def testLogNormalLogNormalKL(self):
batch_size = 6
mu_a = np.array([3.0] * batch_size)
sigma_a = np.array([1.0, 2.0, 3.0, 1.5, 2.5, 3.5])
mu_b = np.array([-3.0] * batch_size)
sigma_b = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0])
ln_a = tfd.LogNormal(loc=mu_a, scale=sigma_a, validate_args=True)
ln_b = tfd.LogNormal(loc=mu_b, scale=sigma_b, validate_args=True)
kl = tfd.kl_divergence(ln_a, ln_b)
kl_val = self.evaluate(kl)
normal_a = tfd.Normal(loc=mu_a, scale=sigma_a, validate_args=True)
normal_b = tfd.Normal(loc=mu_b, scale=sigma_b, validate_args=True)
kl_expected_from_normal = tfd.kl_divergence(normal_a, normal_b)
kl_expected_from_formula = (
(mu_a - mu_b)**2 / (2 * sigma_b**2) + 0.5 *
((sigma_a**2 / sigma_b**2) - 1 - 2 * np.log(sigma_a / sigma_b)))
x = ln_a.sample(int(2e5), seed=test_util.test_seed())
kl_sample = tf.reduce_mean(ln_a.log_prob(x) - ln_b.log_prob(x), axis=0)
kl_sample_ = self.evaluate(kl_sample)
self.assertEqual(kl.shape, (batch_size, ))
self.assertAllClose(kl_val, kl_expected_from_normal)
self.assertAllClose(kl_val, kl_expected_from_formula)
self.assertAllClose(kl_expected_from_formula,
kl_sample_,
atol=0.0,
rtol=1e-2)
def new(params,
event_shape,
loc_activation,
scale_activation,
validate_args=False,
name="LogNormalLayer"):
"""Create the distribution instance from a `params` vector."""
params = tf.convert_to_tensor(value=params, name='params')
event_shape = dist_util.expand_to_vector(
tf.convert_to_tensor(value=event_shape,
name='event_shape',
dtype=tf.int32),
tensor_name='event_shape',
)
output_shape = tf.concat(
[tf.shape(input=params)[:-1], event_shape],
axis=0,
)
loc_params, scale_params = tf.split(params, 2, axis=-1)
loc_params = tf.reshape(loc_activation(loc_params), output_shape)
scale_params = tf.reshape(scale_activation(scale_params), output_shape)
return tfd.Independent(
tfd.LogNormal(loc=loc_params,
scale=scale_params,
validate_args=validate_args),
reinterpreted_batch_ndims=tf.size(input=event_shape),
name=name,
)
def _as_distribution(self, r):
scale = DeferredTensor(self._scale,
lambda x: tf.cast(x, r.dtype),
dtype=r.dtype)
return tfd.LogNormal(loc=DeferredTensor(
r, lambda x: tf.math.log(tf.math.softplus(x)) - 0.5 * scale**2.),
scale=scale)
def new(params,
event_shape=(),
softplus_scale=True,
validate_args=False,
name=None):
"""Create the distribution instance from a `params` vector."""
with tf.compat.v1.name_scope(name, 'LogNormal', [params, event_shape]):
params = tf.convert_to_tensor(value=params, name='params')
event_shape = dist_util.expand_to_vector(tf.convert_to_tensor(
value=event_shape, name='event_shape', dtype=tf.int32),
tensor_name='event_shape')
output_shape = tf.concat([
tf.shape(input=params)[:-1],
event_shape,
],
axis=0)
loc_params, scale_params = tf.split(params, 2, axis=-1)
if softplus_scale:
scale_params = tf.math.softplus(
scale_params) + tfd.softplus_inverse(1.0)
return tfd.Independent(
tfd.LogNormal(loc=tf.reshape(loc_params, output_shape),
scale=tf.reshape(scale_params, output_shape),
validate_args=validate_args),
reinterpreted_batch_ndims=tf.size(input=event_shape),
validate_args=validate_args)
def testComposeFromNonTransformedDistribution(self):
actual_log_normal = tfb.Exp()(tfd.Normal(0.5, 2.))
expected_log_normal = tfd.LogNormal(0.5, 2.)
x = tf.constant([0.1, 1., 5.])
self.assertAllClose(
*self.evaluate([actual_log_normal.log_prob(x),
expected_log_normal.log_prob(x)]),
atol=0, rtol=1e-3)
def testHandlesKwargs(self):
x = tfb.Exp()(tfd.Normal(0, 1), event_shape=[4])
y = tfd.Independent(tfd.LogNormal(tf.zeros(4), 1), 1)
z = tf.constant([[1., 2, 3, 4],
[0.5, 1.5, 2., 2.5]])
self.assertAllClose(
*self.evaluate([y.log_prob(z), x.log_prob(z)]),
atol=0, rtol=1e-3)
def setUp(self):
super(LogNormalSoftplusTest, self).setUp()
self.dtype = np.float32
s = 0.75
self.model = tfp.glm.LogNormalSoftplus(s)
self.expected = tfp.glm.CustomExponentialFamily(
lambda mean: tfd.LogNormal(tf.math.log(mean) - 0.5 * s**2, s),
tf.nn.softplus)
def testComposeFromTransformedDistribution(self):
actual_log_normal = tfb.Exp()(tfd.TransformedDistribution(
distribution=tfd.Normal(0, 1),
bijector=tfb.AffineScalar(shift=0.5, scale=2.)))
expected_log_normal = tfd.LogNormal(0.5, 2.)
x = tf.constant([0.1, 1., 5.])
