def __init__(self, model, state_manager=None, optimizer=None, model_dir=None,
config=None, head_type=ts_head_lib.TimeSeriesRegressionHead):
"""Initialize the Estimator.
Args:
model: The time series model to wrap (inheriting from TimeSeriesModel).
state_manager: The state manager to use, or (by default)
PassthroughStateManager if none is needed.
optimizer: The optimization algorithm to use when training, inheriting
from tf.train.Optimizer. Defaults to Adam with step size 0.02.
model_dir: See `Estimator`.
config: See `Estimator`.
head_type: The kind of head to use for the model (inheriting from
`TimeSeriesRegressionHead`).
"""
input_statistics_generator = math_utils.InputStatisticsFromMiniBatch(
dtype=model.dtype, num_features=model.num_features)
if state_manager is None:
if isinstance(model, ar_model.ARModel):
state_manager = state_management.FilteringOnlyStateManager()
else:
state_manager = state_management.PassthroughStateManager()
if optimizer is None:
optimizer = train.AdamOptimizer(0.02)
self._model = model
ts_regression_head = head_type(
model=model, state_manager=state_manager, optimizer=optimizer,
input_statistics_generator=input_statistics_generator)
model_fn = ts_regression_head.create_estimator_spec
super(TimeSeriesRegressor, self).__init__(
model_fn=model_fn,
model_dir=model_dir,
config=config)
def __init__(self,
periodicities,
input_window_size,
output_window_size,
model_dir=None,
num_features=1,
extra_feature_columns=None,
num_timesteps=10,
loss=ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS,
num_units=128,
optimizer="Adam",
config=None):
"""Initialize the Estimator.
Args:
periodicities: periodicities of the input data, in the same units as the
time feature (for example 24 if feeding hourly data with a daily
periodicity, or 60 * 24 if feeding minute-level data with daily
periodicity). Note this can be a single value or a list of values for
multiple periodicities.
input_window_size: Number of past time steps of data to look at when doing
the regression.
output_window_size: Number of future time steps to predict. Note that
setting this value to > 1 empirically seems to give a better fit.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
num_features: The dimensionality of the time series (default value is
one for univariate, more than one for multivariate).
extra_feature_columns: A list of `tf.feature_column`s (for example
`tf.feature_column.embedding_column`) corresponding to features which
provide extra information to the model but are not part of the series to
be predicted.
num_timesteps: Number of buckets into which to divide (time %
periodicity). This value multiplied by the number of periodicities is
the number of time features added to the model.
loss: Loss function to use for training. Currently supported values are
SQUARED_LOSS and NORMAL_LIKELIHOOD_LOSS. Note that for
NORMAL_LIKELIHOOD_LOSS, we train the covariance term as well. For
SQUARED_LOSS, the evaluation loss is reported based on un-scaled
observations and predictions, while the training loss is computed on
normalized data.
num_units: The size of the hidden state in the encoder and decoder LSTM
cells.
optimizer: string, `tf.train.Optimizer` object, or callable that defines
the optimizer algorithm to use for training. Defaults to the Adam
optimizer with a learning rate of 0.01.
config: Optional `estimator.RunConfig` object to configure the runtime
settings.
"""
optimizer = optimizers.get_optimizer_instance(
optimizer, learning_rate=0.01)
model = ar_model.ARModel(
periodicities=periodicities,
input_window_size=input_window_size,
output_window_size=output_window_size,
num_features=num_features,
exogenous_feature_columns=extra_feature_columns,
num_time_buckets=num_timesteps,
loss=loss,
prediction_model_factory=functools.partial(
ar_model.LSTMPredictionModel, num_units=num_units))
state_manager = state_management.FilteringOnlyStateManager()
super(LSTMAutoRegressor, self).__init__(
model=model,
state_manager=state_manager,
optimizer=optimizer,
model_dir=model_dir,
config=config,
head_type=ts_head_lib.OneShotPredictionHead)
def __init__(
self, periodicities, input_window_size, output_window_size,
num_features, exogenous_feature_columns=None, num_time_buckets=10,
loss=ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS, hidden_layer_sizes=None,
anomaly_prior_probability=None, anomaly_distribution=None,
optimizer=None, model_dir=None, config=None):
"""Initialize the Estimator.
