gluonts.mx.model.estimator module#
- class gluonts.mx.model.estimator.GluonEstimator(*, trainer: gluonts.mx.trainer._base.Trainer, batch_size: int = 32, lead_time: int = 0, dtype: typing.Type = <class 'numpy.float32'>)[source]#
Bases:
gluonts.model.estimator.EstimatorAn Estimator type with utilities for creating Gluon-based models.
To extend this class, one needs to implement three methods: create_transformation, create_training_network, create_predictor, create_training_data_loader, and create_validation_data_loader.
- create_predictor(transformation: gluonts.transform._base.Transformation, trained_network: mxnet.gluon.block.HybridBlock) gluonts.model.predictor.Predictor[source]#
Create and return a predictor object.
- Parameters
transformation – Transformation to be applied to data before it goes into the model.
module – A trained HybridBlock object.
- Returns
A predictor wrapping a HybridBlock used for inference.
- Return type
- create_training_data_loader(data: gluonts.dataset.Dataset, **kwargs) Iterable[Dict[str, Any]][source]#
Create a data loader for training purposes.
- Parameters
data – Dataset from which to create the data loader.
- Returns
The data loader, i.e. and iterable over batches of data.
- Return type
DataLoader
- create_training_network() mxnet.gluon.block.HybridBlock[source]#
Create and return the network used for training (i.e., computing the loss).
- Returns
The network that computes the loss given input data.
- Return type
HybridBlock
- create_transformation() gluonts.transform._base.Transformation[source]#
Create and return the transformation needed for training and inference.
- Returns
The transformation that will be applied entry-wise to datasets, at training and inference time.
- Return type
- create_validation_data_loader(data: gluonts.dataset.Dataset, **kwargs) Iterable[Dict[str, Any]][source]#
Create a data loader for validation purposes.
- Parameters
data – Dataset from which to create the data loader.
- Returns
The data loader, i.e. and iterable over batches of data.
- Return type
DataLoader
- classmethod from_hyperparameters(**hyperparameters) gluonts.mx.model.estimator.GluonEstimator[source]#
- lead_time: int#
- prediction_length: int#
- train(training_data: gluonts.dataset.Dataset, validation_data: Optional[gluonts.dataset.Dataset] = None, shuffle_buffer_length: Optional[int] = None, cache_data: bool = False, **kwargs) gluonts.model.predictor.Predictor[source]#
Train the estimator on the given data.
- Parameters
training_data – Dataset to train the model on.
validation_data – Dataset to validate the model on during training.
- Returns
The predictor containing the trained model.
- Return type
- train_from(predictor: gluonts.mx.model.predictor.GluonPredictor, training_data: gluonts.dataset.Dataset, validation_data: Optional[gluonts.dataset.Dataset] = None, shuffle_buffer_length: Optional[int] = None, cache_data: bool = False) gluonts.model.predictor.Predictor[source]#
- train_model(training_data: gluonts.dataset.Dataset, validation_data: Optional[gluonts.dataset.Dataset] = None, from_predictor: Optional[gluonts.mx.model.predictor.GluonPredictor] = None, shuffle_buffer_length: Optional[int] = None, cache_data: bool = False) gluonts.mx.model.estimator.TrainOutput[source]#
- class gluonts.mx.model.estimator.TrainOutput(transformation, trained_net, predictor)[source]#
Bases:
tuple- predictor: gluonts.model.predictor.Predictor#
Alias for field number 2
- trained_net: mxnet.gluon.block.HybridBlock#
Alias for field number 1
- transformation: gluonts.transform._base.Transformation#
Alias for field number 0