LSTMModel

class pytorch_forecasting.models.basic_rnn.LSTMModel(cell_type: str = 'LSTM', hidden_size: int = 10, rnn_layers: int = 2, dropout: float = 0.1, static_categoricals: List[str] = [], static_reals: List[str] = [], time_varying_categoricals_encoder: List[str] = [], time_varying_categoricals_decoder: List[str] = [], categorical_groups: Dict[str, List[str]] = {}, time_varying_reals_encoder: List[str] = [], time_varying_reals_decoder: List[str] = [], embedding_sizes: Dict[str, Tuple[int, int]] = {}, embedding_paddings: List[str] = [], embedding_labels: Dict[str, numpy.ndarray] = {}, x_reals: List[str] = [], x_categoricals: List[str] = [], n_validation_samples: Optional[int] = None, n_plotting_samples: Optional[int] = None, target: Optional[Union[str, List[str]]] = None, loss: Optional[pytorch_forecasting.metrics.MultiHorizonMetric] = None, logging_metrics: Optional[torch.nn.modules.container.ModuleList] = None, **kwargs)[source]

Bases: pytorch_forecasting.models.base_model.AutoRegressiveBaseModelWithCovariates

Basic RNN network.

Parameters

  • cell_type (str, optional) – Recurrent cell type [“LSTM”, “GRU”]. Defaults to “LSTM”.

  • hidden_size (int, optional) – hidden recurrent size - the most important hyperparameter along with rnn_layers. Defaults to 10.

  • rnn_layers (int, optional) – Number of RNN layers - important hyperparameter. Defaults to 2.

  • dropout (float, optional) – Dropout in RNN layers. Defaults to 0.1.

  • static_categoricals – integer of positions of static categorical variables

  • static_reals – integer of positions of static continuous variables

  • time_varying_categoricals_encoder – integer of positions of categorical variables for encoder

  • time_varying_categoricals_decoder – integer of positions of categorical variables for decoder

  • time_varying_reals_encoder – integer of positions of continuous variables for encoder

  • time_varying_reals_decoder – integer of positions of continuous variables for decoder

  • categorical_groups – dictionary where values are list of categorical variables that are forming together a new categorical variable which is the key in the dictionary

  • x_reals – order of continuous variables in tensor passed to forward function

  • x_categoricals – order of categorical variables in tensor passed to forward function

  • embedding_sizes – dictionary mapping (string) indices to tuple of number of categorical classes and embedding size

  • embedding_paddings – list of indices for embeddings which transform the zero’s embedding to a zero vector

  • embedding_labels – dictionary mapping (string) indices to list of categorical labels

  • n_validation_samples (int, optional) – Number of samples to use for calculating validation metrics. Defaults to None, i.e. no sampling at validation stage and using “mean” of distribution for logging metrics calculation.

  • n_plotting_samples (int, optional) – Number of samples to generate for plotting predictions during training. Defaults to n_validation_samples if not None or 100 otherwise.

  • target (str, optional) – Target variable or list of target variables. Defaults to None.

  • loss (DistributionLoss, optional) – Distribution loss function. Keep in mind that each distribution loss function might have specific requirements for target normalization. Defaults to NormalDistributionLoss.

  • logging_metrics (nn.ModuleList, optional) – Metrics to log during training. Defaults to nn.ModuleList([SMAPE(), MAE(), RMSE(), MAPE(), MASE()]).

Methods

decode(x, hidden_state)

encode(x)

forward(x)

Network forward pass.

Attributes

target_position

forward(x: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor][source]

Network forward pass.

Parameters

x (Dict[str, Union[torch.Tensor, List[torch.Tensor]]]) – network input (x as returned by the dataloader). See to_dataloader() method that returns a tuple of x and y. This function expects x.

Returns

network outputs / dictionary of tensors or list

of tensors. The minimal required entries in the dictionary are (and shapes in brackets):

  • prediction (batch_size x n_decoder_time_steps x n_outputs or list thereof with each entry for a different target): unscaled predictions that can be fed to metric. List of tensors if multiple targets are predicted at the same time.

  • target_scale (batch_size x scale_size or list thereof with each entry for a different target): target scales that allow rescaling the predictions into the real space. The scale can mostly be directly taken from x, i.e. target_scale=x["target_scale"]

Return type

Dict[str, Union[torch.Tensor, List[torch.Tensor]]]

basic_rnn deepar