PytorchForecastingDatasetBuilder

class PytorchForecastingDatasetBuilder(max_encoder_length: int = 30, min_encoder_length: int | None = None, min_prediction_idx: int | None = None, min_prediction_length: int | None = None, max_prediction_length: int = 1, static_categoricals: List[str] | None = None, static_reals: List[str] | None = None, time_varying_known_categoricals: List[str] | None = None, time_varying_known_reals: List[str] | None = None, time_varying_unknown_categoricals: List[str] | None = None, time_varying_unknown_reals: List[str] | None = None, variable_groups: Dict[str, List[int]] | None = None, constant_fill_strategy: Dict[str, str | float | int | bool] | None = None, allow_missing_timesteps: bool = True, lags: Dict[str, List[int]] | None = None, add_relative_time_idx: bool = True, add_target_scales: bool = True, add_encoder_length: bool | str = True, target_normalizer: TorchNormalizer | NaNLabelEncoder | EncoderNormalizer | str | List[TorchNormalizer | NaNLabelEncoder | EncoderNormalizer] | Tuple[TorchNormalizer | NaNLabelEncoder | EncoderNormalizer] = 'auto', categorical_encoders: Dict[str, NaNLabelEncoder] | None = None, scalers: Dict[str, StandardScaler | RobustScaler | TorchNormalizer | EncoderNormalizer] | None = None)

Bases: BaseMixin

Builder for PytorchForecasting dataset.

Init dataset builder.

Parameters here is used for initialization of pytorch_forecasting.data.timeseries.TimeSeriesDataSet object.

Methods

create_inference_dataset(ts, horizon)	Create inference dataset.
create_train_dataset(ts)	Create train dataset.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.

Attributes

This class stores its __init__ parameters as attributes.

Parameters:

• max_encoder_length (int) –

• min_encoder_length (int | None) –

• min_prediction_idx (int | None) –

• min_prediction_length (int | None) –

• max_prediction_length (int) –

• static_categoricals (List[str] | None) –

• static_reals (List[str] | None) –

• time_varying_known_categoricals (List[str] | None) –

• time_varying_known_reals (List[str] | None) –

• time_varying_unknown_categoricals (List[str] | None) –

• time_varying_unknown_reals (List[str] | None) –

• variable_groups (Dict[str, List[int]] | None) –

• constant_fill_strategy (Dict[str, str | float | int | bool] | None) –

• allow_missing_timesteps (bool) –

• lags (Dict[str, List[int]] | None) –

• add_relative_time_idx (bool) –

• add_target_scales (bool) –

• add_encoder_length (bool | str) –

• target_normalizer (TorchNormalizer | NaNLabelEncoder | EncoderNormalizer | str | List[TorchNormalizer | NaNLabelEncoder | EncoderNormalizer] | Tuple[TorchNormalizer | NaNLabelEncoder | EncoderNormalizer]) –

• categorical_encoders (Dict[str, NaNLabelEncoder] | None) –

• scalers (Dict[str, StandardScaler | RobustScaler | TorchNormalizer | EncoderNormalizer] | None) –

create_inference_dataset(ts: TSDataset, horizon: int) → TimeSeriesDataSet

Create inference dataset.

This method should be used only after create_train_dataset that is used during model training.

Parameters:

• ts (TSDataset) – Time series dataset.

• horizon (int) – Size of prediction to make.

Raises:

ValueError: – if method was used before create_train_dataset

Return type:

TimeSeriesDataSet

create_train_dataset(ts: TSDataset) → TimeSeriesDataSet

Create train dataset.

Parameters:

ts (TSDataset) – Time series dataset.

Return type:

TimeSeriesDataSet

set_params(**params: dict) → Self

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 (dict) – Estimator parameters

Returns:

New instance with changed parameters

Return type:

Self

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.