FoldMask

class FoldMask(first_train_timestamp: Optional[Union[str, pandas._libs.tslibs.timestamps.Timestamp]], last_train_timestamp: Union[str, pandas._libs.tslibs.timestamps.Timestamp], target_timestamps: List[Union[str, pandas._libs.tslibs.timestamps.Timestamp]])[source]

Bases: etna.core.mixins.BaseMixin

Container to hold the description of the fold mask.

Fold masks are expected to be used for backtest strategy customization.

Init FoldMask.

Parameters

  • first_train_timestamp (Optional[Union[str, pandas._libs.tslibs.timestamps.Timestamp]]) – First train timestamp, the first timestamp in the dataset if None is passed

  • last_train_timestamp (Union[str, pandas._libs.tslibs.timestamps.Timestamp]) – Last train timestamp

  • target_timestamps (List[Union[str, pandas._libs.tslibs.timestamps.Timestamp]]) – List of target timestamps

Inherited-members

Methods

set_params(**params)

Return new object instance with modified parameters.

to_dict()

Collect all information about etna object in dict.

validate_on_dataset(ts, horizon)

Validate fold mask on the dataset with specified horizon.
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.

validate_on_dataset(ts: etna.datasets.tsdataset.TSDataset, horizon: int)[source]

Validate fold mask on the dataset with specified horizon.

Parameters