gluonts.torch.model.lag_tst package#
- class gluonts.torch.model.lag_tst.LagTSTEstimator(freq: str, prediction_length: int, context_length: Optional[int] = None, d_model: int = 32, nhead: int = 4, dim_feedforward: int = 128, lags_seq: Optional[List[int]] = None, dropout: float = 0.1, activation: str = 'relu', norm_first: bool = False, num_encoder_layers: int = 2, lr: float = 0.001, weight_decay: float = 1e-08, scaling: Optional[str] = 'mean', distr_output: gluonts.torch.distributions.output.Output = gluonts.torch.distributions.studentT.StudentTOutput(beta=0.0), batch_size: int = 32, num_batches_per_epoch: int = 50, trainer_kwargs: Optional[Dict[str, Any]] = None, train_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, validation_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None)[source]#
Bases:
gluonts.torch.model.estimator.PyTorchLightningEstimator
An estimator training the LagTST model for forecasting.
This class is uses the model defined in
SimpleFeedForwardModel
, and wraps it into aLagTSTLightningModule
for training purposes: training is performed using PyTorch Lightning’spl.Trainer
class.- Parameters
freq – Frequency of the data to train on and predict.
prediction_length (int) – Length of the prediction horizon.
context_length – Number of time steps prior to prediction time that the model takes as inputs (default:
10 * prediction_length
).lags_seq – Indices of the lagged target values to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq).
d_model – Size of hidden layers in the Transformer encoder.
nhead – Number of attention heads in the Transformer encoder.
dim_feedforward – Size of hidden layers in the Transformer encoder.
dropout – Dropout probability in the Transformer encoder.
activation – Activation function in the Transformer encoder.
norm_first – Whether to apply normalization before or after the attention.
num_encoder_layers – Number of layers in the Transformer encoder.
lr – Learning rate (default:
1e-3
).weight_decay – Weight decay regularization parameter (default:
1e-8
).scaling – Scaling parameter can be “mean”, “std” or None.
distr_output – Distribution to use to evaluate observations and sample predictions (default: StudentTOutput()).
batch_size – The size of the batches to be used for training (default: 32).
num_batches_per_epoch –
- Number of batches to be processed in each training epoch
(default: 50).
trainer_kwargs – Additional arguments to provide to
pl.Trainer
for construction.train_sampler – Controls the sampling of windows during training.
validation_sampler – Controls the sampling of windows during validation.
- create_lightning_module() lightning.pytorch.core.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: gluonts.transform._base.Transformation, module) gluonts.torch.model.predictor.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
- create_training_data_loader(data: gluonts.dataset.Dataset, module: gluonts.torch.model.lag_tst.lightning_module.LagTSTLightningModule, 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() 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, module: gluonts.torch.model.lag_tst.lightning_module.LagTSTLightningModule, **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
- lead_time: int#
- prediction_length: int#
- class gluonts.torch.model.lag_tst.LagTSTLightningModule(model_kwargs: dict, lr: float = 0.001, weight_decay: float = 1e-08)[source]#
Bases:
lightning.pytorch.core.module.LightningModule
A
pl.LightningModule
class that can be used to train aLagTSTModel
with PyTorch Lightning.This is a thin layer around a (wrapped)
LagTSTModel
object, that exposes the methods to evaluate training and validation loss.- Parameters
model_kwargs – Keyword arguments to construct the
LagTSTModel
to be trained.loss – Loss function to be used for training.
lr – Learning rate.
weight_decay – Weight decay regularization parameter.
- class gluonts.torch.model.lag_tst.LagTSTModel(prediction_length: int, context_length: int, freq: str, d_model: int, nhead: int, dim_feedforward: int, dropout: float, activation: str, norm_first: bool, num_encoder_layers: int, scaling: str, lags_seq: Optional[List[int]] = None, distr_output=gluonts.torch.distributions.studentT.StudentTOutput(beta=0.0))[source]#
Bases:
torch.nn.modules.module.Module
Module implementing the LagTST model for forecasting.
- Parameters
prediction_length – Number of time points to predict.
context_length – Number of time steps prior to prediction time that the model.
distr_output – Distribution to use to evaluate observations and sample predictions. Default:
StudentTOutput()
.
- describe_inputs(batch_size=1) gluonts.model.inputs.InputSpec [source]#
- forward(past_target: torch.Tensor, past_observed_values: torch.Tensor) Tuple[Tuple[torch.Tensor, ...], torch.Tensor, torch.Tensor] [source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- loss(past_target: torch.Tensor, past_observed_values: torch.Tensor, future_target: torch.Tensor, future_observed_values: torch.Tensor) torch.Tensor [source]#
- training: bool#