MLPModel

class MLPModel(input_size: int, decoder_length: int, hidden_size: List, encoder_length: int = 0, lr: float = 0.001, loss: Optional[torch.nn.modules.module.Module] = None, 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)[source]

Bases: etna.models.base.DeepBaseModel

MLPModel.

Init MLP model.

Parameters
  • input_size (int) – size of the input feature space: target plus extra features

  • decoder_length (int) – decoder length

  • hidden_size (List) – List of sizes of the hidden states

  • encoder_length (int) – encoder length

  • lr (float) – learning rate

  • loss (Optional[torch.nn.Module]) – loss function, MSELoss by default

  • 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 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.

fit(ts: etna.datasets.tsdataset.TSDataset) etna.models.base.DeepBaseModel

Fit model.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset with features

Returns

Model after fit

Return type

etna.models.base.DeepBaseModel

forecast(ts: etna.datasets.tsdataset.TSDataset, prediction_size: int, return_components: bool = False) etna.datasets.tsdataset.TSDataset

Make predictions.

This method will make autoregressive predictions.

Parameters
  • ts (etna.datasets.tsdataset.TSDataset) – Dataset with features and expected decoder length for context

  • prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context.

  • return_components (bool) – If True additionally returns forecast components

Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

get_model() etna.models.base.DeepBaseNet

Get model.

Returns

Torch Module

Return type

etna.models.base.DeepBaseNet

classmethod load(path: pathlib.Path) typing_extensions.Self

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters

path (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

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

Get default grid for tuning hyperparameters.

This grid tunes parameters: lr, hidden_size.i where i from 0 to len(hidden_size) - 1. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

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

predict(ts: etna.datasets.tsdataset.TSDataset, prediction_size: int, return_components: bool = False) etna.datasets.tsdataset.TSDataset

Make predictions.

This method will make predictions using true values instead of predicted on a previous step. It can be useful for making in-sample forecasts.

Parameters
  • ts (etna.datasets.tsdataset.TSDataset) – Dataset with features and expected decoder length for context

  • prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context.

  • return_components (bool) – If True additionally returns prediction components

Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

raw_fit(torch_dataset: torch.utils.data.dataset.Dataset) etna.models.base.DeepBaseModel

Fit model on torch like Dataset.

Parameters

torch_dataset (torch.utils.data.dataset.Dataset) – Torch like dataset for model fit

Returns

Model after fit

Return type

etna.models.base.DeepBaseModel

raw_predict(torch_dataset: torch.utils.data.dataset.Dataset) Dict[Tuple[str, str], numpy.ndarray]

Make inference on torch like Dataset.

Parameters

torch_dataset (torch.utils.data.dataset.Dataset) – Torch like dataset for model inference

Returns

Dictionary with predictions

Return type

Dict[Tuple[str, str], numpy.ndarray]

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) etna.core.mixins.TMixin

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters
  • **params – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )
to_dict()

Collect all information about etna object in dict.

property context_size: int

Context size of the model.