def _test_missing_values(self, cut_start, cut_end, offset):
stub_model = StubTimeSeriesModel()
data = self._make_test_data(length=100,
cut_start=cut_start,
cut_end=cut_end,
offset=offset)
input_fn = test_utils.AllWindowInputFn(
input_pipeline.NumpyReader(data), window_size=10)
chainer = state_management.ChainingStateManager(
state_saving_interval=1)
features, _ = input_fn()
stub_model.initialize_graph()
chainer.initialize_graph(model=stub_model)
model_outputs = chainer.define_loss(model=stub_model,
features=features,
mode=estimator_lib.ModeKeys.TRAIN)
with self.cached_session() as session:
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
for _ in range(10):
model_outputs.loss.eval()
returned_loss = model_outputs.loss.eval()
coordinator.request_stop()
coordinator.join()
return returned_loss
def _test_pass_to_next(self, read_offset, step, correct_offset):
stub_model = StubTimeSeriesModel(correct_offset=correct_offset)
data = self._make_test_data(
length=100 + read_offset, cut_start=None, cut_end=None, offset=100.,
step=step)
init_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(
{k: v[:-read_offset] for k, v in data.items()}))
result_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(
{k: v[read_offset:] for k, v in data.items()}))
chainer = state_management.ChainingStateManager(
state_saving_interval=1)
stub_model.initialize_graph()
chainer.initialize_graph(model=stub_model)
init_model_outputs = chainer.define_loss(
model=stub_model, features=init_input_fn()[0],
mode=estimator_lib.ModeKeys.TRAIN)
result_model_outputs = chainer.define_loss(
model=stub_model, features=result_input_fn()[0],
mode=estimator_lib.ModeKeys.TRAIN)
with self.test_session() as session:
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
init_model_outputs.loss.eval()
returned_loss = result_model_outputs.loss.eval()
coordinator.request_stop()
coordinator.join()
return returned_loss
def _test_initialization(self, warmup_iterations, batch_size):
stub_model = StubTimeSeriesModel()
data = self._make_test_data(length=20,
cut_start=None,
cut_end=None,
offset=0.)
if batch_size == -1:
input_fn = test_utils.AllWindowInputFn(
input_pipeline.NumpyReader(data), window_size=10)
else:
input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(data),
window_size=10,
batch_size=batch_size)
chainer = state_management.ChainingStateManager(
state_saving_interval=1)
features, _ = input_fn()
stub_model.initialize_graph()
chainer.initialize_graph(model=stub_model)
model_outputs = chainer.define_loss(model=stub_model,
features=features,
mode=estimator_lib.ModeKeys.TRAIN)
with self.cached_session() as session:
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
for _ in range(warmup_iterations):
# Warm up saved state
model_outputs.loss.eval()
outputs = model_outputs.loss.eval()
coordinator.request_stop()
coordinator.join()
return outputs
def chained_model_outputs(original_model, data):
input_fn = test_utils.AllWindowInputFn(
input_pipeline.NumpyReader(data), window_size=chunk_size)
state_manager = state_management.ChainingStateManager(
state_saving_interval=1)
features, _ = input_fn()
state_manager.initialize_graph(original_model)
model_outputs = state_manager.define_loss(
model=original_model,
features=features,
mode=estimator_lib.ModeKeys.TRAIN)
def _eval_outputs(session):
for _ in range(50):
# Warm up saved state
model_outputs.loss.eval()
(posterior_mean, posterior_var,
priors_from_time) = model_outputs.end_state
posteriors = ((posterior_mean, ), (posterior_var, ),
priors_from_time)
outputs = (model_outputs.loss, posteriors,
model_outputs.predictions)
chunked_outputs_evaled = session.run(outputs)
return chunked_outputs_evaled
return _eval_outputs
def _custom_time_series_regressor(
model_dir, head_type, exogenous_feature_columns):
return ts_estimators.TimeSeriesRegressor(
model=lstm_example._LSTMModel(
num_features=5, num_units=128,
exogenous_feature_columns=exogenous_feature_columns),
optimizer=adam.AdamOptimizer(0.001),
config=estimator_lib.RunConfig(tf_random_seed=4),
state_manager=state_management.ChainingStateManager(),
head_type=head_type,
model_dir=model_dir)
def __init__(self, model, state_manager=None, optimizer=None, model_dir=None,
config=None):
"""See TimeSeriesRegressor. Uses the ChainingStateManager by default."""
