DeepStateModel

class DeepStateModel(ssm: etna.models.nn.deepstate.state_space_model.CompositeSSM, input_size: int, encoder_length: int, decoder_length: int, num_layers: int = 1, n_samples: int = 5, lr: float = 0.001, train_batch_size: int = 16, test_batch_size: int = 16, optimizer_params: Optional[dict] = None, trainer_params: Optional[dict] = None, train_dataloader_params: Optional[dict] = None, test_dataloader_params: Optional[dict] = None, val_dataloader_params: Optional[dict] = None, split_params: Optional[dict] = None)

Bases: etna.models.base.DeepBaseModel

DeepState model.

Init Deep State Model.

Parameters

• ssm (etna.models.nn.deepstate.state_space_model.CompositeSSM) – state Space Model of the system

• input_size (int) – size of the input feature space: features for RNN part.

• encoder_length (int) – encoder length

• decoder_length (int) – decoder length

• num_layers (int) – number of layers in RNN

• n_samples (int) – number of samples to use in predictions generation

• num_layers – number of layers

• lr (float) – learning rate

• train_batch_size (int) – batch size for training

• test_batch_size (int) – batch size for testing

• optimizer_params (Optional[dict]) – parameters for optimizer for Adam optimizer (api reference torch.optim.Adam)

• trainer_params (Optional[dict]) – Pytorch ligthning trainer parameters (api reference pytorch_lightning.trainer.trainer.Trainer)

• train_dataloader_params (Optional[dict]) – parameters for train dataloader like sampler for example (api reference torch.utils.data.DataLoader)

• test_dataloader_params (Optional[dict]) – parameters for test dataloader

• val_dataloader_params (Optional[dict]) – parameters for validation dataloader

• split_params (Optional[dict]) –

  dictionary with parameters for torch.utils.data.random_split() for train-test splitting

  • train_size: (float) value from 0 to 1 - fraction of samples to use for training

  • generator: (Optional[torch.Generator]) - generator for reproducibile train-test splitting

  • torch_dataset_size: (Optional[int]) - number of samples in dataset, in case of dataset not implementing __len__

Inherited-members

Methods

fit(ts)	Fit model.
forecast(ts, prediction_size[, ...])	Make predictions.
get_model()	Get model.
load(path)	Load an object.
params_to_tune()	Get grid for tuning hyperparameters.
predict(ts, prediction_size[, return_components])	Make predictions.
raw_fit(torch_dataset)	Fit model on torch like Dataset.
raw_predict(torch_dataset)	Make inference on torch like Dataset.
save(path)	Save the object.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.

Attributes

context_size

Context size of the model.