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_scaled_euclid_dist.py
# Copyright 2018 the GPflow authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pytest
import tensorflow as tf
import gpflow.kernels as kernels
rng = np.random.RandomState(0)
class Datum:
num_data = 100
D = 100
X = rng.rand(num_data, D) * 100
kernel_list = [
kernels.Matern12(),
kernels.Matern32(),
kernels.Matern52(),
kernels.Exponential(),
kernels.Cosine()
]
@pytest.mark.parametrize('kernel', kernel_list)
def test_kernel_euclidean_distance(kernel):
'''
Tests output & gradients of kernels that are a function of the (scaled) euclidean distance
of the points. We test on a high dimensional space, which can generate very small distances
causing the scaled_square_dist to generate some negative values.
'''
K = kernel(Datum.X)
assert not np.isnan(
K).any(), 'NaNs in the output of the ' + kernel.__name__ + 'kernel.'
assert np.isfinite(
K).all(), 'Infs in the output of the ' + kernel.__name__ + ' kernel.'
X_as_param = tf.Variable(Datum.X)
with tf.GradientTape() as tape:
K_value = kernel(X_as_param, X_as_param)
dK = tape.gradient(K_value, X_as_param)[0]
assert not np.isnan(dK).any(
), 'NaNs in the gradient of the ' + kernel.__name__ + ' kernel.'
assert np.isfinite(
dK).all(), 'Infs in the output of the ' + kernel.__name__ + ' kernel.'
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