base¶

Classes

BaseReconciliator(target_level, source_level)

Base class to hold reconciliation methods.

class BaseReconciliator(target_level: str, source_level: str)[source]¶

Base class to hold reconciliation methods.

Init BaseReconciliator.

Parameters

target_level (str) – Level to be reconciled from the forecasts.
source_level (str) – Level to be forecasted.

aggregate(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset[source]¶

Aggregate the dataset to the source_level.

Parameters: ts (etna.datasets.tsdataset.TSDataset) – TSDataset on the level which is lower or equal to source_level.
Returns: TSDataset on the source_level.
Return type: etna.datasets.tsdataset.TSDataset

abstract fit(ts: etna.datasets.tsdataset.TSDataset) → etna.reconciliation.base.BaseReconciliator[source]¶

Fit the reconciliator parameters.

Parameters: ts (etna.datasets.tsdataset.TSDataset) – TSDataset on the level which is lower or equal to target_level, source_level.
Returns: Fitted instance of reconciliator.
Return type: etna.reconciliation.base.BaseReconciliator

reconcile(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset[source]¶

Reconcile the forecasts in the dataset.

Parameters: ts (etna.datasets.tsdataset.TSDataset) – TSDataset on the source_level.
Returns: TSDataset on the target_level.
Return type: etna.datasets.tsdataset.TSDataset

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.