PatchTSModel

class PatchTSModel(decoder_length: int, encoder_length: int, patch_len: int = 4, stride: int = 1, num_layers: int = 3, hidden_size: int = 128, feedforward_size: int = 256, nhead: int = 16, lr: float = 0.001, loss: Optional[torch.nn.modules.module.Module] = None, train_batch_size: int = 128, test_batch_size: int = 128, 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

PatchTS model using PyTorch layers.

Init PatchTS model.

Parameters

• encoder_length (int) – encoder length

• decoder_length (int) –

• patch_len (int) –

• stride (int) –

• num_layers (int) –

• hidden_size (int) –

• feedforward_size (int) –

• nhead (int) –

• lr (float) –

• loss (Optional[torch.nn.Module]) –

• train_batch_size (int) –

• test_batch_size (int) –

• optimizer_params (Optional[dict]) –

• trainer_params (Optional[dict]) –

• train_dataloader_params (Optional[dict]) –

• test_dataloader_params (Optional[dict]) –

• val_dataloader_params (Optional[dict]) –

• split_params (Optional[dict]) –

decoder_length:

decoder length

patch_len:

size of patch

stride:

step of patch

num_layers:

number of layers

hidden_size:

size of the hidden state

feedforward_size:

size of feedforward layers in transformer

nhead:

number of transformer heads

lr:

learning rate

loss:

loss function, MSELoss by default

train_batch_size:

batch size for training

test_batch_size:

batch size for testing

optimizer_params:

parameters for optimizer for Adam optimizer (api reference torch.optim.Adam)

trainer_params:

Pytorch ligthning trainer parameters (api reference pytorch_lightning.trainer.trainer.Trainer)

train_dataloader_params:

parameters for train dataloader like sampler for example (api reference torch.utils.data.DataLoader)

test_dataloader_params:

parameters for test dataloader

val_dataloader_params:

parameters for validation dataloader

split_params:

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

Parameters

• decoder_length (int) –

• encoder_length (int) –

• patch_len (int) –

• stride (int) –

• num_layers (int) –

• hidden_size (int) –

• feedforward_size (int) –

• nhead (int) –

• lr (float) –

• loss (Optional[torch.nn.Module]) –

• train_batch_size (int) –

• test_batch_size (int) –

• optimizer_params (Optional[dict]) –

• trainer_params (Optional[dict]) –

• train_dataloader_params (Optional[dict]) –

• test_dataloader_params (Optional[dict]) –

• val_dataloader_params (Optional[dict]) –

• split_params (Optional[dict]) –

Methods

fit(ts)	Fit model.
forecast(ts, prediction_size[, ...])	Make predictions.
get_model()	Get model.
load(path)	Load an object.
params_to_tune()	Get default 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.

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get default grid for tuning hyperparameters.

This grid tunes parameters: num_layers, hidden_size, lr, encoder_length. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]