def _prune(self, xs, retain=None):
"""Drops fields from `xs`, retaining those specified by `retain`.
Args:
xs: A structure like that of `self.distribution.dtype`
retain: One of `'pinned'` or `'unpinned'`.
Returns:
xs: Input `xs`, pruned to retain only the parts specified by `retain`.
"""
if retain not in ('pinned', 'unpinned'):
raise ValueError('Invalid value for `retain`: {}'.format(retain))
def should_retain(k):
return (k not in self.pins) ^ (retain == 'pinned')
if isinstance(xs, dict):
return type(xs)((k, v) for k, v in xs.items() if should_retain(k))
if hasattr(xs, '_fields') and hasattr(xs, '_asdict'):
tuple_type = structural_tuple.structtuple(
[k for k in xs._fields if should_retain(k)])
return tuple_type(
**{k: v
for k, v in xs._asdict().items() if should_retain(k)})
names = self.distribution._flat_resolve_names()
return type(xs)(
[x for i, x in enumerate(xs) if should_retain(names[i])])
def testValidNamedTuple(self):
t = structural_tuple.structtuple(['a', 'b', 'c'])
inst = t(a=1, b=2, c=3)
a, b, c = inst
self.assertAllEqualNested((1, 2, 3), (a, b, c))
self.assertAllEqualNested(
t(2, 3, 4), tf.nest.map_structure(lambda x: x + 1, inst))
def testArgsExpansion(self):
def foo(a, b):
return a + b
t = structural_tuple.structtuple(['c', 'd'])
self.assertEqual(3, nest_util.call_fn(foo, t(1, 2)))
def testMake(self): t = structural_tuple.structtuple(['a', 'b']) ab = t._make((1, 2)) self.assertEqual(1, ab.a) self.assertEqual(2, ab.b) ab = t._make((1,)) self.assertEqual(1, ab.a) self.assertIs(None, ab.b)
def test_event_shape_and_structure_jd_coroutine(self):
self.assertEqual(
structural_tuple.structtuple(['x', 'y', 'z'])([], [2], [2, 4, 4]),
tfde.JointDistributionPinned(jd_coroutine(), w=1.).event_shape)
self.assertEqual(
structural_tuple.structtuple(['w', 'z'])([], [2, 4, 4]),
tfde.JointDistributionPinned(jd_coroutine(), None, 1.,
[2., 3]).event_shape)
self.assertEqual(
structural_tuple.structtuple(['w', 'y', 'z'])([], [2], [2, 4, 4]),
tfde.JointDistributionPinned(jd_coroutine(), x=2.).event_shape)
obs = jd_coroutine().sample(seed=test_util.test_seed())[-1:]
self.assertEqual(obs._fields, ('z', ))
self.assertEqual(
structural_tuple.structtuple(['w', 'x', 'y'])([], [], [2]),
tfde.JointDistributionPinned(jd_coroutine(), obs).event_shape)
def testConcatenation(self):
t1 = structural_tuple.structtuple(['a', 'b'])
t2 = structural_tuple.structtuple(['c', 'd'])
ab = t1(a=1, b=2)
cd = t2(c=3, d=4)
abcd = ab + cd
self.assertAllEqual((1, 2, 3, 4), tuple(abcd))
self.assertAllEqual(('a', 'b', 'c', 'd'), abcd._fields)
cdab = cd + ab
self.assertAllEqual((3, 4, 1, 2), tuple(cdab))
self.assertAllEqual(('c', 'd', 'a', 'b'), cdab._fields)
ab_tuple = ab + (3,)
self.assertAllEqual((1, 2, 3), ab_tuple)
tuple_ab = (3,) + ab
self.assertAllEqual((3, 1, 2), tuple_ab)
def _model_unflatten(self, xs):
"""Unflattens `xs` to a structure like-typed to `self.distribution`."""
# Use the underlying JD dtype to infer model structure.
dtype = self.distribution.dtype
if isinstance(dtype, dict):
ks = self._flat_resolve_names()
if len(ks) != len(xs):
raise ValueError('Invalid xs length {}, ks={}'.format(len(xs), ks))
return type(dtype)(zip(ks, xs))
if hasattr(dtype, '_fields') and hasattr(dtype, '_asdict'):
ks = [k for k in dtype._fields if k not in self.pins]
return structural_tuple.structtuple(ks)(*xs)
return type(dtype)(xs)
def _initialize_parameters(generator, seed=None):
"""Samples initial values for all parameters yielded by a generator.