if not isinstance(model, state_space_model.StateSpaceModel):
raise ValueError(
"StateSpaceRegressor only supports state space models (children of "
"StateSpaceModel) in its `model` argument, got {}.".format(model))
if state_manager is None:
state_manager = state_management.ChainingStateManager()
super(StateSpaceRegressor, self).__init__(
model=model,
state_manager=state_manager,
optimizer=optimizer,
model_dir=model_dir,
config=config)
def test_initialize_graph_state_manager_error(self):
with self.assertRaisesRegexp(ValueError, "initialize_graph"):
model = RandomStateSpaceModel(2, 2)
state_manager = state_management.ChainingStateManager()
outputs = state_manager.define_loss(
model=model,
features={
feature_keys.TrainEvalFeatures.TIMES:
constant_op.constant([[1, 2]]),
feature_keys.TrainEvalFeatures.VALUES:
constant_op.constant([[[1.], [2.]]])
},
mode=estimator_lib.ModeKeys.TRAIN)
initializer = variables.global_variables_initializer()
with self.test_session() as sess:
sess.run([initializer])
outputs.loss.eval()
def test_state_override(self):
test_start_state = (numpy.array([[2, 3, 4]]), (numpy.array([2]),
numpy.array([[3., 5.]])))
data = {
feature_keys.FilteringFeatures.TIMES: numpy.arange(5),
feature_keys.FilteringFeatures.VALUES: numpy.zeros(shape=[5, 3])
}
features, _ = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(data))()
features[feature_keys.FilteringFeatures.STATE_TUPLE] = test_start_state
stub_model = _StateOverrideModel()
chainer = state_management.ChainingStateManager()
stub_model.initialize_graph()
chainer.initialize_graph(model=stub_model)
model_outputs = chainer.define_loss(
model=stub_model, features=features, mode=estimator_lib.ModeKeys.EVAL)
with train.MonitoredSession() as session:
end_state = session.run(model_outputs.end_state)
nest.assert_same_structure(test_start_state, end_state)
for expected, received in zip(
nest.flatten(test_start_state), nest.flatten(end_state)):
self.assertAllEqual(expected, received)
def test_chained_exact_posterior_recovery_no_transition_noise(self):
with self.test_session() as session:
stub_model, data, true_params = self._get_single_model()
chunk_size = 10
input_fn = test_utils.AllWindowInputFn(
input_pipeline.NumpyReader(data), window_size=chunk_size)
features, _ = input_fn()
state_manager = state_management.ChainingStateManager(
state_saving_interval=1)
state_manager.initialize_graph(stub_model)
model_outputs = state_manager.define_loss(
model=stub_model,
features=features,
mode=estimator_lib.ModeKeys.TRAIN)
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
for _ in range(
data[feature_keys.TrainEvalFeatures.TIMES].shape[1] //
chunk_size):
model_outputs.loss.eval()
posterior_mean, posterior_var, posterior_times = session.run(
model_outputs.end_state, feed_dict=true_params)
coordinator.request_stop()
coordinator.join()
self.assertAllClose(numpy.zeros([1, 4, 4]),
posterior_var,
atol=1e-2)
self.assertAllClose(numpy.dot(
numpy.linalg.matrix_power(
stub_model.transition,
data[feature_keys.TrainEvalFeatures.TIMES].shape[1]),
true_params[stub_model.prior_state_mean]),
posterior_mean[0],
rtol=1e-1)
self.assertAllClose(
data[feature_keys.TrainEvalFeatures.TIMES][:, -1],
posterior_times)
def test_one_shot_prediction_head_export(self):
model_dir = self.get_temp_dir()
categorical_column = feature_column.categorical_column_with_hash_bucket(
key="categorical_exogenous_feature", hash_bucket_size=16)
exogenous_feature_columns = [
feature_column.numeric_column("2d_exogenous_feature", shape=(2, )),
feature_column.embedding_column(
categorical_column=categorical_column, dimension=10)
]
estimator = ts_estimators.TimeSeriesRegressor(
model=lstm_example._LSTMModel(
num_features=5,
num_units=128,
exogenous_feature_columns=exogenous_feature_columns),
optimizer=adam.AdamOptimizer(0.001),
config=estimator_lib.RunConfig(tf_random_seed=4),
state_manager=state_management.ChainingStateManager(),
head_type=ts_head_lib.OneShotPredictionHead,
model_dir=model_dir)
train_features = {
feature_keys.TrainEvalFeatures.TIMES:
numpy.arange(20, dtype=numpy.int64),
feature_keys.TrainEvalFeatures.VALUES:
numpy.tile(numpy.arange(20, dtype=numpy.float32)[:, None], [1, 5]),
"2d_exogenous_feature":
numpy.ones([20, 2]),
"categorical_exogenous_feature":
numpy.array(["strkey"] * 20)[:, None]
}
train_input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(train_features),
shuffle_seed=2,
num_threads=1,
batch_size=16,
window_size=16)
estimator.train(input_fn=train_input_fn, steps=5)