Args:
periodicities: periodicities of the input data, in the same units as the
time feature. Note this can be a single value or a list of values for
multiple periodicities.
input_window_size: Number of past time steps of data to look at when doing
the regression.
output_window_size: Number of future time steps to predict. Note that
setting it to > 1 empirically seems to give a better fit.
num_features: The dimensionality of the time series (one for univariate,
more than one for multivariate).
exogenous_feature_columns: A list of `tf.feature_column`s (for example
`tf.feature_column.embedding_column`) corresponding to exogenous
features which provide extra information to the model but are not part
of the series to be predicted. Passed to
`tf.feature_column.input_layer`.
num_time_buckets: Number of buckets into which to divide (time %
periodicity) for generating time based features.
loss: Loss function to use for training. Currently supported values are
SQUARED_LOSS and NORMAL_LIKELIHOOD_LOSS. Note that for
NORMAL_LIKELIHOOD_LOSS, we train the covariance term as well. For
SQUARED_LOSS, the evaluation loss is reported based on un-scaled
observations and predictions, while the training loss is computed on
normalized data.
hidden_layer_sizes: list of sizes of hidden layers.
anomaly_prior_probability: If specified, constructs a mixture model under
which anomalies (modeled with `anomaly_distribution`) have this prior
probability. See `AnomalyMixtureARModel`.
anomaly_distribution: May not be specified unless
anomaly_prior_probability is specified and is not None. Controls the
distribution of anomalies under the mixture model. Currently either
`ar_model.AnomalyMixtureARModel.GAUSSIAN_ANOMALY` or
`ar_model.AnomalyMixtureARModel.CAUCHY_ANOMALY`. See
`AnomalyMixtureARModel`. Defaults to `GAUSSIAN_ANOMALY`.
optimizer: The optimization algorithm to use when training, inheriting
from tf.train.Optimizer. Defaults to Adagrad with step size 0.1.
model_dir: See `Estimator`.
config: See `Estimator`.
Raises:
ValueError: For invalid combinations of arguments.
"""
if optimizer is None:
optimizer = train.AdagradOptimizer(0.1)
if anomaly_prior_probability is None and anomaly_distribution is not None:
raise ValueError("anomaly_prior_probability is required if "
"anomaly_distribution is specified.")
if anomaly_prior_probability is None:
if anomaly_distribution is None:
anomaly_distribution = ar_model.AnomalyMixtureARModel.GAUSSIAN_ANOMALY
model = ar_model.ARModel(
periodicities=periodicities, num_features=num_features,
prediction_model_factory=functools.partial(
ar_model.FlatPredictionModel,
hidden_layer_sizes=hidden_layer_sizes),
exogenous_feature_columns=exogenous_feature_columns,
num_time_buckets=num_time_buckets,
input_window_size=input_window_size,
output_window_size=output_window_size, loss=loss)
else:
if loss != ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS:
raise ValueError(
"AnomalyMixtureARModel only supports "
"ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS for its loss argument.")
model = ar_model.AnomalyMixtureARModel(
periodicities=periodicities,
input_window_size=input_window_size,
output_window_size=output_window_size,
num_features=num_features,
prediction_model_factory=functools.partial(
ar_model.FlatPredictionModel,
hidden_layer_sizes=hidden_layer_sizes),
exogenous_feature_columns=exogenous_feature_columns,
num_time_buckets=num_time_buckets,
anomaly_prior_probability=anomaly_prior_probability,
anomaly_distribution=anomaly_distribution)
state_manager = state_management.FilteringOnlyStateManager()
super(ARRegressor, self).__init__(
model=model,
state_manager=state_manager,
optimizer=optimizer,
model_dir=model_dir,
config=config)