self.assertAllClose(
*self.evaluate([actual_log_normal.log_prob(x),
expected_log_normal.log_prob(x)]),
atol=0, rtol=1e-3)
def testLogNormalCDF(self):
loc, scale = 1.5, 0.4
dist = tfd.LogNormal(loc=loc, scale=scale, validate_args=True)
x = np.array([1e-4, 1.0, 2.0], dtype=np.float32)
cdf = dist.cdf(x)
analytical_cdf = .5 + .5 * tf.math.erf(
(np.log(x) - loc) / (scale * np.sqrt(2)))
self.assertAllClose(self.evaluate(cdf), self.evaluate(analytical_cdf))
def testLogNormalSample(self):
loc, scale = 1.5, 0.4
dist = tfd.LogNormal(loc=loc, scale=scale, validate_args=True)
samples = self.evaluate(dist.sample(6000, seed=test_util.test_seed()))
self.assertAllClose(np.mean(samples),
self.evaluate(dist.mean()),
atol=0.1)
self.assertAllClose(np.std(samples),
self.evaluate(dist.stddev()),
atol=0.1)
def test_broadcast_batch_shapes(self):
seed = test_util.test_seed(sampler_type='stateless')
batch_shape = [3, 1, 4]
partial_batch_shape = [2, 1]
expected_broadcast_batch_shape = [3, 2, 4]
# Build a model where parameters have different batch shapes.
partial_batch_loc = self._build_placeholder(
np.random.randn(*partial_batch_shape))
full_batch_loc = self._build_placeholder(
np.random.randn(*batch_shape))
partial_scale_prior = tfd.LogNormal(
loc=partial_batch_loc, scale=tf.ones_like(partial_batch_loc))
full_scale_prior = tfd.LogNormal(
loc=full_batch_loc, scale=tf.ones_like(full_batch_loc))
loc_prior = tfd.Normal(loc=partial_batch_loc,
scale=tf.ones_like(partial_batch_loc))
linear_trend = LocalLinearTrend(level_scale_prior=full_scale_prior,
slope_scale_prior=full_scale_prior,
initial_level_prior=loc_prior,
initial_slope_prior=loc_prior)
seasonal = Seasonal(num_seasons=3,
drift_scale_prior=partial_scale_prior,
initial_effect_prior=loc_prior)
model = Sum([linear_trend, seasonal],
observation_noise_scale_prior=partial_scale_prior)
param_samples = [p.prior.sample(seed=seed) for p in model.parameters]
ssm = model.make_state_space_model(num_timesteps=2,
param_vals=param_samples)
# Test that the model's batch shape matches the SSM's batch shape,
# and that they both match the expected broadcast shape.
self.assertAllEqual(model.batch_shape, ssm.batch_shape)
(model_batch_shape_tensor_,
ssm_batch_shape_tensor_) = self.evaluate((model.batch_shape_tensor(),
ssm.batch_shape_tensor()))
self.assertAllEqual(model_batch_shape_tensor_, ssm_batch_shape_tensor_)
self.assertAllEqual(model_batch_shape_tensor_,
expected_broadcast_batch_shape)
def testLogNormalPDF(self):
loc, scale = 1.5, 0.4
dist = tfd.LogNormal(loc=loc, scale=scale, validate_args=True)
x = np.array([1e-4, 1.0, 2.0], dtype=np.float32)
log_pdf = dist.log_prob(x)
analytical_log_pdf = -np.log(x * scale * np.sqrt(2 * np.pi)) - (
np.log(x) - loc)**2 / (2. * scale**2)
self.assertAllClose(self.evaluate(log_pdf), analytical_log_pdf)
def test_simple_regression_correctness(self):
# Verify that optimizing a simple linear regression by gradient descent
# recovers the known-correct weights.
batch_shape = [4, 3]
num_timesteps = 10
num_features = 2
design_matrix = self._build_placeholder(
np.random.randn(*(batch_shape + [num_timesteps, num_features])))
true_weights = self._build_placeholder([4., -3.])
predicted_time_series = tf.linalg.matmul(design_matrix,
true_weights[..., tf.newaxis])
linear_regression = LinearRegression(
design_matrix=design_matrix,
weights_prior=tfd.Independent(tfd.Cauchy(
loc=self._build_placeholder(np.zeros([num_features])),
scale=self._build_placeholder(np.ones([num_features]))),
reinterpreted_batch_ndims=1))
observation_noise_scale_prior = tfd.LogNormal(
loc=self._build_placeholder(-2),
scale=self._build_placeholder(0.1))
model = Sum(
components=[linear_regression],
observation_noise_scale_prior=observation_noise_scale_prior)
learnable_weights = tf.Variable(
tf.zeros([num_features], dtype=true_weights.dtype))
def build_loss():
learnable_ssm = model.make_state_space_model(
num_timesteps=num_timesteps,
param_vals={
"LinearRegression/_weights": learnable_weights,
"observation_noise_scale":
observation_noise_scale_prior.mode()
})
return -learnable_ssm.log_prob(predicted_time_series)
