Revision 1db48f3a735eb0fba06a7d503f080a7ead512604 authored by Artem Artemev on 11 July 2018, 12:50:44 UTC, committed by GitHub on 11 July 2018, 12:50:44 UTC
1 parent 707b195
test_param.py
# Copyright 2016 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.
# pylint: disable=E1123
import gpflow
import numpy as np
import pandas as pd
import pytest
import tensorflow as tf
from gpflow import GPflowError, settings
from gpflow.test_util import GPflowTestCase, session_tf
from numpy.testing import assert_allclose
class Foo(gpflow.models.Model):
def _build_likelihood(self):
return tf.zeros([1], dtype=gpflow.settings.float_type)
class TestNaming(GPflowTestCase):
def test_index(self):
index = gpflow.core.parentable.Parentable._read_index() + 1
with self.test_context():
def increment_assert(i):
p = gpflow.Param(1)
assert p.index.split("-")[-1] == i
for i in range(index, index + 5):
increment_assert(str(i))
def test_standard_name(self):
with self.test_context():
p = gpflow.Param(1)
assert p.name.startswith('Parameter')
assert p.name == p.pathname
m = gpflow.params.Parameterized()
assert m.name.startswith('Parameterized')
assert m.name == m.pathname
def test_pathname(self):
with self.test_context():
a = gpflow.Param(1)
b = gpflow.Param(1, name='test_name')
a_pathname = a.pathname
b_pathname = b.pathname
assert a.name != b.name
assert a_pathname != b_pathname
assert a_pathname == a.full_name
assert b_pathname == b.full_name
m = gpflow.params.Parameterized()
m.a = a
m.b = b
assert m.a.name != m.b.name
assert m.a.pathname != a_pathname
assert m.b.pathname != b_pathname
assert m.a.full_name != a_pathname
assert m.b.full_name != b_pathname
assert m.a.pathname.split("/")[0] == m.name
assert m.b.pathname.split("/")[0] == m.name
class TestType(GPflowTestCase):
def setUp(self):
int_type = np.int16
float_type = np.float16
test_data = [(1, int_type),
(1.0, float_type),
([1], float_type),
([1.0], float_type),
(np.array([1, 1], dtype=np.float32), np.float32),
(np.array([1, 1], dtype=np.int32), np.int32)]
self.int_type = int_type
self.float_type = float_type
self.test_data = test_data
def test_specific_dtype(self):
test_data = self.test_data + [
(1, np.float32),
(1.0, np.float64),
([1.0], np.float32),
(np.array([1, 2, 3], dtype=np.float64), np.float16)
]
with self.test_context():
for v, vtype in test_data:
p = gpflow.Param(v, dtype=vtype, autobuild=False)
self.assertEqual(p.dtype, vtype)
p.compile()
self.assertEqual(p.dtype, vtype)
def test_default_type(self):
s = gpflow.settings.get_settings()
s.dtypes.int_type = self.int_type
s.dtypes.float_type = self.float_type
with gpflow.settings.temp_settings(s), self.test_context():
for v, vtype in self.test_data:
p = gpflow.Param(v)
self.assertEqual(p.dtype, vtype)
def test_assign_fail_types(self):
with self.test_context():
param = gpflow.Param(np.array([1]), dtype=np.int32, autobuild=False)
def fail_assigns(p):
with self.assertRaises(ValueError):
p.assign([2], dtype=np.float32)
with self.assertRaises(ValueError):
p.assign(np.array([2], dtype=np.float32))
with self.assertRaises(ValueError):
p.assign(np.array([2]), dtype=np.float32)
with self.assertRaises(ValueError):
p.assign([2], dtype=np.int64)
fail_assigns(param)
param.compile()
fail_assigns(param)
class TestParameter(GPflowTestCase):
def setUp(self):
with self.test_context():
self.p = gpflow.Param(1.0)
self.m = gpflow.params.Parameterized()
self.m.p = gpflow.Param(1.0)
self.m.b = gpflow.Param(1.0)
def test_parameter_different_options(self):
with self.test_context() as session:
val = 10.
