tf.contrib.timeseries.ARRegressor

Class ARRegressor

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

An Estimator for an (optionally non-linear) autoregressive model.

ARRegressor is a window-based model, inputting fixed windows of length input_window_size and outputting fixed windows of length output_window_size. These two parameters must add up to the window_size passed to the Chunker used to create an input_fn for training or evaluation. RandomWindowInputFn is suggested for both training and evaluation, although it may be seeded for deterministic evaluation.

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,
    input_window_size,
    output_window_size,
    num_features,
    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).
  • 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.

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.

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 Get Started with 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.
  • 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 Get Started with 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.
  • 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 Get Started with 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.