swh:1:snp:e65ec12a3dc489100df2dfc1b0eab4915109f830
Tip revision: bd04738eff4c8fa2c05ec14b69bffe32357d02f4 authored by vdutor on 25 June 2018, 14:04:02 UTC
Fix the global step for summary tasks
Fix the global step for summary tasks
Tip revision: bd04738
test_autoflow.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.
import tensorflow as tf
import numpy as np
from numpy.testing import assert_almost_equal, assert_allclose
import gpflow
from gpflow.test_util import GPflowTestCase
from gpflow.core import AutoFlow
class DumbModel(gpflow.models.Model):
def __init__(self):
gpflow.models.Model.__init__(self)
self.a = gpflow.Param(3.)
@gpflow.params_as_tensors
def _build_likelihood(self):
return -tf.square(self.a)
class NoArgsModel(DumbModel):
@gpflow.autoflow()
@gpflow.params_as_tensors
def function1(self):
return self.a
@gpflow.autoflow()
@gpflow.params_as_tensors
def function2(self):
return self.a + 1.0
class TestNoArgs(GPflowTestCase):
def test_autoflow_functioning(self):
with self.test_context():
m = NoArgsModel()
m.compile()
def get_keys():
return [k for k in m.__dict__ if k.startswith(AutoFlow.__autoflow_prefix__)]
names = [m.function1.__name__, m.function2.__name__]
names = [AutoFlow.__autoflow_prefix__ + name for name in names]
first_key = names[0]
second_key = names[1]
assert_allclose(m.function1(), 3.)
assert_allclose(m.function2(), 4.)
self.assertEqual(len(get_keys()), 2)
AutoFlow.clear_autoflow(m, name=first_key)
self.assertEqual(len(get_keys()), 1)
assert_allclose(m.function1(), 3.)
self.assertEqual(len(get_keys()), 2)
AutoFlow.clear_autoflow(m, name=second_key)
self.assertEqual(len(get_keys()), 1)
assert_allclose(m.function2(), 4.)
self.assertEqual(len(get_keys()), 2)
AutoFlow.clear_autoflow(m, name=first_key)
AutoFlow.clear_autoflow(m, name=second_key)
self.assertEqual(len(get_keys()), 0)
assert_allclose(m.function1(), 3.)
assert_allclose(m.function2(), 4.)
self.assertEqual(len(get_keys()), 2)
AutoFlow.clear_autoflow(m)
self.assertEqual(len(get_keys()), 0)
class AddModel(DumbModel):
@gpflow.autoflow((tf.float64,), (tf.float64,))
@gpflow.params_as_tensors
def add(self, x, y):
return tf.add(x, y)
class TestShareArgs(GPflowTestCase):
"""
This is designed to replicate bug #85, where having two models caused
autoflow functions to fail because the tf_args were shared over the
instances.
"""
def setUp(self):
with self.test_context():
self.m1 = AddModel()
self.m2 = AddModel()
def test_share_args(self):
with self.test_context():
rng = np.random.RandomState(0)
x = rng.randn(10, 20)
y = rng.randn(10, 20)
ans = x + y
self.m1.add(x, y)
self.m2.add(x, y)
assert_almost_equal(self.m1.add(x, y), ans)
assert_almost_equal(self.m2.add(x, y), ans)
assert_almost_equal(self.m1.add(y, x), ans)
class IncrementModel(DumbModel):
def __init__(self):
DumbModel.__init__(self)
self.b = gpflow.DataHolder(np.array([3.]))
@gpflow.autoflow((tf.float64,))
@gpflow.params_as_tensors
def inc(self, x):
return x + self.b
class TestDataHolder(GPflowTestCase):
def test_add(self):
with self.test_context():
m = IncrementModel()
x = np.random.randn(10, 20)
m.compile()
assert_almost_equal(x + m.a.read_value(), m.inc(x))
class TestGPmodel(GPflowTestCase):
def prepare(self):
rng = np.random.RandomState(0)
X, Y = rng.randn(2, 10, 1)
m = gpflow.models.SVGP(X, Y,
kern=gpflow.kernels.Matern32(1),
likelihood=gpflow.likelihoods.StudentT(),
Z=X[::2].copy())
m.compile()
xnew = np.random.randn(100, 1)
ynew = np.random.randn(100, 1)
return m, xnew, ynew
def test_predict_f(self):
with self.test_context():
m, x, _y = self.prepare()
_mu, _var = m.predict_f(x)
def test_predict_y(self):
with self.test_context():
m, x, _y = self.prepare()
_mu, _var = m.predict_y(x)
def test_predict_density(self):
with self.test_context():
m, x, y = self.prepare()
m.predict_density(x, y)
def test_multiple_AFs(self):
with self.test_context():
m, _x, _y = self.prepare()
m.compute_log_likelihood()
m.compute_log_prior()
m.compute_log_likelihood()
class TestFixAndPredict(GPflowTestCase):
"""
Bug #54 says that if a model parameter is fixed between calls to predict
(an autoflow fn) then the second call fails. This test ensures replicates
that and ensures that the bugfix remains in furure.
"""
def prepare(self):
rng = np.random.RandomState(0)
X, Y = rng.randn(2, 10, 1)
m = gpflow.models.SVGP(X, Y, kern=gpflow.kernels.Matern32(1),
likelihood=gpflow.likelihoods.StudentT(),
Z=X[::2].copy())
xtest = np.random.randn(100, 1)
ytest = np.random.randn(100, 1)
return m, xtest, ytest
def test(self):
with self.test_context():
m, x, y = self.prepare()
m.compile()
m.kern.variance.trainable = False
_, _ = m.predict_f(m.X.read_value())
if __name__ == '__main__':
tf.test.main()