logpdf2
yield eq, gpar.logpdf(x, y, only_last_layer=True), logpdf2
# Test resuming computation.
x_int, x_ind_int = gpar.logpdf(x, y, return_inputs=True, outputs=[0])
yield eq, gpar.logpdf(x_int, y, x_ind=x_ind_int, outputs=[1]), logpdf2
# Test that sampling missing gives a stochastic estimate.
y[1, 0] = np.nan
yield ge, \
B.abs(gpar.logpdf(x, y, sample_missing=True) -
gpar.logpdf(x, y, sample_missing=True)).numpy(), \
1e-3
from varz import Vars
from varz.torch import minimise_l_bfgs_b
from wbml.out import Counter
from .model import GPAR, per_output
__all__ = ['GPARRegressor', 'log_transform', 'squishing_transform']
log = logging.getLogger(__name__)
_dispatch = Dispatcher()
#: Log transform for the data.
log_transform = (B.log, B.exp)
#: Squishing transform for the data.
squishing_transform = (lambda x: B.sign(x) * B.log(1 + B.abs(x)),
lambda x: B.sign(x) * (B.exp(B.abs(x)) - 1))
def _vector_from_init(init, length):
# If only a single value is given, create ones.
if np.size(init) == 1:
return init * np.ones(length)
# Multiple values are given. Check that enough values are available.
init_squeezed = np.squeeze(init)
if np.ndim(init_squeezed) != 1:
raise ValueError('Incorrect shape {} of hyperparameters.'
''.format(np.shape(init)))
if np.size(init_squeezed) < length: # Squeezed doesn't change size.
raise ValueError('Not enough hyperparameters specified.')
from wbml.out import Counter
from .model import GPAR, per_output
__all__ = ["GPARRegressor", "log_transform", "squishing_transform"]
log = logging.getLogger(__name__)
_dispatch = Dispatcher()
#: Log transform for the data.
log_transform = (B.log, B.exp)
#: Squishing transform for the data.
squishing_transform = (
lambda x: B.sign(x) * B.log(B.add(1, B.abs(x))),
lambda x: B.sign(x) * B.subtract(B.exp(B.abs(x)), 1),
)
def _vector_from_init(init, length):
# If only a single value is given, create ones.
if np.size(init) == 1:
return init * np.ones(length)
# Multiple values are given. Check that enough values are available.
init_squeezed = np.squeeze(init)
if np.ndim(init_squeezed) != 1:
raise ValueError("Incorrect shape {} of hyperparameters."
"".format(np.shape(init)))
if np.size(init_squeezed) < length: # Squeezed doesn't change size.
