etna.transforms.decomposition.RupturesChangePointsModel#

class RupturesChangePointsModel(change_points_model: BaseEstimator, **change_points_model_predict_params)[source]#

Bases: BaseChangePointsModelAdapter

RupturesChangePointsModel is ruptures change point models adapter.

Init RupturesChangePointsModel.

Parameters:

  • change_points_model (BaseEstimator) – model to get change points

  • change_point_model_predict_params – params for change_point_model.predict method

Methods

get_change_points(df, in_column)

Find change points within one segment.

get_change_points_intervals(df, in_column)

Find change point intervals in given dataframe and column.

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.

get_change_points(df: DataFrame, in_column: str) → List[Timestamp][source]#

Find change points within one segment.

Parameters:

  • df (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: DataFrame, in_column: str) → List[Tuple[Timestamp | int | None, Timestamp | int | None]][source]#

Find change point intervals in given dataframe and column.

Parameters:

  • df (DataFrame) – dataframe indexed with timestamp (datetime or integer)

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

Returns:

change points intervals

Return type:

List[Tuple[Timestamp | int | None, Timestamp | int | None]]

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 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 = 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()[source]#

Collect all information about etna object in dict.