a = gpflow.Param(val)
assert_allclose(a.read_value(), val)
self.assertEqual(a.size, 1)
size = 2
val = [10.] * size
b = gpflow.Param([10.] * size, fix_shape=False)
assert_allclose(b.read_value(), val)
self.assertEqual(b.dtype, np.float64)
self.assertEqual(b.size, size)
size = 3
val = [10] * size
c = gpflow.Param(val, dtype=np.float16)
assert_allclose(c.read_value(), val)
self.assertEqual(c.dtype, np.float16)
self.assertEqual(c.size, size)
size = 4
val = [10.] * size
d = gpflow.Param(val, trainable=False)
assert_allclose(d.read_value(), val)
self.assertEqual(d.trainable, False)
self.assertEqual(d.size, size)
size = 5
val = [10.] * size
transform = gpflow.transforms.Log1pe()
e = gpflow.Param(val, transform=transform)
assert_allclose(e.read_value(), val)
self.assertEqual(e.size, size)
unconstrained = transform.backward(np.array(val))
assert_allclose(session.run(e.unconstrained_tensor), unconstrained)
size = 6
val = [10.] * size
f = gpflow.Param(val, prior=gpflow.priors.Gaussian(1, 2))
assert_allclose(f.read_value(), val)
assert_allclose(f.read_value(session), val)
self.assertEqual(f.size, size)
self.assertTrue(isinstance(f.prior, gpflow.priors.Gaussian))
def test_initialized(self):
with self.test_context() as session1:
p = gpflow.Param(1.0)
self.assertTrue(p.is_initialized(session1))
with self.test_context() as session2:
self.assertFalse(p.is_initialized(session2))
with self.test_context() as session3:
p = gpflow.Param(1.0, autobuild=False)
self.assertFalse(p.is_initialized(session1))
self.assertFalse(p.is_initialized(session2))
self.assertFalse(p.is_initialized(session3))
p.compile()
self.assertFalse(p.is_initialized(session1))
self.assertFalse(p.is_initialized(session2))
self.assertTrue(p.is_initialized(session3))
def assert_exception(args, fun, exception):
for arg in args:
with self.assertRaises(exception, msg="Raise at '{}'".format(arg)):
fun(arg)
with self.test_context():
assert_exception(['', 'non-tempty', 1.0, None, object()],
p.is_initialized, ValueError)
def test_fail_scenarios(self):
with self.test_context() as session:
p = gpflow.Param(1.0)
values = ['', 'test', 1., object(), None]
for v in values:
def value_error(value):
return self.assertRaises(ValueError, msg='Raised at "{}"'.format(value))
with value_error(v):
p.set_trainable(v)
with value_error(v):
p.trainable = v
with value_error(v):
p.is_built(v)
tensor = tf.get_variable('test', shape=())
tensor_non_trainable = tf.get_variable(
'test_non_trainable', shape=(), trainable=False)
p = gpflow.Param(tensor)
p_non_trainable = gpflow.Param(1.0, trainable=False)
with self.assertRaises(GPflowError):
p_non_trainable._check_tensor_trainable(tensor)
with self.assertRaises(GPflowError):
p._check_tensor_trainable(tensor_non_trainable)
with self.assertRaises(GPflowError):
p.read_value(session=None)
for v in ['', 'non-empty', 1.0, object()]:
with self.assertRaises(ValueError):
p.read_value(session=v)
with self.assertRaises(GPflowError):
p.set_trainable(False)
with self.assertRaises(GPflowError):
p.trainable = False
with self.assertRaises(GPflowError):
p.set_trainable(True)
with self.assertRaises(GPflowError):
p.trainable = True
values = ['', 'test', 1., object()]
for v in values:
with self.assertRaises(ValueError, msg='Raised at "{}"'.format(v)):
p.anchor(v)
with self.assertRaises(tf.errors.FailedPreconditionError):
p.anchor(session)
with self.assertRaises(ValueError):
tensor = tf.get_variable('test1', shape=(), trainable=False)
gpflow.Param(tensor)
with self.assertRaises(ValueError):
tensor = tf.get_variable('test2', shape=())
gpflow.Param(tensor, trainable=False)
def test_str(self):
with self.test_context():
def check_str(obj, expect_str):
expect = [e for e in expect_str.format(name=p.name).split(' ') if e != '']
got = [e for e in str(obj).split(' ') if e != '']
print(expect)
print(got)
self.assertEqual(expect, got)
p_str = (' class prior transform trainable shape '
'fixed_shape value\n{name} Parameter None (none)'
' True () True 1.0')
p = gpflow.Param(1., name="short")
check_str(p, p_str)
d_str = (' class shape fixed_shape value'
'\n{name} DataHolder () False 1.0')
d = gpflow.DataHolder(1., name="short")
check_str(d, d_str)
params_str = (' class prior transform trainable shape'
' fixed_shape value\n{name}/p Parameter None'
' (none) True () True 1.0')
params = gpflow.Parameterized(name="short")
params.p = p
params.d = d
check_str(params, params_str)
def test_generators(self):
with self.test_context():
self.assertEqual(len(list(self.m.parameters)), 2)
self.assertEqual(len(list(self.m.data_holders)), 0)
self.assertEqual(len(list(self.m.params)), 2)
def test_assign(self):
with self.test_context(tf.Graph()) as session:
with self.assertRaises(GPflowError):
self.p.read_value(session)
with self.test_context() as session:
self.p.assign(2.0)
self.assertEqual(self.p.read_value(), 2.0)
self.assertEqual(self.p.value, 2.0)
self.m.p = 2.0
self.assertEqual(self.m.p.read_value(), 2.0)
self.assertEqual(self.m.p.value, 2.0)
self.p.assign(100.0, session=session)
self.assertEqual(self.p.read_value(session), 100.0)
self.assertEqual(self.p.value, 100.0)
def test_assign_tensor(self):
with self.test_context():
tensor = tf.get_variable('a', shape=())
param = gpflow.Param(tensor)
with self.assertRaises(GPflowError):
param.assign(10)
def test_floating_assign(self):
with self.test_context():
val = 10.
