tf.contrib.timeseries.StructuralEnsembleRegressor

Class StructuralEnsembleRegressor

Defined in tensorflow/contrib/timeseries/python/timeseries/estimators.py.

An Estimator for structural time series models.

"Structural" refers to the fact that this model explicitly accounts for structure in the data, such as periodicity and trends.

StructuralEnsembleRegressor is a state space model. It contains components for modeling level, local linear trends, periodicity, and mean-reverting transients via a moving average component. Multivariate series are fit with full covariance matrices for observation and latent state transition noise, each feature of the multivariate series having its own latent components.

Note that unlike ARRegressor, StructuralEnsembleRegressor is sequential, and so accepts variable window sizes with the same model.

For training, RandomWindowInputFn is recommended as an input_fn. Model state is managed through ChainingStateManager: since state space models are inherently sequential, we save state from previous iterations to get approximate/eventual consistency while achieving good performance through batched computation.

For evaluation, either pass a significant chunk of the series in a single window (e.g. set window_size to the whole series with WholeDatasetInputFn), or use enough random evaluation iterations to cover several passes through the whole dataset. Either method will ensure that stale saved state has been flushed.

Properties

config

model_dir

model_fn

Returns the model_fn which is bound to self.params.

Returns:

The model_fn with following signature: def model_fn(features, labels, mode, config)

params

Methods

__init__

__init__(
    periodicities,
    num_features,
    cycle_num_latent_values=11,
    moving_average_order=4,
    autoregressive_order=0,
    exogenous_feature_columns=None,
    exogenous_update_condition=None,
    dtype=tf.double,
    anomaly_prior_probability=None,
    optimizer=None,
    model_dir=None,
    config=None,
    head_type=ts_head_lib.TimeSeriesRegressionHead
)

Initialize the Estimator.

Args:

  • periodicities: The expected periodicity of the data (for example 24 if feeding hourly data with a daily periodicity, or 60 * 24 if feeding minute-level data with daily periodicity). Either a scalar or a list. This parameter can be any real value, and does not control the size of the model. However, increasing this without increasing num_values_per_cycle will lead to smoother periodic behavior, as the same number of distinct values will be cycled through over a longer period of time.
  • num_features: The dimensionality of the time series (one for univariate, more than one for multivariate).
  • cycle_num_latent_values: Along with moving_average_order and num_features, controls the latent state size of the model. Square matrices of size num_features * (moving_average_order + cycle_num_latent_values + 3) are created and multiplied, so larger values may be slow. The trade-off is with resolution: cycling between a smaller number of latent values means that only smoother functions can be modeled.
  • moving_average_order: Controls model size (along with cycle_num_latent_values and autoregressive_order) and the number of steps before transient deviations revert to the mean defined by the period and level/trend components.
  • autoregressive_order: Each contribution from this component is a linear combination of this many previous contributions. Also helps to determine the model size. Learning autoregressive coefficients typically requires more steps and a smaller step size than other components.
  • exogenous_feature_columns: A list of tf.feature_columns (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.
  • exogenous_update_condition: A function taking two Tensor arguments, times (shape [batch size]) and features (a dictionary mapping exogenous feature keys to Tensors with shapes [batch size, ...]), and returning a boolean Tensor with shape [batch size] indicating whether state should be updated using exogenous features for each part of the batch. Where it is False, no exogenous update is performed. If None (default), exogenous updates are always performed. Useful for avoiding "leaky" frequent exogenous updates when sparse updates are desired. Called only during graph construction. See the "known anomaly" example for example usage.
  • dtype: The floating point data type to compute with. float32 may be faster, but can be problematic for larger models and longer time series.
  • anomaly_prior_probability: If not None, the model attempts to automatically detect and ignore anomalies during training. This parameter then controls the prior probability of an anomaly. Values closer to 0 mean that points will be discarded less frequently. The default value (None) means that anomalies are not discarded, which may be slightly faster.
  • 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).

build_raw_serving_input_receiver_fn

build_raw_serving_input_receiver_fn(
    default_batch_size=None,
    default_series_length=None
)

Build an input_receiver_fn for export_savedmodel which accepts arrays.

Automatically creates placeholders for exogenous FeatureColumns passed to the model.

Args:

  • default_batch_size: If specified, must be a scalar integer. Sets the batch size in the static shape information of all feature Tensors, which means only this batch size will be accepted by the exported model. If None (default), static shape information for batch sizes is omitted.
  • default_series_length: If specified, must be a scalar integer. Sets the series length in the static shape information of all feature Tensors, which means only this series length will be accepted by the exported model. If None (default), static shape information for series length is omitted.

Returns:

An input_receiver_fn which may be passed to the Estimator's export_savedmodel.

eval_dir

eval_dir(name=None)

Shows directory name where evaluation metrics are dumped.

Args:

  • name: Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

Returns:

A string which is the path of directory contains evaluation metrics.

evaluate

evaluate(
    input_fn,
    steps=None,
    hooks=None,
    checkpoint_path=None,
    name=None
)

Evaluates the model given evaluation data input_fn.

For each step, calls input_fn, which returns one batch of data. Evaluates until: - steps batches are processed, or - input_fn raises an end-of-input exception (OutOfRangeError or StopIteration).

