etna.pipeline.FoldMask#
- class FoldMask(first_train_timestamp: str | Timestamp | None, last_train_timestamp: str | Timestamp, target_timestamps: List[str | Timestamp])[source]#
Bases:
BaseMixinContainer to hold the description of the fold mask.
Fold masks are expected to be used for backtest strategy customization.
Init FoldMask.
- Parameters:
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.
Attributes
This class stores its
__init__parameters as attributes.- set_params(**params: dict) Self[source]#
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
modelin aPipeline.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, )