# We provide graph- and eager-mode optimization for TF 2.0 compatibility.
num_train_steps = 80
optimizer = tf1.train.AdamOptimizer(learning_rate=0.1)
if tf.executing_eagerly():
for _ in range(num_train_steps):
optimizer.minimize(build_loss)
else:
train_op = optimizer.minimize(build_loss())
self.evaluate(tf1.global_variables_initializer())
for _ in range(num_train_steps):
_ = self.evaluate(train_op)
self.assertAllClose(*self.evaluate((true_weights, learnable_weights)),
atol=0.2)
def test_get_mean_field_approximation_tree(
flat_tree_test_data: TreeTestData, with_init_loc: bool
):
test_tree = data_to_tensor_tree(flat_tree_test_data)
taxon_count = test_tree.taxon_count
tree_name = "tree_dist_name"
init_loc: tp.Optional[tp.Dict[str, object]]
if with_init_loc:
init_loc = dict(tree=test_tree)
else:
init_loc = None
model = tfd.JointDistributionNamed(
dict(
pop_size=tfd.LogNormal(_constant(0.0), _constant(1.0)),
tree=lambda pop_size: ConstantCoalescent(
taxon_count, pop_size, test_tree.sampling_times, tree_name=tree_name
),
obs=lambda tree: tfd.Normal(
_constant(0.0), tf.reduce_sum(tree.branch_lengths)
),
)
)
obs = _constant([10.0])
pinned = model.experimental_pin(obs=obs)
approximation = get_fixed_topology_mean_field_approximation(
pinned,
dtype=DEFAULT_FLOAT_DTYPE_TF,
topology_pins={tree_name: test_tree.topology},
init_loc=init_loc,
)
sample = approximation.sample()
assert (
tf.reduce_all(
sample["tree"].topology.parent_indices == test_tree.topology.parent_indices
)
.numpy()
.item()
)
assert_allclose(
sample["tree"].sampling_times.numpy(), test_tree.sampling_times.numpy()
)
model_log_prob = pinned.unnormalized_log_prob(sample)
approx_log_prob = approximation.log_prob(sample)
assert np.isfinite(model_log_prob.numpy())
assert np.isfinite(approx_log_prob.numpy())
def testLogNormalStats(self):
loc = np.float32([3., 1.5])
scale = np.float32([0.4, 1.1])
dist = tfd.LogNormal(loc=loc, scale=scale, validate_args=True)
self.assertAllClose(self.evaluate(dist.mean()),
np.exp(loc + scale**2 / 2))
self.assertAllClose(self.evaluate(dist.variance()),
(np.exp(scale**2) - 1) * np.exp(2 * loc + scale**2))
self.assertAllClose(self.evaluate(dist.stddev()),
np.sqrt(self.evaluate(dist.variance())))
self.assertAllClose(self.evaluate(dist.mode()),
np.exp(loc - scale**2))
self.assertAllClose(self.evaluate(dist.entropy()),
np.log(scale * np.exp(loc + 0.5) * np.sqrt(2 * np.pi)))
def test_nested_partial_value(self, sample_fn):
innermost = tfd.JointDistributionNamed({
'a':
tfd.Exponential(1.),
'b':
lambda a: tfd.Sample(tfd.LogNormal(a, a), [5]),
})
inner = tfd.JointDistributionNamed({
'c': tfd.Exponential(1.),
'd': innermost,
})
outer = tfd.JointDistributionNamed({
'e': tfd.Exponential(1.),
'f': inner,
})
seed = test_util.test_seed(sampler_type='stateless')
true_xs = outer.sample(seed=seed)
def _update(dict_, **kwargs):
dict_.copy().update(**kwargs)
return dict_
# These asserts work because we advance the stateless seed inside the model
# whether or not a sample is actually generated.
partial_xs = _update(true_xs, f=None)
xs = sample_fn(outer, value=partial_xs, seed=seed)
self.assertAllCloseNested(true_xs, xs)
partial_xs = _update(true_xs, e=None)
xs = sample_fn(outer, value=partial_xs, seed=seed)
self.assertAllCloseNested(true_xs, xs)
partial_xs = _update(true_xs, f=_update(true_xs['f'], d=None))
xs = sample_fn(outer, value=partial_xs, seed=seed)
self.assertAllCloseNested(true_xs, xs)
partial_xs = _update(true_xs,
f=_update(true_xs['f'],
d=_update(true_xs['f']['d'], a=None)))
xs = sample_fn(outer, value=partial_xs, seed=seed)
self.assertAllCloseNested(true_xs, xs)
def test_get_mean_field_approximation():
sample_size = 3
model = tfd.JointDistributionNamed(
dict(
a=tfd.Normal(_constant(0.0), _constant(1.0)),
b=lambda a: tfd.Sample(tfd.LogNormal(a, _constant(1.0)), sample_size),
obs=lambda b: tfd.Independent(
tfd.Normal(b, _constant(1.0)), reinterpreted_batch_ndims=1
),
)
)
obs = _constant([-1.1, 2.1, 0.1])
pinned = model.experimental_pin(obs=obs)
approximation = get_mean_field_approximation(
pinned, init_loc=dict(a=_constant(0.1)), dtype=DEFAULT_FLOAT_DTYPE_TF
)
sample = approximation.sample()
model_log_prob = pinned.unnormalized_log_prob(sample)
approx_log_prob = approximation.log_prob(sample)
assert np.isfinite(model_log_prob.numpy())
assert np.isfinite(approx_log_prob.numpy())
def test_kahan_precision(self, jit=False):
maybe_jit = lambda f: f
if jit:
self.skip_if_no_xla()
maybe_jit = tf.function(jit_compile=True)
n = 2_500
stream = test_util.test_seed_stream()
samps = tfd.Normal(0, 1).sample(n, seed=stream())
scale = tfd.LogNormal(0, .2).sample([7, 1], seed=stream())
mvn = tfd.MultivariateNormalTriL(
loc=tf.zeros([n]), scale_tril=tf.linalg.diag(tf.zeros([n]) + scale),
experimental_use_kahan_sum=True)
mvn64 = tfd.MultivariateNormalTriL(
loc=tf.zeros([n], dtype=tf.float64),
scale_tril=tf.linalg.diag(
tf.zeros([n], dtype=tf.float64) + tf.cast(scale, tf.float64)))
lp = maybe_jit(mvn.log_prob)(samps)
lp64 = mvn64.log_prob(tf.cast(samps, tf.float64))
lp, lp64 = self.evaluate((tf.cast(lp, tf.float64), lp64))
# With fast-math off, without Kahan fails 15-100%, max-abs-error ~.006
self.assertAllClose(lp64, lp, rtol=0, atol=.0008)
def __init__(self,
num_seasons,
num_steps_per_season=1,
drift_scale_prior=None,
initial_effect_prior=None,
constrain_mean_effect_to_zero=True,
observed_time_series=None,
name=None):
"""Specify a seasonal effects model.
Args:
num_seasons: Scalar Python `int` number of seasons.
num_steps_per_season: Python `int` number of steps in each
season. This may be either a scalar (shape `[]`), in which case all
seasons have the same length, or a NumPy array of shape `[num_seasons]`,
in which seasons have different length, but remain constant around
different cycles, or a NumPy array of shape `[num_cycles, num_seasons]`,
in which num_steps_per_season for each season also varies in different
cycle (e.g., a 4 years cycle with leap day).
Default value: 1.
drift_scale_prior: optional `tfd.Distribution` instance specifying a prior
on the `drift_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
initial_effect_prior: optional `tfd.Distribution` instance specifying a
normal prior on the initial effect of each season. This may be either
a scalar `tfd.Normal` prior, in which case it applies independently to
every season, or it may be multivariate normal (e.g.,
`tfd.MultivariateNormalDiag`) with event shape `[num_seasons]`, in
which case it specifies a joint prior across all seasons. If `None`, a
heuristic default prior is constructed based on the provided
`observed_time_series`.
