gluonts.mx.model.predictor module#

class gluonts.mx.model.predictor.GluonPredictor(input_names: typing.List[str], prediction_net, batch_size: int, prediction_length: int, ctx: mxnet.context.Context, input_transform: gluonts.transform._base.Transformation, lead_time: int = 0, forecast_generator: gluonts.model.forecast_generator.ForecastGenerator = gluonts.model.forecast_generator.SampleForecastGenerator(), output_transform: typing.Optional[typing.Callable[[typing.Dict[str, typing.Any], numpy.ndarray], numpy.ndarray]] = None, dtype: typing.Type = <class 'numpy.float32'>)[source]#

Bases: gluonts.model.predictor.Predictor

Base predictor type for Gluon-based models.

Parameters

  • input_names – Input tensor names for the graph

  • prediction_net – Network that will be called for prediction

  • batch_size – Number of time series to predict in a single batch

  • prediction_length – Number of time steps to predict

  • input_transform – Input transformation pipeline

  • output_transform – Output transformation

  • ctx – MXNet context to use for computation

  • forecast_generator – Class to generate forecasts from network outputs

BlockType#

alias of mxnet.gluon.block.Block

as_symbol_block_predictor(batch: Optional[Dict[str, Any]] = None, dataset: Optional[gluonts.dataset.Dataset] = None) → gluonts.mx.model.predictor.SymbolBlockPredictor[source]#

Returns a variant of the current GluonPredictor backed by a Gluon SymbolBlock. If the current predictor is already a SymbolBlockPredictor, it just returns itself.

One of batch or datset must be set.

Parameters

  • batch – A batch of data to use for the required forward pass after the hybridize() call of the underlying network.

  • dataset – Dataset from which a batch is extracted if batch is not set.

Returns

A predictor derived from the current one backed by a SymbolBlock.

Return type

SymbolBlockPredictor

hybridize(batch: Dict[str, Any]) → None[source]#

Hybridizes the underlying prediction network.

Parameters

batch – A batch of data to use for the required forward pass after the hybridize() call.

property network#
predict(dataset: gluonts.dataset.Dataset, num_samples: Optional[int] = None, num_workers: Optional[int] = None, num_prefetch: Optional[int] = None, **kwargs) → Iterator[gluonts.model.forecast.Forecast][source]#

Compute forecasts for the time series in the provided dataset. This method is not implemented in this abstract class; please use one of the subclasses. :param dataset: The dataset containing the time series to predict.

Returns

Iterator over the forecasts, in the same order as the dataset iterable was provided.

Return type

Iterator[Forecast]

serialize(path: pathlib.Path) → None[source]#
serialize_prediction_net(path: pathlib.Path) → None[source]#
class gluonts.mx.model.predictor.RepresentableBlockPredictor(prediction_net, batch_size: int, prediction_length: int, ctx: mxnet.context.Context, input_transform: gluonts.transform._base.Transformation, lead_time: int = 0, forecast_generator: gluonts.model.forecast_generator.ForecastGenerator = gluonts.model.forecast_generator.SampleForecastGenerator(), output_transform: typing.Optional[typing.Callable[[typing.Dict[str, typing.Any], numpy.ndarray], numpy.ndarray]] = None, dtype: typing.Type = <class 'numpy.float32'>)[source]#

Bases: gluonts.mx.model.predictor.GluonPredictor

A predictor which serializes the network structure using the JSON- serialization methods located in gluonts.core.serde. Use the following logic to create a RepresentableBlockPredictor from a trained prediction network.

>>> def create_representable_block_predictor(
...        prediction_network: mx.gluon.HybridBlock,
...        **kwargs
... ) -> RepresentableBlockPredictor:
...    return RepresentableBlockPredictor(
...        prediction_net=prediction_network,
...        **kwargs
...    )
BlockType#

alias of mxnet.gluon.block.HybridBlock

as_symbol_block_predictor(batch: Optional[Dict[str, Any]] = None, dataset: Optional[gluonts.dataset.Dataset] = None) → gluonts.mx.model.predictor.SymbolBlockPredictor[source]#

Returns a variant of the current GluonPredictor backed by a Gluon SymbolBlock. If the current predictor is already a SymbolBlockPredictor, it just returns itself.

One of batch or datset must be set.

Parameters

  • batch – A batch of data to use for the required forward pass after the hybridize() call of the underlying network.

  • dataset – Dataset from which a batch is extracted if batch is not set.

Returns

A predictor derived from the current one backed by a SymbolBlock.

Return type

SymbolBlockPredictor

classmethod deserialize(path: pathlib.Path, ctx: Optional[mxnet.context.Context] = None) → gluonts.mx.model.predictor.RepresentableBlockPredictor[source]#

Load a serialized predictor from the given path.

Parameters

  • path – Path to the serialized files predictor.

  • **kwargs – Optional context/device parameter to be used with the predictor. If nothing is passed will use the GPU if available and CPU otherwise.

serialize(path: pathlib.Path) → None[source]#
serialize_prediction_net(path: pathlib.Path) → None[source]#
class gluonts.mx.model.predictor.SymbolBlockPredictor(input_names: typing.List[str], prediction_net, batch_size: int, prediction_length: int, ctx: mxnet.context.Context, input_transform: gluonts.transform._base.Transformation, lead_time: int = 0, forecast_generator: gluonts.model.forecast_generator.ForecastGenerator = gluonts.model.forecast_generator.SampleForecastGenerator(), output_transform: typing.Optional[typing.Callable[[typing.Dict[str, typing.Any], numpy.ndarray], numpy.ndarray]] = None, dtype: typing.Type = <class 'numpy.float32'>)[source]#

Bases: gluonts.mx.model.predictor.GluonPredictor

A predictor which serializes the network structure as an MXNet symbolic graph. Should be used for models deployed in production in order to ensure forward-compatibility as GluonTS models evolve.

Used by the training shell if training is invoked with a hyperparameter use_symbol_block_predictor = True.

BlockType#

alias of mxnet.gluon.block.SymbolBlock

as_symbol_block_predictor(batch: Optional[Dict[str, Any]] = None, dataset: Optional[gluonts.dataset.Dataset] = None) → gluonts.mx.model.predictor.SymbolBlockPredictor[source]#

Returns a variant of the current GluonPredictor backed by a Gluon SymbolBlock. If the current predictor is already a SymbolBlockPredictor, it just returns itself.

One of batch or datset must be set.

Parameters

  • batch – A batch of data to use for the required forward pass after the hybridize() call of the underlying network.

  • dataset – Dataset from which a batch is extracted if batch is not set.

Returns

A predictor derived from the current one backed by a SymbolBlock.

Return type

SymbolBlockPredictor

classmethod deserialize(path: pathlib.Path, ctx: Optional[mxnet.context.Context] = None) → gluonts.mx.model.predictor.SymbolBlockPredictor[source]#

Load a serialized predictor from the given path.

Parameters

  • path – Path to the serialized files predictor.

  • **kwargs – Optional context/device parameter to be used with the predictor. If nothing is passed will use the GPU if available and CPU otherwise.

serialize_prediction_net(path: pathlib.Path) → None[source]#