def _build_sts(self, observed_time_series=None):
max_timesteps = 100
num_features = 3
prior = tfd.Sample(tfd.Laplace(0., 1.), sample_shape=[num_features])
# LinearRegression components don't currently take an `observed_time_series`
# argument, so they can't infer a prior batch shape. This means we have to
# manually set the batch shape expected by the tests.
dtype = np.float32
if observed_time_series is not None:
observed_time_series_tensor, _ = (
sts_util.canonicalize_observed_time_series_with_mask(
observed_time_series))
batch_shape = tf.shape(observed_time_series_tensor)[:-2]
dtype = dtype_util.as_numpy_dtype(
observed_time_series_tensor.dtype)
prior = tfd.Sample(tfd.Laplace(tf.zeros(batch_shape, dtype=dtype),
1.),
sample_shape=[num_features])
regression = LinearRegression(design_matrix=np.random.randn(
max_timesteps, num_features).astype(dtype),
weights_prior=prior)
return Sum(components=[regression],
observed_time_series=observed_time_series)
def _build_sts(self, observed_time_series=None):
first_component = LocalLinearTrend(
observed_time_series=observed_time_series, name='first_component')
second_component = LocalLinearTrend(
observed_time_series=observed_time_series, name='second_component')
return Sum(components=[first_component, second_component],
observed_time_series=observed_time_series)
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 = linear_operator_util.matmul_with_broadcast(
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))
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()
})
loss = -learnable_ssm.log_prob(predicted_time_series)
train_op = tf.train.AdamOptimizer(0.1).minimize(loss)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(80):
_ = sess.run(train_op)
self.assertAllClose(*sess.run((true_weights, learnable_weights)),
atol=0.2)
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 test_broadcast_batch_shapes(self):
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() 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 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 _build_sts(self, observed_time_series=None):
max_timesteps = 100
num_features = 3
# LinearRegression components don't currently take an `observed_time_series`
# argument, so they can't infer a prior batch shape. This means we have to
# manually set the batch shape expected by the tests.
batch_shape = None
if observed_time_series is not None:
observed_time_series_tensor, _ = (
sts_util.canonicalize_observed_time_series_with_mask(
observed_time_series))
batch_shape = tf.shape(observed_time_series_tensor)[:-2]
regression = SparseLinearRegression(design_matrix=np.random.randn(
max_timesteps, num_features).astype(np.float32),
weights_batch_shape=batch_shape)
return Sum(components=[regression],
observed_time_series=observed_time_series)
def _build_sts(self, observed_time_series=None):
max_timesteps = 100
num_features = 3
prior = tfd.Laplace(0., 1.)
# LinearRegression components don't currently take an `observed_time_series`
# argument, so they can't infer a prior batch shape. This means we have to
# manually set the batch shape expected by the tests.
if observed_time_series is not None:
observed_time_series = sts_util.maybe_expand_trailing_dim(
observed_time_series)
batch_shape = observed_time_series.shape[:-2]
prior = tfd.TransformedDistribution(prior, tfb.Identity(),
event_shape=[num_features],
batch_shape=batch_shape)
regression = LinearRegression(
design_matrix=tf.random.normal([max_timesteps, num_features]),
weights_prior=prior)
return Sum(components=[regression],
observed_time_series=observed_time_series)
def _build_sts(self, observed_time_series=None):
max_timesteps = 100
num_features = 3
prior = tfd.Laplace(0., 1.)
# LinearRegression components don't currently take an `observed_time_series`
# argument, so they can't infer a prior batch shape. This means we have to
# manually set the batch shape expected by the tests.
if observed_time_series is not None:
observed_time_series_tensor, _ = (
sts_util.canonicalize_observed_time_series_with_mask(
observed_time_series))
batch_shape = tf.shape(input=observed_time_series_tensor)[:-2]
prior = tfd.TransformedDistribution(prior, tfb.Identity(),
event_shape=[num_features],
batch_shape=batch_shape)
regression = LinearRegression(
design_matrix=np.random.randn(
max_timesteps, num_features).astype(np.float32),
weights_prior=prior)
return Sum(components=[regression],
observed_time_series=observed_time_series)