Default value: `None`.
constrain_mean_effect_to_zero: if `True`, use a model parameterization
that constrains the mean effect across all seasons to be zero. This
constraint is generally helpful in identifying the contributions of
different model components and can lead to more interpretable
posterior decompositions. It may be undesirable if you plan to directly
examine the latent space of the underlying state space model.
Default value: `True`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series.
Any priors not explicitly set will be given default values according to
the scale of the observed time series (or batch of time series). May
optionally be an instance of `tfp.sts.MaskedTimeSeries`, which includes
a mask `Tensor` to specify timesteps with missing observations.
Default value: `None`.
name: the name of this model component.
Default value: 'Seasonal'.
"""
with tf.compat.v1.name_scope(name,
'Seasonal',
values=[observed_time_series]) as name:
_, observed_stddev, observed_initial = (
sts_util.empirical_statistics(observed_time_series)
if observed_time_series is not None else (0., 1., 0.))
# Heuristic default priors. Overriding these may dramatically
# change inference performance and results.
if drift_scale_prior is None:
drift_scale_prior = tfd.LogNormal(loc=tf.math.log(
.01 * observed_stddev),
scale=3.)
if initial_effect_prior is None:
initial_effect_prior = tfd.Normal(
loc=observed_initial,
scale=tf.abs(observed_initial) + observed_stddev)
dtype = tf.debugging.assert_same_float_dtype(
[drift_scale_prior, initial_effect_prior])
if isinstance(initial_effect_prior, tfd.Normal):
initial_state_prior = tfd.MultivariateNormalDiag(
loc=tf.stack([initial_effect_prior.mean()] * num_seasons,
axis=-1),
scale_diag=tf.stack([initial_effect_prior.stddev()] *
num_seasons,
axis=-1))
else:
initial_state_prior = initial_effect_prior
if constrain_mean_effect_to_zero:
# Transform the prior to the residual parameterization used by
# `ConstrainedSeasonalStateSpaceModel`, imposing a zero-sum constraint.
# This doesn't change the marginal prior on individual effects, but
# does introduce dependence between the effects.
(effects_to_residuals,
_) = build_effects_to_residuals_matrix(num_seasons,
dtype=dtype)
effects_to_residuals_linop = tf.linalg.LinearOperatorFullMatrix(
effects_to_residuals
) # Use linop so that matmul broadcasts.
initial_state_prior_loc = effects_to_residuals_linop.matvec(
initial_state_prior.mean())
initial_state_prior_scale_linop = effects_to_residuals_linop.matmul(
initial_state_prior.scale) # returns LinearOperator
initial_state_prior = tfd.MultivariateNormalFullCovariance(
loc=initial_state_prior_loc,
covariance_matrix=initial_state_prior_scale_linop.matmul(
initial_state_prior_scale_linop.to_dense(),
adjoint_arg=True))
self._constrain_mean_effect_to_zero = constrain_mean_effect_to_zero
self._initial_state_prior = initial_state_prior
self._num_seasons = num_seasons
self._num_steps_per_season = num_steps_per_season
super(Seasonal, self).__init__(
parameters=[
Parameter('drift_scale', drift_scale_prior,
tfb.Softplus()),
],
latent_size=(num_seasons -
1 if self.constrain_mean_effect_to_zero else
num_seasons),
name=name)
def __init__(self,
num_seasons,
num_steps_per_season=1,
drift_scale_prior=None,
initial_effect_prior=None,
observed_time_series=None,
name=None):
"""Specify a seasonal effects model.
Args:
num_seasons: Scalar Python `int` number of seasons.
num_steps_per_season: Python `int` number of steps in each
season. This may be either a scalar (shape `[]`), in which case all
seasons have the same length, or a NumPy array of shape `[num_seasons]`.
Default value: 1.
drift_scale_prior: optional `tfd.Distribution` instance specifying a prior
on the `drift_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
initial_effect_prior: optional `tfd.Distribution` instance specifying a
normal prior on the initial effect of each season. This may be either
a scalar `tfd.Normal` prior, in which case it applies independently to
every season, or it may be multivariate normal (e.g.,
`tfd.MultivariateNormalDiag`) with event shape `[num_seasons]`, in
which case it specifies a joint prior across all seasons. If `None`, a
heuristic default prior is constructed based on the provided
`observed_time_series`.
Default value: `None`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series.
Any priors not explicitly set will be given default values according to
the scale of the observed time series (or batch of time series).
Default value: `None`.
name: the name of this model component.
Default value: 'Seasonal'.
"""
with tf.compat.v1.name_scope(name,
'Seasonal',
values=[observed_time_series]) as name:
observed_stddev, observed_initial = (
sts_util.empirical_statistics(observed_time_series)
if observed_time_series is not None else (1., 0.))
# Heuristic default priors. Overriding these may dramatically
# change inference performance and results.
if drift_scale_prior is None:
drift_scale_prior = tfd.LogNormal(loc=tf.math.log(
.01 * observed_stddev),
scale=3.)
if initial_effect_prior is None:
initial_effect_prior = tfd.Normal(
loc=observed_initial,
scale=tf.abs(observed_initial) + observed_stddev)
self._num_seasons = num_seasons
self._num_steps_per_season = num_steps_per_season
tf.debugging.assert_same_float_dtype(
[drift_scale_prior, initial_effect_prior])
if isinstance(initial_effect_prior, tfd.Normal):
self._initial_state_prior = tfd.MultivariateNormalDiag(
loc=tf.stack([initial_effect_prior.mean()] * num_seasons,
axis=-1),
scale_diag=tf.stack([initial_effect_prior.stddev()] *
num_seasons,
axis=-1))
else:
self._initial_state_prior = initial_effect_prior
super(Seasonal, self).__init__(parameters=[
Parameter('drift_scale', drift_scale_prior, tfb.Softplus()),
],
latent_size=num_seasons,
name=name)
def __init__(self,
components,
constant_offset=None,
observation_noise_scale_prior=None,
observed_time_series=None,
name=None):
"""Specify a structural time series model representing a sum of components.
Args:
components: Python `list` of one or more StructuralTimeSeries instances.