p = gpflow.Param(val, fix_shape=False)
assert_allclose(p.read_value(), val)
val = [10, 10]
p.assign(val)
assert_allclose(p.read_value(), val)
val = [10, 10, 10]
p.assign(val)
assert_allclose(p.read_value(), val)
val = [[10, 10, 10], [10, 10, 10]]
p.assign(val)
assert_allclose(p.read_value(), val)
with self.test_context():
val = 10.
p = gpflow.Param(val)
val = [10., 10.]
with self.assertRaises(ValueError):
p.assign(val)
val = [[10.]]
with self.assertRaises(ValueError):
p.assign(val)
def test_create_and_replace(self):
with self.test_context():
tensor = tf.get_variable('a', shape=()) + 1.0
param = gpflow.Param(1e3)
with self.assertRaises(ValueError):
external_param = gpflow.Param(tensor)
external_param = gpflow.Param(tensor, trainable=False)
new_param = gpflow.Param(1.0, name='new_param')
self.m.b = external_param
self.assertEqual(self.m.b, external_param)
p = self.m.p
self.m.p = param
assert self.m.p is param
assert p.name.startswith('Parameter')
assert p.root is p
self.m.d = new_param
assert self.m.d is new_param
assert self.m.d.pathname == '{name}/d'.format(name=self.m.name)
def test_assign_with_compile(self):
with self.test_context():
self.p.compile()
self.m.compile()
self.p.assign(2.0)
self.m.p = 2.0
self.assertEqual(self.p.read_value(), 2.0)
self.assertEqual(self.m.p.read_value(), 2.0)
def test_root(self):
self.assertTrue(self.m.p.root is self.m)
def test_existing_tensor(self):
with self.test_context():
_ = tf.get_variable('param/unconstrained', shape=())
with self.assertRaises(GPflowError):
p = gpflow.Param(1.0, name='param')
def test_trainable(self):
self.assertTrue(self.p.trainable)
self.p.trainable = False
self.assertFalse(self.p.trainable)
self.assertTrue(self.m.trainable)
self.m.p.trainable = False
self.assertFalse(self.m.p.trainable)
self.assertTrue(self.m.trainable)
def test_trainable_with_compile(self):
with self.test_context():
self.p.compile()
self.m.compile()
self.assertTrue(self.p.trainable)
self.p.trainable = False
self.assertFalse(self.p.trainable)
self.assertTrue(self.m.trainable)
self.m.p.trainable = False
self.assertTrue(self.m.trainable)
self.assertFalse(self.m.p.trainable)
_check_trainable_flag(self.m, self.assertTrue, self.assertFalse)
def test_fixed_shape(self):
with self.test_context():
p = gpflow.Param(1., fix_shape=False)
self.assertFalse(p.fixed_shape)
self.assertAllEqual(p.shape, ())
self.assertEqual(p.size, 1)
p.assign([10., 10.])
self.assertFalse(p.fixed_shape)
self.assertAllEqual(p.shape, (2,))
self.assertEqual(p.size, 2)
p.fix_shape()
self.assertTrue(p.fixed_shape)
self.assertAllEqual(p.shape, (2,))
self.assertEqual(p.size, 2)
p.assign(np.zeros(p.shape))
with self.assertRaises(ValueError):
p.assign([1.], force=True)
with self.assertRaises(ValueError):
p.assign(1., force=True)
with self.assertRaises(ValueError):
p.assign(np.zeros((3,3)), force=True)
class TestParameterized(GPflowTestCase):
@staticmethod
def create_layout():
p = gpflow.Parameterized(name='p')
p.a = gpflow.Param(10.)
p.b = gpflow.Param(11.)
p.c = gpflow.Parameterized()
p.c.d = gpflow.Param(12., fix_shape=False)
p.c.e = gpflow.DataHolder(13.)
return p
def test_is_built(self):
with self.test_context():
p = gpflow.Parameterized()
self.assertTrue(p.is_built_coherence())