Args:

  • input_fn: A function that constructs the input data for evaluation. See Premade Estimators for more information. The function should construct and return one of the following:

    • A 'tf.data.Dataset' object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
    • A tuple (features, labels): Where features is a Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
  • steps: Number of steps for which to evaluate model. If None, evaluates until input_fn raises an end-of-input exception.

  • hooks: List of SessionRunHook subclass instances. Used for callbacks inside the evaluation call.

  • checkpoint_path: Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, evaluation is run with newly initialized Variables instead of restored from checkpoint.

  • name: Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

Returns:

A dict containing the evaluation metrics specified in model_fn keyed by name, as well as an entry global_step which contains the value of the global step for which this evaluation was performed.

Raises:

  • ValueError: If steps <= 0.
  • ValueError: If no model has been trained, namely model_dir, or the given checkpoint_path is empty.

export_savedmodel

export_savedmodel(
    export_dir_base,
    serving_input_receiver_fn,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None,
    strip_default_attrs=False
)

Exports inference graph as a SavedModel into given dir.

For a detailed guide, see Using SavedModel with Estimators.

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single MetaGraphDef saved from this session.

The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

Args:

  • export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
  • serving_input_receiver_fn: A function that takes no argument and returns a ServingInputReceiver or TensorServingInputReceiver.
  • assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
  • as_text: whether to write the SavedModel proto in text format.
  • checkpoint_path: The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
  • strip_default_attrs: Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see Stripping Default-Valued Attributes.

Returns:

The string path to the exported directory.

Raises:

  • ValueError: if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found.

get_variable_names

get_variable_names()

Returns list of all variable names in this model.

Returns:

List of names.

Raises:

  • ValueError: If the Estimator has not produced a checkpoint yet.

get_variable_value

get_variable_value(name)

Returns value of the variable given by name.

Args:

  • name: string or a list of string, name of the tensor.

Returns:

Numpy array - value of the tensor.

Raises:

  • ValueError: If the Estimator has not produced a checkpoint yet.

latest_checkpoint

latest_checkpoint()

Finds the filename of latest saved checkpoint file in model_dir.

Returns:

The full path to the latest checkpoint or None if no checkpoint was found.

predict

predict(
    input_fn,
    predict_keys=None,
    hooks=None,
    checkpoint_path=None,
    yield_single_examples=True
)

Yields predictions for given features.

Args:

  • input_fn: A function that constructs the features. Prediction continues until input_fn raises an end-of-input exception (OutOfRangeError or StopIteration). See Premade Estimators for more information. The function should construct and return one of the following:

    • A 'tf.data.Dataset' object: Outputs of Dataset object must have same constraints as below.
    • features: A Tensor or a dictionary of string feature name to Tensor. features are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
    • A tuple, in which case the first item is extracted as features.
  • predict_keys: list of str, name of the keys to predict. It is used if the EstimatorSpec.predictions is a dict. If predict_keys is used then rest of the predictions will be filtered from the dictionary. If None, returns all.

  • hooks: List of SessionRunHook subclass instances. Used for callbacks inside the prediction call.

  • checkpoint_path: Path of a specific checkpoint to predict. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, prediction is run with newly initialized Variables instead of restored from checkpoint.

  • yield_single_examples: If False, yield the whole batch as returned by the model_fn instead of decomposing the batch into individual elements. This is useful if model_fn returns some tensors whose first dimension is not equal to the batch size.

Yields:

Evaluated values of predictions tensors.

Raises:

  • ValueError: Could not find a trained model in model_dir.
  • ValueError: If batch length of predictions is not the same and yield_single_examples is True.
  • ValueError: If there is a conflict between predict_keys and predictions. For example if predict_keys is not None but EstimatorSpec.predictions is not a dict.

train

train(
    input_fn,
    hooks=None,
    steps=None,
    max_steps=None,
    saving_listeners=None
)

Trains a model given training data input_fn.

Args:

  • input_fn: A function that provides input data for training as minibatches. See Premade Estimators for more information. The function should construct and return one of the following:

    • A 'tf.data.Dataset' object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
    • A tuple (features, labels): Where features is a Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
  • hooks: List of SessionRunHook subclass instances. Used for callbacks inside the training loop.

  • steps: Number of steps for which to train model. If None, train forever or train until input_fn generates the OutOfRange error or StopIteration exception. 'steps' works incrementally. If you call two times train(steps=10) then training occurs in total 20 steps. If OutOfRange or StopIteration occurs in the middle, training stops before 20 steps. If you don't want to have incremental behavior please set max_steps instead. If set, max_steps must be None.

  • max_steps: Number of total steps for which to train model. If None, train forever or train until input_fn generates the OutOfRange error or StopIteration exception. If set, steps must be None. If OutOfRange or StopIteration occurs in the middle, training stops before max_steps steps. Two calls to train(steps=100) means 200 training iterations. On the other hand, two calls to train(max_steps=100) means that the second call will not do any iteration since first call did all 100 steps.

  • saving_listeners: list of CheckpointSaverListener objects. Used for callbacks that run immediately before or after checkpoint savings.

Returns:

self, for chaining.

Raises:

  • ValueError: If both steps and max_steps are not None.
  • ValueError: If either steps or max_steps is <= 0.