These must have unique names.
constant_offset: optional `float` `Tensor` of shape broadcasting to
`concat([batch_shape, [num_timesteps]]`) specifying a constant value
added to the sum of outputs from the component models.
This allows the components to model the shifted series
`observed_time_series - constant_offset`. If `None`, this is set to the
mean of the provided `observed_time_series`.
Default value: `None`.
observation_noise_scale_prior: optional `tfd.Distribution` instance
specifying a prior on `observation_noise_scale`. If `None`, a heuristic
default prior is constructed based on the provided
`observed_time_series`.
Default value: `None`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series. This is
used to set the constant offset, if not provided, and to construct a
default heuristic `observation_noise_scale_prior` if not provided. May
optionally be an instance of `tfp.sts.MaskedTimeSeries`, which includes
a mask `Tensor` to specify timesteps with missing observations.
Default value: `None`.
name: Python `str` name of this model component; used as `name_scope`
for ops created by this class.
Default value: 'Sum'.
Raises:
ValueError: if components do not have unique names.
"""
with tf.name_scope(name or 'Sum') as name:
if observed_time_series is not None:
observed_mean, observed_stddev, _ = (
sts_util.empirical_statistics(observed_time_series))
else:
observed_mean, observed_stddev = 0., 1.
if observation_noise_scale_prior is None:
observation_noise_scale_prior = tfd.LogNormal(loc=tf.math.log(
.01 * observed_stddev),
scale=2.)
dtype = dtype_util.common_dtype([
constant_offset, observation_noise_scale_prior, observed_mean,
observed_stddev
])
# Ensure that offsets have canonical shape `[..., num_timesteps]`.
if constant_offset is None:
constant_offset = tf.convert_to_tensor(observed_mean,
dtype=dtype)[...,
tf.newaxis]
constant_offset *= tf.ones([1], dtype=dtype)
# Check that components have unique names, to ensure that inherited
# parameters will be assigned unique names.
component_names = [c.name for c in components]
if len(component_names) != len(set(component_names)):
raise ValueError(
'Components must have unique names: {}'.format(
component_names))
components_by_name = collections.OrderedDict([(c.name, c)
for c in components])
# Build parameters list for the combined model, by inheriting parameters
# from the component models in canonical order.
parameters = [
Parameter(
'observation_noise_scale', observation_noise_scale_prior,
tfb.Chain(
[tfb.Scale(scale=observed_stddev),
tfb.Softplus()]))
]
for component in components:
for parameter in component.parameters:
parameters.append(
Parameter(name='{}_{}'.format(component.name,
parameter.name),
prior=parameter.prior,
bijector=parameter.bijector))
self._components = components
self._components_by_name = components_by_name
self._constant_offset = constant_offset
super(Sum, self).__init__(parameters=parameters,
latent_size=sum([
component.latent_size
for component in components
]),
name=name)
def __init__(self,
order,
coefficients_prior=None,
level_scale_prior=None,
initial_state_prior=None,
coefficient_constraining_bijector=None,
observed_time_series=None,
name=None):
"""Specify an autoregressive model.
Args:
order: scalar Python positive `int` specifying the number of past
timesteps to regress on.
coefficients_prior: optional `tfd.Distribution` instance specifying a
prior on the `coefficients` parameter. If `None`, a default standard
normal (`tfd.MultivariateNormalDiag(scale_diag=tf.ones([order]))`) prior
is used.
Default value: `None`.
level_scale_prior: optional `tfd.Distribution` instance specifying a prior
on the `level_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
initial_state_prior: optional `tfd.Distribution` instance specifying a
prior on the initial state, corresponding to the values of the process
at a set of size `order` of imagined timesteps before the initial step.
If `None`, a heuristic default prior is constructed based on the
provided `observed_time_series`.
Default value: `None`.
coefficient_constraining_bijector: optional `tfb.Bijector` instance
representing a constraining mapping for the autoregressive coefficients.
For example, `tfb.Tanh()` constrains the coefficients to lie in
`(-1, 1)`, while `tfb.Softplus()` constrains them to be positive, and
`tfb.Identity()` implies no constraint. If `None`, the default behavior
constrains the coefficients to lie in `(-1, 1)` using a `Tanh` bijector.
Default value: `None`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series.
Any priors not explicitly set will be given default values according to
the scale of the observed time series (or batch of time series). May
optionally be an instance of `tfp.sts.MaskedTimeSeries`, which includes
a mask `Tensor` to specify timesteps with missing observations.
Default value: `None`.
name: the name of this model component.
Default value: 'Autoregressive'.
"""
with tf.name_scope(name or 'Autoregressive') as name:
masked_time_series = None
if observed_time_series is not None:
masked_time_series = (
sts_util.canonicalize_observed_time_series_with_mask(
observed_time_series))
dtype = dtype_util.common_dtype(
[(masked_time_series.time_series
if masked_time_series is not None else None),
coefficients_prior,
level_scale_prior,
initial_state_prior], dtype_hint=tf.float32)
if observed_time_series is not None:
_, observed_stddev, observed_initial = sts_util.empirical_statistics(
masked_time_series)
else:
observed_stddev, observed_initial = (
tf.convert_to_tensor(value=1., dtype=dtype),
tf.convert_to_tensor(value=0., dtype=dtype))
batch_ones = tf.ones(tf.concat([
tf.shape(observed_initial), # Batch shape
[order]], axis=0), dtype=dtype)
# Heuristic default priors. Overriding these may dramatically
# change inference performance and results.
if coefficients_prior is None:
coefficients_prior = tfd.MultivariateNormalDiag(
scale_diag=batch_ones)
if level_scale_prior is None:
level_scale_prior = tfd.LogNormal(
loc=tf.math.log(0.05 * observed_stddev), scale=3.)
