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class _LSTMModel(ts_model.SequentialTimeSeriesModel):
"""A time series model-building example using an RNNCell."""
- def __init__(self, num_units, num_features, dtype=tf.float32):
+ def __init__(self, num_units, num_features, exogenous_feature_columns=None,
+ dtype=tf.float32):
"""Initialize/configure the model object.
Note that we do not start graph building here. Rather, this object is a
num_units: The number of units in the model's LSTMCell.
num_features: The dimensionality of the time series (features per
timestep).
+ exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
+ objects representing features which are inputs to the model but are
+ not predicted by it. These must then be present for training,
+ evaluation, and prediction.
dtype: The floating point data type to use.
"""
super(_LSTMModel, self).__init__(
train_output_names=["mean"],
predict_output_names=["mean"],
num_features=num_features,
+ exogenous_feature_columns=exogenous_feature_columns,
dtype=dtype)
self._num_units = num_units
# Filled in by initialize_graph()
tf.zeros([], dtype=tf.int64),
# The previous observation or prediction.
tf.zeros([self.num_features], dtype=self.dtype),
+ # The most recently seen exogenous features.
+ tf.zeros(self._get_exogenous_embedding_shape(), dtype=self.dtype),
# The state of the RNNCell (batch dimension removed since this parent
# class will broadcast).
[tf.squeeze(state_element, axis=0)
loss (note that we could also return other measures of goodness of fit,
although only "loss" will be optimized).
"""
- state_from_time, prediction, lstm_state = state
+ state_from_time, prediction, exogenous, lstm_state = state
with tf.control_dependencies(
[tf.assert_equal(current_times, state_from_time)]):
# Subtract the mean and divide by the variance of the series. Slightly
(prediction - transformed_values) ** 2, axis=-1)
# Keep track of the new observation in model state. It won't be run
# through the LSTM until the next _imputation_step.
- new_state_tuple = (current_times, transformed_values, lstm_state)
+ new_state_tuple = (current_times, transformed_values,
+ exogenous, lstm_state)
return (new_state_tuple, predictions)
def _prediction_step(self, current_times, state):
"""Advance the RNN state using a previous observation or prediction."""
- _, previous_observation_or_prediction, lstm_state = state
+ _, previous_observation_or_prediction, exogenous, lstm_state = state
+ # Update LSTM state based on the most recent exogenous and endogenous
+ # features.
+ inputs = tf.concat([previous_observation_or_prediction, exogenous],
+ axis=-1)
lstm_output, new_lstm_state = self._lstm_cell_run(
- inputs=previous_observation_or_prediction, state=lstm_state)
+ inputs=inputs, state=lstm_state)
next_prediction = self._predict_from_lstm_output(lstm_output)
- new_state_tuple = (current_times, next_prediction, new_lstm_state)
+ new_state_tuple = (current_times, next_prediction,
+ exogenous, new_lstm_state)
return new_state_tuple, {"mean": self._scale_back_data(next_prediction)}
def _imputation_step(self, current_times, state):
def _exogenous_input_step(
self, current_times, current_exogenous_regressors, state):
- """Update model state based on exogenous regressors."""
- raise NotImplementedError(
- "Exogenous inputs are not implemented for this example.")
+ """Save exogenous regressors in model state for use in _prediction_step."""
+ state_from_time, prediction, _, lstm_state = state
+ return (state_from_time, prediction,
+ current_exogenous_regressors, lstm_state)
def train_and_predict(
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),
+ 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))
+ + (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)))
+ evaluation, steps=100,
+ exogenous_features=predict_exogenous_features)))
times = evaluation["times"][0]
observed = evaluation["observed"][0, :, :]
predicted_mean = numpy.squeeze(numpy.concatenate(
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:
saved_model_output = (
tf.contrib.timeseries.saved_model_utils.predict_continuation(
continue_from=evaluation, signatures=signatures,
- session=session, steps=100))
+ 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(