gluonts.torch.model.wavenet.estimator module#

class gluonts.torch.model.wavenet.estimator.WaveNetEstimator(freq: str, prediction_length: int, num_bins: int = 1024, num_residual_channels: int = 24, num_skip_channels: int = 32, dilation_depth: Optional[int] = None, num_stacks: int = 1, temperature: float = 1.0, num_feat_dynamic_real: int = 0, num_feat_static_cat: int = 0, num_feat_static_real: int = 0, cardinality: List[int] = [1], seasonality: Optional[int] = None, embedding_dimension: int = 5, use_log_scale_feature: bool = True, time_features: Optional[List[Callable[[PeriodIndex], ndarray]]] = None, lr: float = 0.001, weight_decay: float = 1e-08, train_sampler: Optional[InstanceSampler] = None, validation_sampler: Optional[InstanceSampler] = None, batch_size: int = 32, num_batches_per_epoch: int = 50, num_parallel_samples: int = 100, negative_data: bool = False, trainer_kwargs: Optional[Dict[str, Any]] = None)[source]#

Bases: PyTorchLightningEstimator

create_lightning_module() → LightningModule[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:: pl.LightningModule

create_predictor(transformation: Transformation, module: WaveNetLightningModule) → PyTorchPredictor[source]#

Create and return a predictor object.

Parameters:

transformation – Transformation to be applied to data before it goes into the model.
module – A trained pl.LightningModule object.

Returns:

A predictor wrapping a nn.Module used for inference.

Return type:

Predictor

create_training_data_loader(data: Dataset, module: WaveNetLightningModule, shuffle_buffer_length: Optional[int] = None, **kwargs) → Iterable[source]#

Create a data loader for training purposes.

Parameters:

data – Dataset from which to create the data loader.
module – The pl.LightningModule object that will receive the batches from the data loader.

Returns:

The data loader, i.e. and iterable over batches of data.

Return type:

Iterable

create_transformation() → 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:: Transformation

create_validation_data_loader(data: Dataset, module: WaveNetLightningModule, **kwargs) → Iterable[source]#

Create a data loader for validation purposes.

Parameters:

data – Dataset from which to create the data loader.
module – The pl.LightningModule object that will receive the batches from the data loader.

Returns:

The data loader, i.e. and iterable over batches of data.

Return type:

Iterable