if (coefficients_prior.event_shape.is_fully_defined() and
order != coefficients_prior.event_shape[0]):
raise ValueError("Prior dimension {} doesn't match order {}.".format(
coefficients_prior.event_shape[0], order))
if initial_state_prior is None:
initial_state_prior = tfd.MultivariateNormalDiag(
loc=observed_initial[..., tf.newaxis] * batch_ones,
scale_diag=(tf.abs(observed_initial) +
observed_stddev)[..., tf.newaxis] * batch_ones)
self._order = order
self._coefficients_prior = coefficients_prior
self._level_scale_prior = level_scale_prior
self._initial_state_prior = initial_state_prior
if coefficient_constraining_bijector is None:
coefficient_constraining_bijector = tfb.Tanh()
super(Autoregressive, self).__init__(
parameters=[
Parameter('coefficients',
coefficients_prior,
coefficient_constraining_bijector),
Parameter('level_scale', level_scale_prior,
tfb.Chain([tfb.AffineScalar(scale=observed_stddev),
tfb.Softplus()]))
],
latent_size=order,
name=name)
def __init__(self,
level_scale_prior=None,
slope_mean_prior=None,
slope_scale_prior=None,
autoregressive_coef_prior=None,
initial_level_prior=None,
initial_slope_prior=None,
observed_time_series=None,
constrain_ar_coef_stationary=True,
constrain_ar_coef_positive=False,
name=None):
"""Specify a semi-local linear trend model.
Args:
level_scale_prior: optional `tfd.Distribution` instance specifying a prior
on the `level_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
slope_mean_prior: optional `tfd.Distribution` instance specifying a prior
on the `slope_mean` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
slope_scale_prior: optional `tfd.Distribution` instance specifying a prior
on the `slope_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
autoregressive_coef_prior: optional `tfd.Distribution` instance specifying
a prior on the `autoregressive_coef` parameter. If `None`, the default
prior is a standard `Normal(0., 1.)`. Note that the prior may be
implicitly truncated by `constrain_ar_coef_stationary` and/or
`constrain_ar_coef_positive`.
Default value: `None`.
initial_level_prior: optional `tfd.Distribution` instance specifying a
prior on the initial level. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
initial_slope_prior: optional `tfd.Distribution` instance specifying a
prior on the initial slope. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series.
Any priors not explicitly set will be given default values according to
the scale of the observed time series (or batch of time series). May
optionally be an instance of `tfp.sts.MaskedTimeSeries`, which includes
a mask `Tensor` to specify timesteps with missing observations.
Default value: `None`.
constrain_ar_coef_stationary: if `True`, perform inference using a
parameterization that restricts `autoregressive_coef` to the interval
`(-1, 1)`, or `(0, 1)` if `force_positive_ar_coef` is also `True`,
corresponding to stationary processes. This will implicitly truncates
the support of `autoregressive_coef_prior`.
Default value: `True`.
constrain_ar_coef_positive: if `True`, perform inference using a
parameterization that restricts `autoregressive_coef` to be positive,
or in `(0, 1)` if `constrain_ar_coef_stationary` is also `True`. This
will implicitly truncate the support of `autoregressive_coef_prior`.
Default value: `False`.
name: the name of this model component.
Default value: 'SemiLocalLinearTrend'.
"""
with tf.name_scope(name or 'SemiLocalLinearTrend') as name:
if observed_time_series is not None:
_, observed_stddev, observed_initial = sts_util.empirical_statistics(
observed_time_series)
else:
observed_stddev, observed_initial = 1., 0.
# Heuristic default priors. Overriding these may dramatically
# change inference performance and results.
if level_scale_prior is None:
level_scale_prior = tfd.LogNormal(loc=tf.math.log(
.01 * observed_stddev),
scale=2.)
if slope_mean_prior is None:
slope_mean_prior = tfd.Normal(loc=0., scale=observed_stddev)
if slope_scale_prior is None:
slope_scale_prior = tfd.LogNormal(loc=tf.math.log(
.01 * observed_stddev),
scale=2.)
if autoregressive_coef_prior is None:
autoregressive_coef_prior = tfd.Normal(
loc=0., scale=tf.ones_like(observed_initial))
if initial_level_prior is None:
initial_level_prior = tfd.Normal(
loc=observed_initial,
scale=tf.abs(observed_initial) + observed_stddev)
if initial_slope_prior is None:
initial_slope_prior = tfd.Normal(loc=0., scale=observed_stddev)
self._initial_state_prior = tfd.MultivariateNormalDiag(
loc=tf.stack(
[initial_level_prior.mean(),
initial_slope_prior.mean()],
axis=-1),
scale_diag=tf.stack([
initial_level_prior.stddev(),
initial_slope_prior.stddev()
],
axis=-1))
# Constrain the support of the autoregressive coefficient.
if constrain_ar_coef_stationary and constrain_ar_coef_positive:
autoregressive_coef_bijector = tfb.Sigmoid(
) # support in (0, 1)
elif constrain_ar_coef_positive:
autoregressive_coef_bijector = tfb.Softplus(
) # support in (0, infty)
elif constrain_ar_coef_stationary:
autoregressive_coef_bijector = tfb.Tanh() # support in (-1, 1)
else:
autoregressive_coef_bijector = tfb.Identity() # unconstrained
stddev_preconditioner = tfb.Scale(scale=observed_stddev)
scaled_softplus = tfb.Chain(
[stddev_preconditioner, tfb.Softplus()])
super(SemiLocalLinearTrend, self).__init__(parameters=[
Parameter('level_scale', level_scale_prior, scaled_softplus),
Parameter('slope_mean', slope_mean_prior,
stddev_preconditioner),
Parameter('slope_scale', slope_scale_prior, scaled_softplus),
Parameter('autoregressive_coef', autoregressive_coef_prior,
autoregressive_coef_bijector),
],
latent_size=2,
name=name)
def testGradientWorksDespiteBijectorCaching(self):
x = tf.constant(2.)
fn_result, grads = util.maybe_call_fn_and_grads(
lambda x_: tfd.LogNormal(loc=0., scale=1.).log_prob(x_), x)
self.assertAllEqual(False, fn_result is None)
self.assertAllEqual([False], [g is None for g in grads])
def __init__(self,
components,
observation_noise_scale_prior=None,
observed_time_series=None,
name=None):
"""Specify a structural time series model representing a sum of components.
Args:
components: Python `list` of one or more StructuralTimeSeries instances.
