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_training.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 gpflow
from gpflow.utilities.utilities import leaf_components
from gpflow.utilities import multiple_assign
rng = np.random.RandomState(0)
class Data:
H0 = 5
H1 = 2
M = 10
D = 1
Z = rng.rand(M, 1)
ls = 2.0
ls_new = 1.5
var = 1.0
var_new = 10.
# ------------------------------------------
# Fixtures
# ------------------------------------------
@pytest.fixture
def model():
return gpflow.models.SVGP(
kernel=gpflow.kernels.SquaredExponential(lengthscale=Data.ls, variance=Data.var),
likelihood=gpflow.likelihoods.Gaussian(),
inducing_variable=Data.Z,
q_diag=True
)
# ------------------------------------------
# Reference
# ------------------------------------------
model_param_updates = {
'SVGP.kernel.lengthscale': Data.ls_new,
'SVGP.likelihood.variance': Data.var_new,
'SVGP.inducing_variable.Z': np.zeros_like(Data.Z),
'SVGP.q_sqrt': 0.5 * np.ones((Data.M, 1))
}
model_wrong_path = [
{'kernel.lengthscale': Data.ls_new},
{'SVGP.Gaussian.variance': Data.var_new},
{'inducing_variable.Z': np.zeros_like(Data.Z)},
{'SVGP.q_std': 0.5 * np.ones((Data.M, 1))}
]
model_wrong_value = [
{'SVGP.likelihood.variance': np.ones((2, 1), dtype=np.int32)},
{'SVGP.inducing_variable.Z': [1, 2, 3]}
]
@pytest.mark.parametrize('var_update_dict', [model_param_updates])
def test_multiple_assign_updates_correct_values(model, var_update_dict):
old_value_dict = leaf_components(model).copy()
multiple_assign(model, var_update_dict)
for path, variable in leaf_components(model).items():
if path in var_update_dict.keys():
np.testing.assert_almost_equal(variable.value().numpy(), var_update_dict[path],
decimal=7)
else:
np.testing.assert_equal(variable.value().numpy(), old_value_dict[path].value().numpy())
@pytest.mark.parametrize('wrong_var_update_dict', model_wrong_path)
def test_multiple_assign_fails_with_invalid_path(model, wrong_var_update_dict):
with pytest.raises(KeyError):
multiple_assign(model, wrong_var_update_dict)
@pytest.mark.parametrize('wrong_var_update_dict', model_wrong_value)
def test_multiple_assign_fails_with_invalid_values(model, wrong_var_update_dict):
with pytest.raises(ValueError):
multiple_assign(model, wrong_var_update_dict)
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