# TODO(@awav): Should it be NO?
self.assertEqual(p.is_built_coherence(tf.Graph()), gpflow.Build.YES)
values = [None, "", 1.0, object()]
for v in values:
with self.assertRaises(ValueError, msg='Passed value {}'.format(v)):
p.is_built(v)
p.a = gpflow.Param(1.0)
self.assertEqual(p.is_built_coherence(), gpflow.Build.NO)
p.compile()
not_compatible = gpflow.Build.NOT_COMPATIBLE_GRAPH
self.assertTrue(p.is_built_coherence())
self.assertEqual(p.is_built(tf.Graph()), not_compatible)
with self.assertRaises(GPflowError):
p.is_built_coherence(tf.Graph())
for v in values:
with self.assertRaises(ValueError, msg='Passed value "{}"'.format(v)):
p.is_built(v)
def test_anchor(self):
with self.test_context() as session:
p = gpflow.Parameterized()
p.a = gpflow.Param(1.0)
p.compile()
with self.assertRaises(ValueError):
p.anchor(None)
new_value = 2.0
p.a.parameter_tensor.load(new_value)
p.anchor(session)
assert_allclose(p.a.read_value(), new_value)
def test_read_values(self):
def check_values(values, expected_dict, unexpected_dicts):
self.assertTrue(values == expected_dict)
for unexpected_dict in unexpected_dicts:
self.assertFalse(values == unexpected_dict)
expected_dict = {'p/a': 10., 'p/b': 11., 'p/c/d': 12.}
unexpected_dicts = [
{'p': 10., 'p/b': 11., 'p/c/d': 12.},
{'p/a': 11., 'p/b': 11., 'p/c/d': 12.},
{'p/a': 11.}
]
with self.test_context() as session:
session_new = tf.Session(graph=session.graph)
self.assertNotEqual(session_new, session)
with session_new.as_default():
with gpflow.defer_build():
p = self.create_layout()
values = p.read_values()
check_values(values, expected_dict, unexpected_dicts)
p.compile()
values = p.read_values()
check_values(values, expected_dict, unexpected_dicts)
with self.assertRaises(tf.errors.FailedPreconditionError):
p.read_values(session=session)
with self.test_context() as session_fail:
self.assertFalse(session == session_fail)
with self.assertRaises(tf.errors.FailedPreconditionError):
p.read_values(session=session_fail)
with self.test_context() as session_intialize:
p.initialize(session=session_intialize)
values = p.read_values(session=session_intialize)
check_values(values, expected_dict, unexpected_dicts)
values = p.read_values(session=session_new)
check_values(values, expected_dict, unexpected_dicts)
session_new.close()
def test_parameterized_assign(self):
with self.test_context():
## Create parameterized object inside context
p = self.create_layout()
values = p.read_values()
values['p/b'] = 100.
values['p/c/d'] = 100.
p.assign(values)
assert_allclose(p.a.read_value(), 10)
assert_allclose(p.b.read_value(), 100)
assert_allclose(p.c.d.read_value(), 100)
values = list(map(float, p.read_values().values()))
self.assertTrue(set(values) == set([10, 100, 100]))
with self.test_context() as session:
assign_values = {'p/a': 1e3, 'p/c/d': 1e4}
p.assign(assign_values, session=session)
assert_allclose(p.a.read_value(), 1e3)
assert_allclose(p.b.read_value(), 100)
assert_allclose(p.c.d.read_value(), 1e4)
values = list(map(float, p.read_values().values()))
self.assertTrue(set(values) == set([1e3, 100, 1e4]))
def test_parameterized_assign_panda(self):
with self.test_context():
p = self.create_layout()
vals1 = [1e2, 1e3, 1e4]
vals2 = [2e2, 2e3, 2e4]
self.assertEqual(len(vals1), len(vals2))
df1 = pd.DataFrame({'p/a': vals1, 'p/c/d': vals1})
df2 = pd.DataFrame({'p/a': vals2, 'p/c/d': vals2})
for i in range(len(vals1)):
df_slice1 = df1.iloc[i]
p.assign(df_slice1, force=False)
values = p.read_values()
for name in df_slice1.index:
assert_allclose(df_slice1[name], values[name])
df_slice2 = df2.iloc[i]
p.assign(df_slice2, force=True)
values = p.read_values()
for name in df_slice2.index:
assert_allclose(df_slice2[name], values[name])
def test_fail_assign(self):
with self.test_context():
p = self.create_layout()
values = [1.0, {'a': 1.0}, None, "", "artem", object()]
for v in values:
with self.assertRaises(ValueError):
p.assign(v)
different_shape = {
'p/a': np.zeros((10, 1)),
'p/b': -1,
'p/c/d': -1
}
a = p.a.read_value()
b = p.b.read_value()
c_d = p.c.d.read_value()
with self.assertRaises(ValueError):
p.assign(different_shape)
assert_allclose(p.a.read_value(), a)
assert_allclose(p.b.read_value(), b)
assert_allclose(p.c.d.read_value(), c_d)
def test_fix_shapes(self):
with self.test_context():
def children(p):
yield from p.parameters
yield from p.data_holders
p = self.create_layout()
self.assertFalse(all([c.fixed_shape for c in children(p)]))
p.fix_shape()
self.assertTrue(all([c.fixed_shape for c in children(p)]))
p = self.create_layout()
p.fix_shape(parameters=False, data_holders=True)
self.assertTrue(all([c.fixed_shape for c in p.data_holders]))
p.fix_shape(parameters=True)
self.assertTrue(all([c.fixed_shape for c in p.parameters]))
self.assertTrue(all([c.fixed_shape for c in children(p)]))
def test_trainables(self):
with self.test_context():
p = self.create_layout()