These must have unique names.
observation_noise_scale_prior: optional `tfd.Distribution` instance
specifying a prior on `observation_noise_scale`. If `None`, a heuristic
default prior is constructed based on the provided
`observed_time_series`.
Default value: `None`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series. This is
used only if `observation_noise_scale_prior` is not provided, to
construct a default heuristic prior.
Default value: `None`.
name: Python `str` name of this model component; used as `name_scope`
for ops created by this class.
Default value: 'Sum'.
Raises:
ValueError: if components do not have unique names.
"""
with tf.compat.v1.name_scope(
name, 'Sum', values=[observed_time_series]) as name:
if observation_noise_scale_prior is None:
observed_stddev, _ = (
sts_util.empirical_statistics(observed_time_series)
if observed_time_series is not None else (1., 0.))
observation_noise_scale_prior = tfd.LogNormal(
loc=tf.math.log(.01 * observed_stddev), scale=2.)
# Check that components have unique names, to ensure that inherited
# parameters will be assigned unique names.
component_names = [c.name for c in components]
if len(component_names) != len(set(component_names)):
raise ValueError(
'Components must have unique names: {}'.format(component_names))
components_by_name = collections.OrderedDict(
[(c.name, c) for c in components])
# Build parameters list for the combined model, by inheriting parameters
# from the component models in canonical order.
parameters = [
Parameter('observation_noise_scale', observation_noise_scale_prior,
tfb.Softplus()),
] + [Parameter(name='{}_{}'.format(component.name, parameter.name),
prior=parameter.prior,
bijector=parameter.bijector)
for component in components for parameter in component.parameters]
self._components = components
self._components_by_name = components_by_name
super(Sum, self).__init__(
parameters=parameters,
latent_size=sum(
[component.latent_size for component in components]),
name=name)
def __init__(self,
level_scale_prior=None,
initial_level_prior=None,
observed_time_series=None,
name=None):
"""Specify a local level model.
Args:
level_scale_prior: optional `tfd.Distribution` instance specifying a prior
on the `level_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
initial_level_prior: optional `tfd.Distribution` instance specifying a
prior on the initial level. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series.
Any priors not explicitly set will be given default values according to
the scale of the observed time series (or batch of time series). May
optionally be an instance of `tfp.sts.MaskedTimeSeries`, which includes
a mask `Tensor` to specify timesteps with missing observations.
Default value: `None`.
name: the name of this model component.
Default value: 'LocalLevel'.
"""
with tf.name_scope(name or 'LocalLevel') as name:
dtype = dtype_util.common_dtype(
[level_scale_prior, initial_level_prior])
if observed_time_series is not None:
_, observed_stddev, observed_initial = (
sts_util.empirical_statistics(observed_time_series))
else:
observed_stddev, observed_initial = (tf.convert_to_tensor(
value=1.,
dtype=dtype), tf.convert_to_tensor(value=0., dtype=dtype))
# Heuristic default priors. Overriding these may dramatically
# change inference performance and results.
if level_scale_prior is None:
level_scale_prior = tfd.LogNormal(loc=tf.math.log(
.05 * observed_stddev),
scale=3.,
name='level_scale_prior')
if initial_level_prior is None:
self._initial_state_prior = tfd.MultivariateNormalDiag(
loc=observed_initial[..., tf.newaxis],
scale_diag=(tf.abs(observed_initial) +
observed_stddev)[..., tf.newaxis],
name='initial_level_prior')
else:
self._initial_state_prior = tfd.MultivariateNormalDiag(
loc=initial_level_prior.mean()[..., tf.newaxis],
scale_diag=initial_level_prior.stddev()[..., tf.newaxis])
super(LocalLevel, self).__init__(parameters=[
Parameter(
'level_scale', level_scale_prior,
tfb.Chain([
tfb.AffineScalar(scale=observed_stddev),
tfb.Softplus()
])),
],
latent_size=1,
name=name)
def testSupportBijectorOutsideRange(self):
dist = tfd.LogNormal(loc=1., scale=2., validate_args=True)
with self.assertRaisesOpError('must be greater than 0'):
dist._experimental_default_event_space_bijector().inverse(
[-4.2, -1e-6, -1.3])
def __init__(self,
design_matrix,
drift_scale_prior=None,
initial_weights_prior=None,
observed_time_series=None,
name=None):
"""Specify a dynamic linear regression.
Args:
design_matrix: float `Tensor` of shape `concat([batch_shape,
[num_timesteps, num_features]])`.
drift_scale_prior: instance of `tfd.Distribution` specifying a prior on
the `drift_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
initial_weights_prior: instance of `tfd.MultivariateNormal` representing
the prior distribution on the latent states (the regression weights).
Must have event shape `[num_features]`. If `None`, a weakly-informative
Normal(0., 10.) prior is used.
Default value: `None`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series. Any `NaN`s
are interpreted as missing observations; missingness may be also be
explicitly specified by passing a `tfp.sts.MaskedTimeSeries` instance.
Any priors not explicitly set will be given default values according to
the scale of the observed time series (or batch of time series).
Default value: `None`.
name: Python `str` for the name of this component.
Default value: 'DynamicLinearRegression'.
"""
init_parameters = dict(locals())
with tf.name_scope(name or 'DynamicLinearRegression') as name:
dtype = dtype_util.common_dtype(
[design_matrix, drift_scale_prior, initial_weights_prior])
num_features = prefer_static.shape(design_matrix)[-1]
# Default to a weakly-informative Normal(0., 10.) for the initital state
if initial_weights_prior is None:
initial_weights_prior = tfd.MultivariateNormalDiag(
scale_diag=10. * tf.ones([num_features], dtype=dtype))
# Heuristic default priors. Overriding these may dramatically
# change inference performance and results.
if drift_scale_prior is None:
if observed_time_series is None:
observed_stddev = tf.constant(1.0, dtype=dtype)
else:
_, observed_stddev, _ = sts_util.empirical_statistics(
observed_time_series)
drift_scale_prior = tfd.LogNormal(loc=tf.math.log(
.05 * observed_stddev),
scale=3.,
name='drift_scale_prior')
self._initial_state_prior = initial_weights_prior
self._design_matrix = design_matrix
super(DynamicLinearRegression,
self).__init__(parameters=[
Parameter(
'drift_scale', drift_scale_prior,
tfb.Chain([
tfb.Scale(scale=observed_stddev),
tfb.Softplus()
]))
],
latent_size=num_features,
init_parameters=init_parameters,
name=name)
def __init__(self,
level_scale_prior=None,
slope_scale_prior=None,
initial_level_prior=None,
initial_slope_prior=None,
observed_time_series=None,
name=None):
"""Specify a local linear trend model.