Args:
generator: Python generator that yields initialization callables
(which take a `seed` and return a (structure of) `Tensor`(s)),
returns a value, and has no side effects. See module description.
seed: PRNG seed; see `tfp.random.sanitize_seed` for details.
Returns:
raw_parameters: Python list of `Tensor` (or structure of `Tensor`s) initial
parameter values returned from the yielded callables.
"""
gen = generator()
if not isinstance(gen, types.GeneratorType):
raise ValueError(
'Expected generator but saw function: {}. A generator '
'must contain at least one `yield` statement. To define a '
'trivial generator, which yields zero times, a `yield` '
'statement may be placed after `return`, but must still '
'be present.'.format(generator))
raw_parameters = []
parameter_names = []
param_value = None
try:
while True:
parameter = gen.send(param_value)
if not hasattr(parameter, 'init_fn'):
raise ValueError(
'Expected generator to yield a '
'trainable_state_util.Parameter namedtuple, but saw '
'{} instead.'.format(parameter))
seed, local_seed = samplers.split_seed(seed, n=2)
# Note: this framework guarantees that the `init_fn` is only ever called
# here, immediately after being yielded before control is returned
# to the coroutine. This allows the coroutine to safely incorporate
# loop-dependent state in the closure of `init_fn` if desired.
param_value = _call_init_fn(parameter.init_fn, seed=local_seed)
raw_value = (param_value
if parameter.constraining_bijector is None else
parameter.constraining_bijector.inverse(param_value))
raw_parameters.append(raw_value)
parameter_names.append(
_get_unused_parameter_name(parameter.name or 'parameter',
parameter_names))
except StopIteration:
pass
return structural_tuple.structtuple(parameter_names)(*raw_parameters)
def test_dtype_and_structure_jd_named(self):
self.assertEqual(dict(x=tf.float32, y=tf.float32, z=tf.float32),
tfde.JointDistributionPinned(jd_named(), w=1.).dtype)
self.assertEqual(
collections.OrderedDict((('w', tf.float32), ('z', tf.float32))),
tfde.JointDistributionPinned(jd_named_ordered(), None, 1.,
[2., 3]).dtype)
self.assertEqual(
collections.OrderedDict(
(('w', tf.float32), ('x', tf.float32), ('z', tf.float32))),
tfde.JointDistributionPinned(jd_named_ordered(), y=[2., 3]).dtype)
self.assertEqual(
structural_tuple.structtuple(['w', 'y',
'z'])(tf.float32, tf.float32,
tf.float32),
tfde.JointDistributionPinned(jd_named_namedtuple(), x=2.).dtype)
def test_event_shape_and_structure_jd_named(self):
self.assertEqual(
dict(x=[], y=[2], z=[2, 4, 4]),
tfde.JointDistributionPinned(jd_named(), w=1.).event_shape)
self.assertEqual(
collections.OrderedDict((('w', []), ('z', [2, 4, 4]))),
tfde.JointDistributionPinned(jd_named_ordered(), None, 1.,
[2., 3]).event_shape)
self.assertEqual(
collections.OrderedDict((('w', []), ('x', []), ('z', [2, 4, 4]))),
tfde.JointDistributionPinned(jd_named_ordered(),
y=[2., 3]).event_shape)
self.assertEqual(
structural_tuple.structtuple(['w', 'y', 'z'])([], [2], [2, 4, 4]),
tfde.JointDistributionPinned(jd_named_namedtuple(),
x=2.).event_shape)
def testSlicing(self):
t = structural_tuple.structtuple(['a', 'b', 'c'])
inst = t(a=1, b=2, c=3)
abc = inst[:]
self.assertAllEqual((1, 2, 3), tuple(abc))
self.assertAllEqual(('a', 'b', 'c'), abc._fields)
ab = inst[:2]
self.assertAllEqual((1, 2), tuple(ab))
self.assertAllEqual(('a', 'b'), ab._fields)
ac = inst[::2]
self.assertAllEqual((1, 3), tuple(ac))
self.assertAllEqual(('a', 'c'), ac._fields)
ab2 = abc[:2]
self.assertAllEqual((1, 2), tuple(ab2))
self.assertAllEqual(('a', 'b'), ab2._fields)
def test_bijector(self):
jd = tfd.JointDistributionSequential([
tfd.Uniform(-1., 1.), lambda a: tfd.Uniform(
a + tf.ones_like(a), a + tf.constant(2, a.dtype)),
lambda b, a: tfd.Uniform(a, b, name='c')
])
bij = jd._experimental_default_event_space_bijector(a=-.5, b=1.)
