tf.contrib.timeseries.ARRegressor

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

Inherits From: TimeSeriesRegressor

tf.contrib.timeseries.ARRegressor(
    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
)

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.

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_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.compat.v1.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.

Attributes
config
model_dir
model_fn	Returns the model_fn which is bound to self.params.
params

Methods

build_one_shot_parsing_serving_input_receiver_fn

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build_one_shot_parsing_serving_input_receiver_fn(
    filtering_length, prediction_length, default_batch_size=None,
    values_input_dtype=None, truncate_values=False
)

Build an input_receiver_fn for export_savedmodel accepting tf.Examples.

Only compatible with OneShotPredictionHead (see head).

Args
filtering_length	The number of time steps used as input to the model, for which values are provided. If more than filtering_length values are provided (via truncate_values), only the first filtering_length values are used.
prediction_length	The number of time steps requested as predictions from the model. Times and all exogenous features must be provided for these steps.
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.
values_input_dtype	An optional dtype specification for values in the tf.Example protos (either float32 or int64, since these are the numeric types supported by tf.Example). After parsing, values are cast to the model's dtype (float32 or float64).
truncate_values	If True, expects filtering_length + prediction_length values to be provided, but only uses the first filtering_length. If False (default), exactly filtering_length values must be provided.

Returns

An input_receiver_fn which may be passed to the Estimator's export_savedmodel. Expects features contained in a vector of serialized tf.Examples with shape batch size, each tf.Example containing features with the following shapes: times: [filtering_length + prediction_length] integer values: [filtering_length, num features] floating point. If truncate_values is True, expects filtering_length + prediction_length values but only uses the first filtering_length. all exogenous features: filtering_length + prediction_length, ...

build_raw_serving_input_receiver_fn

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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

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eval_dir(
    name=None
)

Shows the 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

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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 (tf.errors.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 tf.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 tf.train.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 ones 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. For canned estimators, the dict contains the loss (mean loss per mini-batch) and the average_loss (mean loss per sample). Canned classifiers also return the accuracy. Canned regressors also return the label/mean and the prediction/mean.

Raises
ValueError	If steps <= 0.

experimental_export_all_saved_models

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experimental_export_all_saved_models(
    export_dir_base, input_receiver_fn_map, assets_extra=None, as_text=False,
    checkpoint_path=None
)

Exports a SavedModel with tf.MetaGraphDefs for each requested mode.

For each mode passed in via the input_receiver_fn_map, this method builds a new graph by calling the input_receiver_fn to obtain feature and label Tensors. Next, this method calls the Estimator's model_fn in the passed mode to generate the model graph based on those features and labels, and restores the given checkpoint (or, lacking that, the most recent checkpoint) into the graph. Only one of the modes is used for saving variables to the SavedModel (order of preference: tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, then tf.estimator.ModeKeys.PREDICT), such that up to three tf.MetaGraphDefs are saved with a single set of variables in a single SavedModel directory.

For the variables and tf.MetaGraphDefs, a timestamped export directory below export_dir_base, and writes a SavedModel into it containing the tf.MetaGraphDef for the given mode and its associated signatures.

For prediction, 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 tf.saved_model.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 tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

For training and evaluation, the train_op is stored in an extra collection, and loss, metrics, and predictions are included in a SignatureDef for the mode in question.

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.
input_receiver_fn_map	dict of tf.estimator.ModeKeys to input_receiver_fn mappings, where the input_receiver_fn is a function that takes no arguments and returns the appropriate subclass of InputReceiver.
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.

Returns
The string path to the exported directory.

Raises
ValueError	if any input_receiver_fn is None, no export_outputs are provided, or no checkpoint can be found.

export_saved_model

View source

export_saved_model(
    export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False,
    checkpoint_path=None, experimental_mode=ModeKeys.PREDICT
)

Exports inference graph as a SavedModel into the 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 tf.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 tf.saved_model.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 tf.estimator.export.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'}.

The experimental_mode parameter can be used to export a single train/eval/predict graph as a SavedModel. See experimental_export_all_saved_models for full docs.

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 tf.estimator.export.ServingInputReceiver or tf.estimator.export.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.
experimental_mode	tf.estimator.ModeKeys value indicating with mode will be exported. Note that this feature is experimental.

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.

export_savedmodel

View source

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 the given dir. (deprecated)

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 tf.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 tf.saved_model.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 tf.estimator.export.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 tf.estimator.export.ServingInputReceiver or tf.estimator.export.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

View source

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

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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

View source

latest_checkpoint()

Finds the filename of the latest saved checkpoint file in model_dir.

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

predict

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

Yields predictions for given features.

Please note that interleaving two predict outputs does not work. See: issue/20506

Args
input_fn	A function that constructs the features. Prediction continues until input_fn raises an end-of-input exception (tf.errors.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 tf.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 tf.estimator.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 tf.train.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 ones restored from checkpoint.
yield_single_examples	If False, yields 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	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 tf.estimator.EstimatorSpec.predictions is not a dict.

train

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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 tf.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 tf.train.SessionRunHook subclass instances. Used for callbacks inside the training loop.
steps	Number of steps for which to train the model. If None, train forever or train until input_fn generates the tf.errors.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 tf.errors.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 <= 0.