base

Classes

BaseChangePointsModelAdapter()

BaseChangePointsModelAdapter is the base class for change point models adapters.
class BaseChangePointsModelAdapter[source]

BaseChangePointsModelAdapter is the base class for change point models adapters.

abstract get_change_points(df: pandas.core.frame.DataFrame, in_column: str) → List[pandas._libs.tslibs.timestamps.Timestamp][source]

Find change points within one segment.

Parameters

  • df (pandas.core.frame.DataFrame) – dataframe indexed with timestamp

  • in_column (str) – name of column to get change points

Returns

change point timestamps

Return type

change points

get_change_points_intervals(df: pandas.core.frame.DataFrame, in_column: str) → List[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp]][source]

Find change point intervals in given dataframe and column.

Parameters

  • df (pandas.core.frame.DataFrame) – dataframe indexed with timestamp

  • in_column (str) – name of column to get change points

Returns

change points intervals

Return type

List[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp]]

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.