gluonts.mx.model.estimator module¶
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class
gluonts.mx.model.estimator.
GluonEstimator
(*, trainer: gluonts.mx.trainer._base.Trainer, batch_size: int = 32, lead_time: int = 0, dtype: gluonts.core.component.DType = <class 'numpy.float32'>)[source]¶ Bases:
gluonts.model.estimator.Estimator
An 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.
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create_predictor
(transformation: gluonts.transform._base.Transformation, trained_network: mxnet.gluon.block.HybridBlock) → gluonts.model.predictor.Predictor[source]¶ Create and return a predictor object.
- Returns
A predictor wrapping a HybridBlock used for inference.
- Return type
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create_training_data_loader
(data: Iterable[Dict[str, Any]], **kwargs) → gluonts.dataset.loader.DataLoader[source]¶
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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
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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
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create_validation_data_loader
(data: Iterable[Dict[str, Any]], **kwargs) → gluonts.dataset.loader.DataLoader[source]¶
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freq
= None¶
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classmethod
from_hyperparameters
(**hyperparameters) → gluonts.mx.model.estimator.GluonEstimator[source]¶
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lead_time
= None¶
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prediction_length
= None¶
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train
(training_data: Optional[Iterable[Dict[str, Any]]] = None, validation_data: Optional[Iterable[Dict[str, Any]]] = None, num_workers: Optional[int] = None, num_prefetch: Optional[int] = 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
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train_model
(training_data: Optional[Iterable[Dict[str, Any]]] = None, validation_data: Optional[Iterable[Dict[str, Any]]] = None, num_workers: Optional[int] = None, num_prefetch: Optional[int] = None, shuffle_buffer_length: Optional[int] = None, cache_data: bool = False) → gluonts.mx.model.estimator.TrainOutput[source]¶
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