self.assertTrue(all([c.trainable for c in p.parameters]))
self.assertTrue(p.trainable)
p.set_trainable(False)
self.assertFalse(all([c.trainable for c in p.parameters]))
self.assertFalse(p.trainable)
p.set_trainable(True)
self.assertTrue(all([c.trainable for c in p.parameters]))
self.assertTrue(p.trainable)
values = [None, "test", "", 1]
for v in values:
with self.assertRaises(ValueError, msg='Caught exception for "{}"'.format(v)):
p.set_trainable(v)
class TestParameterizedNoParameters(GPflowTestCase):
def setUp(self):
with self.test_context(), gpflow.defer_build():
self.m = gpflow.params.Parameterized(name='m')
self.m.p = gpflow.params.Parameterized()
self.m.b = gpflow.params.Parameterized()
def test_feeds_empty(self):
with self.test_context():
p = gpflow.Parameterized()
self.assertEqual(p.initializables, [])
self.assertEqual(p.initializable_feeds, {})
self.assertEqual(p.feeds, {})
def test_is_built(self):
with self.test_context():
self.assertEqual(self.m.is_built_coherence(), gpflow.Build.YES)
def test_compile(self):
with self.test_context():
self.m.compile()
self.assertEqual(self.m.is_built_coherence(), gpflow.Build.YES)
def test_generators(self):
with self.test_context():
self.assertEqual(list(self.m.parameters), [])
self.assertEqual(list(self.m.data_holders), [])
self.assertEqual(len(list(self.m.params)), 2)
def test_add_parameter_to_empty_parameterized(self):
with self.test_context():
self.m.compile()
self.m.a = gpflow.Param(10)
self.assertEqual(self.m.is_built_coherence(), gpflow.Build.NO)
self.m.compile()
self.assertEqual(self.m.is_built_coherence(), gpflow.Build.YES)
with self.assertRaises(GPflowError):
self.m.b = gpflow.Param(20)
class TestParameterizedCompile(GPflowTestCase):
def setUp(self):
self.test_graph = tf.Graph()
with self.test_context() as session:
self.graph = session.graph
tensor = tf.get_variable('a', shape=())
self.m = gpflow.params.Parameterized(name='m')
self.m.p = gpflow.params.Parameterized()
self.m.a = gpflow.Param(tensor)
self.m.b = gpflow.Param(1.0, trainable=False)
self.m.c = gpflow.Param(np.array([1.0, 2.0]))
self.m.p.d = gpflow.Param(1.0)
def test_compile(self):
with self.test_context():
tensor = self.m.a.parameter_tensor
self.m.compile()
self.assertEqual(len(list(self.m.parameters)), 4)
self.assertEqual(len(list(self.m.trainable_tensors)), 3)
self.assertEqual(self.m.a.parameter_tensor, tensor)
for param in self.m.parameters:
self.assertTrue(gpflow.misc.is_tensor(param.parameter_tensor))
self.assertTrue(gpflow.misc.is_tensor(param.constrained_tensor))
self.assertTrue(gpflow.misc.is_tensor(param.prior_tensor))
def test_modify_compiled(self):
with self.test_context():
self.assertEqual(len(list(self.m.parameters)), 4)
self.assertEqual(len(list(self.m.trainable_tensors)), 3)
for param in self.m.parameters:
self.assertTrue(gpflow.misc.is_tensor(param.parameter_tensor))
self.assertTrue(gpflow.misc.is_tensor(param.constrained_tensor))
self.assertTrue(gpflow.misc.is_tensor(param.prior_tensor))
def test_fails_after_compile(self):
with self.test_context(self.graph):
self.m.compile()
with self.assertRaises(GPflowError):
self.m.d = gpflow.Param(1.0)
with self.assertRaises(AttributeError):
_param = self.m.d
def test_compile(self):
with self.test_context():
self.m.compile()
with self.test_context() as session:
self.m.compile(session=session)
class TestAutobuild(GPflowTestCase):
def test_autobuild_option(self):
with self.test_context():
foo = Foo(autobuild=False)
equal = self.assertEqual
equal(foo.is_built(tf.get_default_graph()), gpflow.Build.NO)
equal(foo.is_built_coherence(), gpflow.Build.NO)
p = gpflow.Param(10)
equal(p.is_built(tf.get_default_graph()), gpflow.Build.YES)
equal(p.is_built_coherence(), gpflow.Build.YES)
b = gpflow.Param(10, autobuild=False)
equal(b.is_built(tf.get_default_graph()), gpflow.Build.NO)
equal(b.is_built_coherence(), gpflow.Build.NO)
foo.p = p
equal(foo.p, p)
equal(hasattr(foo, 'p'), True)
equal(foo.is_built(tf.get_default_graph()), gpflow.Build.NO)
equal(foo.is_built_coherence(), gpflow.Build.NO)
foo.b = b
equal(foo.b, b)
equal(hasattr(foo, 'b'), True)
equal(foo.is_built(tf.get_default_graph()), gpflow.Build.NO)
equal(foo.is_built_coherence(), gpflow.Build.NO)
foo.compile()
equal(foo.is_built(tf.get_default_graph()), gpflow.Build.YES)
equal(foo.is_built_coherence(), gpflow.Build.YES)
equal(p.is_built(tf.get_default_graph()), gpflow.Build.YES)
equal(p.is_built_coherence(), gpflow.Build.YES)
equal(b.is_built(tf.get_default_graph()), gpflow.Build.YES)
equal(b.is_built_coherence(), gpflow.Build.YES)
class TestParameterizedDeep(GPflowTestCase):
def setUp(self):
with self.test_context():
self.m = gpflow.params.Parameterized(name='m')
self.m.a = gpflow.Param(1.0, trainable=False)
self.m.foo = gpflow.params.Parameterized()
self.m.foo.bar = gpflow.params.Parameterized()
self.m.foo.bar.baz = gpflow.Param(1.0)
def test_generators(self):