input_receiver_fn = estimator.build_raw_serving_input_receiver_fn()
export_location = estimator.export_savedmodel(self.get_temp_dir(),
input_receiver_fn)
graph = ops.Graph()
with graph.as_default():
with session_lib.Session() as session:
signatures = loader.load(session, [tag_constants.SERVING],
export_location)
self.assertEqual([feature_keys.SavedModelLabels.PREDICT],
list(signatures.signature_def.keys()))
predict_signature = signatures.signature_def[
feature_keys.SavedModelLabels.PREDICT]
six.assertCountEqual(self, [
feature_keys.FilteringFeatures.TIMES,
feature_keys.FilteringFeatures.VALUES,
"2d_exogenous_feature", "categorical_exogenous_feature"
], predict_signature.inputs.keys())
features = {
feature_keys.TrainEvalFeatures.TIMES:
numpy.tile(
numpy.arange(35, dtype=numpy.int64)[None, :], [2, 1]),
feature_keys.TrainEvalFeatures.VALUES:
numpy.tile(
numpy.arange(20, dtype=numpy.float32)[None, :, None],
[2, 1, 5]),
"2d_exogenous_feature":
numpy.ones([2, 35, 2]),
"categorical_exogenous_feature":
numpy.tile(
numpy.array(["strkey"] * 35)[None, :, None], [2, 1, 1])
}
feeds = {
graph.as_graph_element(input_value.name):
features[input_key]
for input_key, input_value in
predict_signature.inputs.items()
}
fetches = {
output_key: graph.as_graph_element(output_value.name)
for output_key, output_value in
predict_signature.outputs.items()
}
output = session.run(fetches, feed_dict=feeds)
self.assertAllEqual((2, 15, 5), output["mean"].shape)
def train_and_predict(csv_file_name=_DATA_FILE,
training_steps=200,
estimator_config=None,
export_directory=None):
"""Train and predict using a custom time series model."""
# Construct an Estimator from our LSTM model.
exogenous_feature_columns = [
# Exogenous features are not part of the loss, but can inform
# predictions. In this example the features have no extra information, but
# are included as an API example.
tf.contrib.layers.real_valued_column("2d_exogenous_feature",
dimension=2)
]
estimator = ts_estimators.TimeSeriesRegressor(
model=_LSTMModel(num_features=5,
num_units=128,
exogenous_feature_columns=exogenous_feature_columns),
optimizer=tf.train.AdamOptimizer(0.001),
config=estimator_config,
# Set state to be saved across windows.
state_manager=state_management.ChainingStateManager())
reader = tf.contrib.timeseries.CSVReader(
csv_file_name,
column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES, ) +
(tf.contrib.timeseries.TrainEvalFeatures.VALUES, ) * 5 +
("2d_exogenous_feature", ) * 2))
train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(reader,
batch_size=4,
window_size=32)
estimator.train(input_fn=train_input_fn, steps=training_steps)
evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader)
evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)
# Predict starting after the evaluation
predict_exogenous_features = {
"2d_exogenous_feature":
numpy.concatenate([numpy.ones([1, 100, 1]),
numpy.zeros([1, 100, 1])],
axis=-1)
}
(predictions, ) = tuple(
estimator.predict(
input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
evaluation,
steps=100,
exogenous_features=predict_exogenous_features)))
times = evaluation["times"][0]
observed = evaluation["observed"][0, :, :]
predicted_mean = numpy.squeeze(
numpy.concatenate([evaluation["mean"][0], predictions["mean"]],
axis=0))
all_times = numpy.concatenate([times, predictions["times"]], axis=0)
# Export the model in SavedModel format.
if export_directory is None:
export_directory = tempfile.mkdtemp()
input_receiver_fn = estimator.build_raw_serving_input_receiver_fn()
export_location = estimator.export_savedmodel(export_directory,
input_receiver_fn)
# Predict using the SavedModel
with tf.Graph().as_default():
with tf.Session() as session:
signatures = tf.saved_model.loader.load(
session, [tf.saved_model.tag_constants.SERVING],
export_location)
saved_model_output = (
tf.contrib.timeseries.saved_model_utils.predict_continuation(
continue_from=evaluation,
signatures=signatures,
session=session,
steps=100,
exogenous_features=predict_exogenous_features))
# The exported model gives the same results as the Estimator.predict()
# call above.
numpy.testing.assert_allclose(
predictions["mean"],
numpy.squeeze(saved_model_output["mean"], axis=0))
return times, observed, all_times, predicted_mean
def train_and_predict(csv_file_name=_DATA_FILE,
training_steps=200,
estimator_config=None,
export_directory=None):
"""Train and predict using a custom time series model."""
