https://github.com/GPflow/GPflow
Revision deb4508578f7223fa1ad5e3b6458626c4b41ef09 authored by Eric Hammy on 17 October 2019, 14:46:42 UTC, committed by GitHub on 17 October 2019, 14:46:42 UTC
1. Fix hidden bug in SGPR 2. Add the sgpr.compute_qu method from gpflow1 1. [Bug]. SGPR likelihoods were previously using full rank matrices instead of diagonal ones in both upper bound and likelihood calculation. Ie `Kdiag` was not "diag". This error was being masked by the intentional deactivation of tests comparing to the SGPR to the GPR, and what appears to be a hack to make tests working on the upper bound case. 2. [Migration]. Fixing the above broke another test, originally used for sgpr.compute_qu. The method sgpr.compute_qu had not been migrated from gpflow1, and a test that was meant to check it had been patched up to pass, erroneously. After speaking to @markvdw, concluded this method is useful, in particular to compare to SVGP model. The test has been patched up and the method ported to gpflow2.
1 parent 3b2a2ee
Tip revision: deb4508578f7223fa1ad5e3b6458626c4b41ef09 authored by Eric Hammy on 17 October 2019, 14:46:42 UTC
Fix hidden bug in SGPR (#1106)
Fix hidden bug in SGPR (#1106)
Tip revision: deb4508
test_prior.py
import gpflow
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
import pytest
np.random.seed(1)
class Datum:
X = 10 * np.random.randn(5,1)
Y = 10 * np.random.randn(5,1)
lengthscale = 3.3
def test_gpr_objective_equivalence():
"""
In Maximum Likelihood Estimation (MLE), i.e. when there are no priors on
the parameters, the objective should not depend on any transforms on the
parameters.
We use GPR as a simple model that has an objective.
"""
data = (Datum.X, Datum.Y)
l_value = Datum.lengthscale
l_variable = tf.Variable(l_value, dtype=gpflow.default_float(), trainable=True)
m1 = gpflow.models.GPR(data, kernel=gpflow.kernels.SquaredExponential(lengthscale=l_value))
m2 = gpflow.models.GPR(data, kernel=gpflow.kernels.SquaredExponential())
m2.kernel.lengthscale = gpflow.Parameter(l_variable, transform=None)
assert np.allclose(m1.kernel.lengthscale.numpy(),
m2.kernel.lengthscale.numpy()) # consistency check
assert np.allclose(m1.neg_log_marginal_likelihood().numpy(),
m2.neg_log_marginal_likelihood().numpy()), \
"MLE objective should not depend on Parameter transform"
def test_log_prior_with_no_prior():
"""
A parameter without any prior should have zero log-prior,
even if it has a transform to constrain it.
"""
param = gpflow.Parameter(5.3, transform=gpflow.positive())
assert param.log_prior().numpy() == 0.0
def fix_dtype(x):
# TODO replace with generic function for handling tensorflow_probability ...
return tf.cast(x, gpflow.default_float())
class DummyModel(gpflow.models.BayesianModel):
value = 3.3
log_scale = 0.4
def __init__(self, with_transform):
super().__init__()
prior = tfp.distributions.Normal(fix_dtype(1.0), fix_dtype(1.0))
scale = np.exp(self.log_scale)
if with_transform:
transform = tfp.bijectors.AffineScalar(scale=fix_dtype(scale))
else:
transform = None
self.theta = gpflow.Parameter(self.value, prior=prior, transform=transform)
def log_likelihood(self):
return (self.theta + 5) ** 2
def test_map_contains_log_det_jacobian():
m1 = DummyModel(with_transform=True)
m2 = DummyModel(with_transform=False)
assert np.allclose(- m1.neg_log_marginal_likelihood().numpy(),
- m2.neg_log_marginal_likelihood().numpy() + m1.log_scale), \
"MAP objective should differ by log|Jacobian| of the transform"
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