with self.test_context():
self.assertEqual(len(list(self.m.parameters)), 2)
self.assertEqual(len(list(self.m.data_holders)), 0)
self.assertEqual(len(list(self.m.params)), 2)
def test_root(self):
self.assertTrue(self.m.foo.root is self.m)
self.assertTrue(self.m.foo.bar.root is self.m)
self.assertTrue(self.m.foo.bar.baz.root is self.m)
def test_deep_name(self):
assert self.m.foo.pathname == 'm/foo'
assert self.m.foo.bar.pathname == 'm/foo/bar'
assert self.m.foo.bar.baz.pathname == 'm/foo/bar/baz'
def test_deep_trainable(self):
with self.test_context():
self.m.compile()
self.m.trainable = False
self.assertEqual(len(list(self.m.trainable_tensors)), 0)
_check_trainable_flag(self.m, self.assertTrue, self.assertFalse)
self.m.trainable = True
self.assertEqual(
len(list(self.m.parameters)),
len(list(self.m.trainable_tensors)))
_check_trainable_flag(self.m, self.assertTrue, self.assertFalse)
class TestParamLikeInvariant(GPflowTestCase):
def test_self_reference(self):
m = gpflow.params.Parameterized()
with self.assertRaises(ValueError):
m.foo = m
m.foo = gpflow.params.Parameterized()
with self.assertRaises(ValueError):
m.foo.bar = m
def test_reassign(self):
m = gpflow.params.Parameterized()
p = gpflow.params.Parameterized()
m.foo = p # assign
m.foo = p # reassign
# TODO(@awav):
# m = gpflow.params.Parameterized()
# m.foo = gpflow.params.Parameterized()
# m.foo.bar = gpflow.params.Parameterized()
# with self.assertRaises(ValueError):
# m.baz = m.foo.bar
# TODO(@awav):
#m = gpflow.params.Parameterized()
#m.foo = gpflow.params.Parameterized()
#m.foo.bar = gpflow.params.Parameterized()
#m.boo = gpflow.params.Parameterized()
#with self.assertRaises(ValueError):
# m.boo.far = m.foo.bar
# TODO(@awav):
# def testAddingToAnother(self):
# """
# Adding the same Paramterized object to another tree is fine.
# """
# m1 = gpflow.params.Parameterized()
# m1.foo = gpflow.params.Parameterized()
# m2 = gpflow.params.Parameterized()
# with self.assertRaises(GPflowError):
# m2.foo = m1.foo
class TestParamList(GPflowTestCase):
def test_construction(self):
with self.test_context():
gpflow.ParamList([])
gpflow.ParamList([gpflow.Param(1)])
gpflow.ParamList([1.0, np.array([1, 2]), gpflow.Param(1.0)])
with self.assertRaises(ValueError):
gpflow.ParamList([gpflow.Param(1), 'stringsnotallowed'])
with self.assertRaises(ValueError):
# tuples not valid in constuctor:
gpflow.ParamList((gpflow.Param(1),))
with self.assertRaises(ValueError):
# param objects not valid in constructor (must be in list)
gpflow.ParamList(gpflow.Param(1))
with gpflow.defer_build():
p = gpflow.ParamList([0.0])
p[0] = gpflow.Param(1.0)
with self.assertRaises(ValueError):
p[0] = 1.0
with self.assertRaises(ValueError):
p[0] = "test"
p = gpflow.ParamList([])
p.append(gpflow.Param(1.0))
p.append(gpflow.Param(2.0))
p.append(2.0)
self.assertEqual(len(p), 3)
with self.assertRaises(ValueError):
p.append("test")
def test_naming(self):
with self.test_context():
p1 = gpflow.Param(1.2)
p2 = gpflow.Param(np.array([3.4, 5.6], settings.float_type))
l = gpflow.ParamList([p1, p2])
assert p1.pathname == l.name + '/0'
assert p2.pathname == l.name + '/1'
def test_setitem(self):
with self.test_context():
p1 = gpflow.Param(1.2)
p2 = gpflow.Param(np.array([3.4, 5.6], settings.float_type))
param_list = gpflow.ParamList([p1, p2], name='param_list', autobuild=False)
self.assertEqual(p1.read_value(), param_list[0].read_value())
self.assertTrue(np.all(param_list[1].read_value() == p2.read_value()))
param_list[0] = gpflow.Param(2.0)
self.assertEqual(p1.read_value(), 1.2)
self.assertEqual(p1.root, p1)
self.assertEqual(param_list[0].read_value(), 2.0)
arr = np.array([1.1, 2.2], settings.float_type)
param_list[1] = gpflow.Param(arr)
self.assertEqual(p2.root, p2)
self.assertTrue(np.all(param_list[1].read_value() == arr))
param_list.compile()
with self.assertRaises(GPflowError):
param_list[0] = gpflow.Param(12)
def test_append(self):
with self.test_context():
p1 = gpflow.Param(1.2)
p4 = gpflow.Param(np.array([3.4, 5.6], settings.float_type))
with gpflow.defer_build():
p2 = gpflow.Param(1.2)
param_list = gpflow.ParamList([p1])
param_list.append(p2)
p3 = gpflow.Param(1.2)
param_list.append(p3)
param_list.compile()
with self.assertRaises(gpflow.GPflowError):
param_list.append(p4)
self.assertTrue(p1 in param_list.params)
self.assertTrue(p2 in param_list.params)
self.assertTrue(p3 in param_list.params)
self.assertFalse(p4 in param_list.params)
with self.assertRaises(ValueError):
param_list.append('foo')
def test_len(self):
with self.test_context():
p1 = gpflow.Param(1.2)
p2 = gpflow.Param(np.array([3.4, 5.6], settings.float_type))
l = gpflow.ParamList([p1, p2])
self.assertTrue(len(l) == 2)
def test_with_parameterized(self):
with self.test_context():
pzd = gpflow.params.Parameterized()
p = gpflow.Param(1.2)
pzd.p = p
param_list = gpflow.ParamList([pzd])
param_list[0].p = 5.