Args:
level_scale_prior: optional `tfd.Distribution` instance specifying a prior
on the `level_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
slope_scale_prior: optional `tfd.Distribution` instance specifying a prior
on the `slope_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
initial_level_prior: optional `tfd.Distribution` instance specifying a
prior on the initial level. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
initial_slope_prior: optional `tfd.Distribution` instance specifying a
prior on the initial slope. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series.
Any priors not explicitly set will be given default values according to
the scale of the observed time series (or batch of time series).
Default value: `None`.
name: the name of this model component.
Default value: 'LocalLinearTrend'.
"""
with tf.name_scope(
name, 'LocalLinearTrend', values=[observed_time_series]) as name:
observed_stddev, observed_initial = (
sts_util.empirical_statistics(observed_time_series)
if observed_time_series is not None else (1., 0.))
# Heuristic default priors. Overriding these may dramatically
# change inference performance and results.
if level_scale_prior is None:
level_scale_prior = tfd.LogNormal(
loc=tf.log(.05 * observed_stddev),
scale=3.,
name='level_scale_prior')
if slope_scale_prior is None:
slope_scale_prior = tfd.LogNormal(
loc=tf.log(.05 * observed_stddev),
scale=3.,
name='slope_scale_prior')
if initial_level_prior is None:
initial_level_prior = tfd.Normal(
loc=observed_initial,
scale=tf.abs(observed_initial) + observed_stddev,
name='initial_level_prior')
if initial_slope_prior is None:
initial_slope_prior = tfd.Normal(
loc=0., scale=observed_stddev, name='initial_slope_prior')
tf.assert_same_float_dtype([
level_scale_prior, slope_scale_prior, initial_level_prior,
initial_slope_prior
])
self._initial_state_prior = tfd.MultivariateNormalDiag(
loc=tf.stack(
[initial_level_prior.mean(),
initial_slope_prior.mean()
], axis=-1),
scale_diag=tf.stack([
initial_level_prior.stddev(),
initial_slope_prior.stddev()
], axis=-1))
super(LocalLinearTrend, self).__init__(
parameters=[
Parameter('level_scale', level_scale_prior, tfb.Softplus()),
Parameter('slope_scale', slope_scale_prior, tfb.Softplus())
],
latent_size=2,
name=name)
def __init__(self,
period,
frequency_multipliers,
allow_drift=True,
drift_scale_prior=None,
initial_state_prior=None,
observed_time_series=None,
name=None):
"""Specify a smooth seasonal effects model.
Args:
period: positive scalar `float` `Tensor` giving the number of timesteps
required for the longest cyclic effect to repeat.
frequency_multipliers: One-dimensional `float` `Tensor` listing the
frequencies (cyclic components) included in the model, as multipliers of
the base/fundamental frequency `2. * pi / period`. Each component is
specified by the number of times it repeats per period, and adds two
latent dimensions to the model. A smooth seasonal model that can
represent any periodic function is given by `frequency_multipliers = [1,
2, ..., floor(period / 2)]`. However, it is often desirable to enforce a
smoothness assumption (and reduce the computational burden) by dropping
some of the higher frequencies.
allow_drift: optional Python `bool` specifying whether the seasonal
effects can drift over time. Setting this to `False`
removes the `drift_scale` parameter from the model. This is
mathematically equivalent to
`drift_scale_prior = tfd.Deterministic(0.)`, but removing drift
directly is preferred because it avoids the use of a degenerate prior.
Default value: `True`.
drift_scale_prior: optional `tfd.Distribution` instance specifying a prior
on the `drift_scale` parameter. If `None`, a heuristic default prior is
constructed based on the provided `observed_time_series`.
Default value: `None`.
initial_state_prior: instance of `tfd.MultivariateNormal` representing
the prior distribution on the latent states. Must have event shape
`[2 * len(frequency_multipliers)]`. If `None`, a heuristic default prior
is constructed based on the provided `observed_time_series`.
observed_time_series: optional `float` `Tensor` of shape
`batch_shape + [T, 1]` (omitting the trailing unit dimension is also
supported when `T > 1`), specifying an observed time series.
Any priors not explicitly set will be given default values according to
the scale of the observed time series (or batch of time series). May
optionally be an instance of `tfp.sts.MaskedTimeSeries`, which includes
a mask `Tensor` to specify timesteps with missing observations.
Default value: `None`.
name: the name of this model component.
Default value: 'SmoothSeasonal'.
"""
with tf.name_scope(name or 'SmoothSeasonal') as name:
_, observed_stddev, observed_initial = (
sts_util.empirical_statistics(observed_time_series)
if observed_time_series is not None else (0., 1., 0.))
latent_size = 2 * static_num_frequencies(frequency_multipliers)
# Heuristic default priors. Overriding these may dramatically
# change inference performance and results.
if drift_scale_prior is None:
drift_scale_prior = tfd.LogNormal(loc=tf.math.log(
.01 * observed_stddev),
scale=3.)
if initial_state_prior is None:
initial_state_scale = (tf.abs(observed_initial) +
observed_stddev)[..., tf.newaxis]
ones = tf.ones([latent_size], dtype=drift_scale_prior.dtype)
initial_state_prior = tfd.MultivariateNormalDiag(
scale_diag=initial_state_scale * ones)
self._initial_state_prior = initial_state_prior
self._period = period
self._frequency_multipliers = frequency_multipliers
parameters = []
if allow_drift:
parameters.append(
Parameter(
'drift_scale', drift_scale_prior,
tfb.Chain([
tfb.AffineScalar(scale=observed_stddev),
tfb.Softplus()
])))
self._allow_drift = allow_drift
super(SmoothSeasonal, self).__init__(parameters=parameters,
latent_size=latent_size,
name=name)