self.assertAllClose((2 / 3, ), tf.math.sigmoid(bij.inverse((0.5, ))))
@tfd.JointDistributionCoroutine
def model():
root = tfd.JointDistributionCoroutine.Root
x = yield root(tfd.Normal(0., 1., name='x'))
y = yield root(tfd.Gamma(1., 1., name='y'))
yield tfd.Normal(x, y, name='z')
bij = model._experimental_default_event_space_bijector(
model.sample(seed=test_util.test_seed())[-1:])
self.assertAllCloseNested(
structural_tuple.structtuple(['x', 'y'])(1., 2.),
bij.forward((1., tfp.math.softplus_inverse(2.))))
def testReplaceUnknownFields(self):
t = structural_tuple.structtuple(['a'])
a = t()
with self.assertRaisesRegexp(
ValueError, r'Got unexpected field names: \[\'b\', \'c\'\]'):
a._replace(b=1, c=2)
def testMakeTooManyValues(self):
t = structural_tuple.structtuple(['a', 'b'])
with self.assertRaisesRegexp(TypeError,
'Expected 2 arguments or fewer, got 3'):
t._make([1, 2, 3])
def testMoreThan255Fields(self):
num_fields = 1000
t = structural_tuple.structtuple(
['field{}'.format(n) for n in range(num_fields)])
self.assertLen(t._fields, num_fields)
def testInvalidIdentifierField(self):
with self.assertRaisesRegexp(ValueError,
'Field names must be valid identifiers: 0'):
structural_tuple.structtuple(['0'])
def testNonStrField(self):
with self.assertRaisesRegexp(
TypeError, 'Field names must be strings: 1 has type <class \'int\'>'):
structural_tuple.structtuple([1])
def testUnderscoreField(self):
with self.assertRaisesRegexp(
ValueError, 'Field names cannot start with an underscore: _a'):
structural_tuple.structtuple(['_a'])
def testKeywordField(self):
with self.assertRaisesRegexp(ValueError,
'Field names cannot be a keyword: def'):
structural_tuple.structtuple(['def'])
def testDuplicateConstructorArg(self):
t = structural_tuple.structtuple(['a'])
with self.assertRaisesRegexp(TypeError,
'Got multiple values for argument a'):
t(1, a=2)
def testDuplicateField(self):
with self.assertRaisesRegexp(ValueError,
'Encountered duplicate field name: a'):
structural_tuple.structtuple(['a', 'a'])
def testCacheWithUnderscores(self): t1 = structural_tuple.structtuple(['a_b', 'b']) t2 = structural_tuple.structtuple(['a', 'b_c']) self.assertIsNot(t1, t2)
def testCacheWorks(self): t1 = structural_tuple.structtuple(['a', 'b', 'c']) t2 = structural_tuple.structtuple(['a', 'b', 'c']) t3 = structural_tuple.structtuple(['a', 'b']) self.assertIs(t1, t2) self.assertIsNot(t1, t3)
def testUnexpectedConstructorArg(self):
t = structural_tuple.structtuple(['a'])
with self.assertRaisesRegexp(TypeError,
'Got an unexpected keyword argument b'):
t(b=2)
def test_bijector_unconstrained_shapes(self):
pinned = tfde.JointDistributionPinned(jd_coroutine(), x=1., y=[1., 1])
bij = pinned._experimental_default_event_space_bijector()
self.assertEqual(
structural_tuple.structtuple(['w', 'z'])([], [2, 6]),
bij.inverse_event_shape(pinned.event_shape))
def testMissingAttribute(self):
t = structural_tuple.structtuple(['a'])
a = t()
with self.assertRaisesRegexp(AttributeError,
'StructTuple has no attribute b'):
_ = a.b
def _model_unflatten(self, xs):
if self._sample_dtype is None:
return structural_tuple.structtuple(self._flat_resolve_names())(*xs)
# Cast `xs` as `tuple` so we can handle generators.
return tf.nest.pack_sequence_as(self._sample_dtype, tuple(xs))
def testTreeUtilIntegration(self): t = structural_tuple.structtuple(['a', 'b', 'c']) inst = t(a=1, b=2, c=3) mapped = jax.tree_util.tree_map(lambda x: x + 1, inst) self.assertIsInstance(mapped, type(inst)) self.assertAllEqualNested(t(2, 3, 4), mapped)
