Revision 6c35a7a8544dfecfe92ef9b559b90fcb40b364eb authored by Mark van der Wilk on 20 November 2017, 17:43:17 UTC, committed by GitHub on 20 November 2017, 17:43:17 UTC
* Add Features base classes * Convert SGPR and SVGP models to use InducingFeatures (including backwards compatibility) * Fix tests * Added Multiscale feature. To show the generality of the inter-domain code. * Fixed py2 metaclass issue, as per John Bradshaw's suggestion. * Improve docstrings, register Multiscale feature * Change SGPR models to determine feature length dynamically [but feature.__len__() still needs to be made dynamic as well!] * Bits and pieces missed in the merge. * Add features to __init__. * Fixed incorrect parameter dtype assignment on compile. * Two bugfixs. - Static assignment of len(feature) - Upper bound mixin referred to Z. * Fixed bugs in multiscale & added features. * Added tests for `Multiscale` inducing features. * Updated `RELEASE.md`, and small changes for tests. * add test for len(feature) * Deprecation property for `Z`, relative imports, improved test. * `SGPMC` has inducing features now + better docstrings. * Fixed `SGPMC`. * Testing now uses tf1.4. * `feat` now `feature` + other changes. * Update _version.py * change exception
1 parent 0741f86
test_profiling.py
# Copyright 2017 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.from __future__ import print_function
import glob
import os
import numpy as np
import tensorflow as tf
import gpflow
from gpflow.test_util import GPflowTestCase
class TestProfiling(GPflowTestCase):
def prepare(self):
with gpflow.defer_build():
X = np.random.rand(100, 1)
Y = np.sin(X) + np.random.randn(*X.shape) * 0.01
k = gpflow.kernels.RBF(1)
return gpflow.models.GPR(X, Y, k)
def test_profile(self):
m = self.prepare()
s = gpflow.settings.get_settings()
s.profiling.dump_timeline = True
s.profiling.output_directory = tf.test.get_temp_dir()
s.profiling.output_file_name = 'test_trace_profile'
with gpflow.settings.temp_settings(s):
with gpflow.session_manager.get_session().as_default():
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m, maxiter=10)
expected_file = os.path.join(s.profiling.output_directory,
s.profiling.output_file_name + '.json')
self.assertTrue(os.path.exists(expected_file))
os.remove(expected_file)
def test_autoflow(self):
m = self.prepare()
s = gpflow.settings.get_settings()
s.profiling.dump_timeline = True
s.profiling.output_directory = tf.test.get_temp_dir()
s.profiling.output_file_name = 'test_trace_autoflow'
with gpflow.settings.temp_settings(s):
with gpflow.session_manager.get_session().as_default():
m.compile()
m.kern.compute_K_symm(m.X.read_value())
directory = s.profiling.output_directory
filename = s.profiling.output_file_name + '.json'
expected_file = os.path.join(directory, filename)
print(expected_file)
self.assertTrue(os.path.exists(expected_file))
os.remove(expected_file)
m.clear()
s.profiling.output_directory = tf.test.get_temp_dir()
m.compile()
# TODO(@awav): CHECK IT
# with self.assertRaises(IOError):
# with gpflow.settings.temp_settings(s):
# m.kern.compute_K_symm(m.X.read_value())
def test_eachtime(self):
m = self.prepare()
s = gpflow.settings.get_settings()
s.profiling.dump_timeline = True
s.profiling.each_time = True
s.profiling.output_directory = tf.test.get_temp_dir() + '/each_time/'
name = 'test_eachtime'
s.profiling.output_file_name = name
with gpflow.settings.temp_settings(s):
with gpflow.session_manager.get_session():
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m, maxiter=2)
pattern = s.profiling.output_directory + '/{name}*.json'.format(name=name)
for filename in glob.glob(pattern):
os.remove(filename)
if os.path.exists(s.profiling.output_directory):
os.rmdir(s.profiling.output_directory)
if __name__ == "__main__":
tf.test.main()
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