self.assertEqual(param_list[0].p.read_value(), 5)
def test_in_model(self):
class Foo(gpflow.models.Model):
def __init__(self):
gpflow.models.Model.__init__(self)
self.param_list = gpflow.ParamList([gpflow.Param(1.), gpflow.Param(12.)])
@gpflow.params_as_tensors
def _build_likelihood(self):
return -tf.add_n([tf.square(x) for x in self.param_list])
with self.test_context():
m = Foo()
m.compile()
optimizer = gpflow.train.ScipyOptimizer()
optimizer.minimize(m, maxiter=10)
atol = 1e-6 if settings.float_type is np.float32 else 1e-8
params = [param.read_value() for param in m.parameters]
self.assertTrue(np.allclose(params, 0., atol=atol))
class TestFixWithPrior(GPflowTestCase):
"""
This tests that models with a fixed parameter which has a prior continue to work
"""
def test_non_trainable_with_prior(self):
with self.test_context():
m = Foo(autobuild=False)
m.p = gpflow.Param(1.0, gpflow.transforms.positive, autobuild=False)
m.pp = gpflow.Param(1.0, gpflow.transforms.positive, autobuild=False)
m.p.prior = gpflow.priors.Gamma(1, 1)
m.pp.prior = gpflow.priors.Gamma(1, 1)
m.p.trainable = False
m.compile()
optimizer = gpflow.train.ScipyOptimizer()
optimizer.minimize(m, maxiter=10)
#class TestRandomizeDefault(GPflowTestCase):
# """
# This tests that distributions can sample random values without priors
# """
#
# def test(self):
# with self.test_context():
# np.random.seed(1)
# m = gpflow.models.Model()
# m.p = gpflow.Param(1.0)
# m.pp = gpflow.Param(1.0, gpflow.transforms.Log1pe())
# m.pf = gpflow.Param(1.0)
# m.pf.trainable = False
#
# m.pmd = gpflow.Param(np.ones((5, 2)))
# ltr = gpflow.transforms.LowerTriangular(1,2).forward(np.ones(2 * 10))
# m.pmd2 = gpflow.Param(
# ltr, transform=gpflow.transforms.LowerTriangular(1,2))
#
# #should work as (pseudo) random vals a.s. are not 1.0
# m.p.randomize()
# self.assertFalse(m.p.value == 1.0)
# m.pp.randomize()
# self.assertFalse(m.pp.value == 1.0 or m.pp.value <= 0.0)
#
# #check if fixing works
# m.pf.randomize()
# self.assertTrue(m.pf.value == 1.0)
# m.pf.randomize(skipfixed=False)
# self.assertFalse(m.pf.value == 1.0)
#
# #check multidimensional
# pmd_shape = m.pmd.shape
# m.pmd.randomize()
# self.assertFalse(np.any(m.pmd.value == 1.0))
# self.assertEquals(m.pmd.shape, pmd_shape)
#
# #check non size-preserving transform
# pmd2_shape = m.pmd2.shape
# m.pmd2.randomize()
# self.assertFalse(np.any(m.pmd2.value == 1.0))
# self.assertEquals(m.pmd2.shape, pmd2_shape)
#
#class TestRandomizePrior(GPflowTestCase):