# Construct an Estimator from our LSTM model.
categorical_column = tf.feature_column.categorical_column_with_hash_bucket(
key="categorical_exogenous_feature", hash_bucket_size=16)
exogenous_feature_columns = [
# Exogenous features are not part of the loss, but can inform
# predictions. In this example the features have no extra information, but
# are included as an API example.
tf.feature_column.numeric_column("2d_exogenous_feature", shape=(2, )),
tf.feature_column.embedding_column(
categorical_column=categorical_column, dimension=10)
]
estimator = ts_estimators.TimeSeriesRegressor(
model=_LSTMModel(num_features=5,
num_units=128,
exogenous_feature_columns=exogenous_feature_columns),
optimizer=tf.train.AdamOptimizer(0.001),
config=estimator_config,
# Set state to be saved across windows.
state_manager=state_management.ChainingStateManager())
reader = tf.contrib.timeseries.CSVReader(
csv_file_name,
column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES, ) +
(tf.contrib.timeseries.TrainEvalFeatures.VALUES, ) * 5 +
("2d_exogenous_feature", ) * 2 +
("categorical_exogenous_feature", )),
# Data types other than for `times` need to be specified if they aren't
# float32. In this case one of our exogenous features has string dtype.
column_dtypes=((tf.int64, ) + (tf.float32, ) * 7 + (tf.string, )))
train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(reader,
batch_size=4,
window_size=32)
estimator.train(input_fn=train_input_fn, steps=training_steps)
evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader)
evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)
# Predict starting after the evaluation
predict_exogenous_features = {
"2d_exogenous_feature":
numpy.concatenate([numpy.ones([1, 100, 1]),
numpy.zeros([1, 100, 1])],
axis=-1),
"categorical_exogenous_feature":
numpy.array(["strkey"] * 100)[None, :, None]
}
(predictions, ) = tuple(
estimator.predict(
input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
evaluation,
steps=100,
exogenous_features=predict_exogenous_features)))
times = evaluation["times"][0]
observed = evaluation["observed"][0, :, :]
predicted_mean = numpy.squeeze(
numpy.concatenate([evaluation["mean"][0], predictions["mean"]],
axis=0))
all_times = numpy.concatenate([times, predictions["times"]], axis=0)
# Export the model in SavedModel format. We include a bit of extra boilerplate
# for "cold starting" as if we didn't have any state from the Estimator, which
# is the case when serving from a SavedModel. If Estimator output is
# available, the result of "Estimator.evaluate" can be passed directly to
# `tf.contrib.timeseries.saved_model_utils.predict_continuation` as the
# `continue_from` argument.
with tf.Graph().as_default():
filter_feature_tensors, _ = evaluation_input_fn()
with tf.train.MonitoredSession() as session:
# Fetch the series to "warm up" our state, which will allow us to make
# predictions for its future values. This is just a dictionary of times,
# values, and exogenous features mapping to numpy arrays. The use of an
# input_fn is just a convenience for the example; they can also be
# specified manually.
filter_features = session.run(filter_feature_tensors)
if export_directory is None:
export_directory = tempfile.mkdtemp()
input_receiver_fn = estimator.build_raw_serving_input_receiver_fn()
export_location = estimator.export_savedmodel(export_directory,
input_receiver_fn)
# Warm up and predict using the SavedModel
with tf.Graph().as_default():
with tf.Session() as session:
signatures = tf.saved_model.loader.load(
session, [tf.saved_model.tag_constants.SERVING],
export_location)
state = tf.contrib.timeseries.saved_model_utils.cold_start_filter(
signatures=signatures,
session=session,
features=filter_features)
saved_model_output = (
tf.contrib.timeseries.saved_model_utils.predict_continuation(
continue_from=state,
signatures=signatures,
session=session,
steps=100,
exogenous_features=predict_exogenous_features))
# The exported model gives the same results as the Estimator.predict()
# call above.
numpy.testing.assert_allclose(
predictions["mean"],
numpy.squeeze(saved_model_output["mean"], axis=0))
return times, observed, all_times, predicted_mean