# """
# This tests that distributions can sample random values from priors
# """
#
# def test(self):
# with self.test_context():
# np.random.seed(1)
# from inspect import getargspec
#
# m = gpflow.models.Model()
# m.p = gpflow.Param(1.0)
# m.pmd = gpflow.Param(
# np.eye(5), transform=gpflow.transforms.DiagMatrix())
#
# priors = [obj for obj in gpflow.priors.__dict__.values() if
# isinstance(obj, type) and
# issubclass(obj, gpflow.priors._prior) and
# obj is not gpflow.priors._prior]
#
# with self.assertRaises(NotImplementedError):
# m.p = 1.0
# m.p.prior = gpflow.priors._prior()
# m.p.randomize()
#
# for prior in priors:
# signature = getargspec(prior.__init__)
# params = {}
# if signature.defaults is not None:
# param_names = signature.args[:-len(signature.defaults)]
# else:
# param_names = signature.args
# for param in param_names:
# if param not in params.keys() and param is not 'self':
# params[param] = 1.
#
# m.p = 1.0
# m.p.prior = prior(**params)
# m.pmd.prior = prior(**params)
# m.p.randomize()
# m.pmd.randomize()
# self.assertFalse(m.p.value == 1.0)
# self.assertFalse(np.any(m.pmd.value == np.ones(5)))
# self.assertTrue(m.pmd.value.shape == (5,5))
#
#
#class TestRandomizeFeedPriors(GPflowTestCase):
# """
# Test if standard randomize behavior can be overriden using
# distributions keyword.
# """
#
# def test(self):
# with self.test_context():
# np.random.seed(1)
# m = gpflow.models.Model()
# m.p = gpflow.Param(1.0)
# with self.assertRaises(NotImplementedError):
# m.p.randomize(distributions={m.p: gpflow.priors._prior()})
# m.p.randomize(distributions={m.p: gpflow.priors.Gaussian(0, 1)})
# self.assertFalse(m.p.value == 1.0)
#
#
#class TestRandomizeHierarchical(GPflowTestCase):
# """
# This tests that models can randomize all contained parameters
# """
#
# def test(self):
# with self.test_context():
# np.random.seed(1)
# m = gpflow.models.Model()
# m.p = gpflow.Param(1.0)
# m.p2 = gpflow.Param(1.0)
# m.m = gpflow.models.Model()
# m.m.p = gpflow.Param(1.0)
# m.m.p2 = gpflow.Param(1.0)
#
# m.p2.prior = gpflow.priors.Gaussian(0, 1)
# m.m.p2.prior = gpflow.priors.Gaussian(0, 1)
# m.randomize()
#
# self.assertFalse(m.p.value == 1.0)
# self.assertFalse(m.p2.value == 1.0)
# self.assertFalse(m.m.p.value == 1.0)
# self.assertFalse(m.m.p2.value == 1.0)
class TestScopes(GPflowTestCase):
def setUp(self):
with self.test_context() as session:
self.graph = session.graph
rng = np.random.RandomState(0)
X = rng.randn(10, 1)
Y = rng.randn(10, 1)
k = gpflow.kernels.RBF(1)
self.m = gpflow.models.GPR(X, Y, k)
self.m.compile()
def test_likelihood_name(self):
likelihood = self.m.likelihood_tensor
expected_name = self.m.tf_name_scope + '/likelihood'
self.assertTrue(likelihood.name.startswith(expected_name))
def test_kern_name(self):
with self.test_context(self.graph):
@gpflow.name_scope('test_kernel')
@gpflow.params_as_tensors
def run_kernel(m):
return m.kern.K(m.X)
K = run_kernel(self.m)
self.assertTrue(K.name.startswith('test_kernel/'))
def _check_trainable_flag(m, assert_true, assert_false):
for param in m.parameters:
assert_bool = assert_false
if param.trainable:
assert_bool = assert_true
assert_bool(gpflow.misc.is_tensor_trainable(param.parameter_tensor))
@pytest.fixture
def param(session_tf):
return gpflow.Param(10.)
@pytest.fixture
def params_tree(session_tf):
p = gpflow.Parameterized()
p.a = gpflow.Param(1.)
return p
def failures():
return [None, 1, "unknown", object()]
@pytest.mark.parametrize('arg', failures())
def test_parentable_childname_failures(params_tree, arg):
with pytest.raises(ValueError):
params_tree.childname(arg)
def test_parentable_childname_not_found(param, params_tree):
with pytest.raises(KeyError):
params_tree.childname(param)
@pytest.mark.parametrize('arg', failures())
def test_parentable_set_child_failure(params_tree, arg):
with pytest.raises(ValueError):
params_tree.set_child('b', arg)
with pytest.raises(ValueError):
params_tree.set_child('a', arg)
def test_parentable_unset_child_not_found(params_tree, param):
with pytest.raises(ValueError):
params_tree.unset_child('b', param)
with pytest.raises(ValueError):
params_tree.unset_child('a', param)
def test_parentable_unset_child_not_found(params_tree, param):
with pytest.raises(ValueError):
params_tree.unset_child('b', param)
with pytest.raises(ValueError):
params_tree.unset_child('a', param)
@pytest.mark.parametrize('arg', failures()[1:])
def test_parentable_set_parent_failures(param, arg):
with pytest.raises(ValueError):
param.set_parent(arg)
def test_parentable_set_parent_self_reference(params_tree):
with pytest.raises(ValueError):
params_tree.a.set_parent(params_tree)
def test_as_pandas_table_static(params_tree):
pt1 = params_tree.as_pandas_table()
pt2 = params_tree.as_pandas_table()
assert pt1.equals(pt2)
params_tree.a = params_tree.a.value + 5.0
pt3 = params_tree.as_pandas_table()
assert not pt1.equals(pt3)
if __name__ == '__main__':
tf.test.main()
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