_OneSegmentChangePointsSegmentationTransform

class _OneSegmentChangePointsSegmentationTransform(in_column: str, out_column: str, change_points_model: etna.transforms.decomposition.change_points_based.change_points_models.base.BaseChangePointsModelAdapter)[source]

Bases: etna.transforms.decomposition.change_points_based.base._OneSegmentChangePointsTransform

_OneSegmentChangePointsSegmentationTransform make label encoder to change points.

Init _OneSegmentChangePointsSegmentationTransform. :param in_column: name of column to apply transform to :param out_column: result column name. If not given use self.__repr__() :param change_points_model: model to get change points

Inherited-members

Parameters

Methods

fit(df)

Fit transform.

fit_transform(df)

Fit and transform Dataframe.

inverse_transform(df)

Split df to intervals of stable trend according to previous change point detection and add trend to each one.

set_params(**params)

Return new object instance with modified parameters.

to_dict()

Collect all information about etna object in dict.

transform(df)

Transform data from df.

fit(df: pandas.core.frame.DataFrame) etna.transforms.decomposition.change_points_based.base._OneSegmentChangePointsTransform

Fit transform. Get no-changepoints intervals with change_points_model and fit per_interval_model on the intervals.

Parameters

df (pandas.core.frame.DataFrame) – dataframe to process

Returns

fitted _OneSegmentChangePointsTransform

Return type

self

fit_transform(df: pandas.core.frame.DataFrame) pandas.core.frame.DataFrame

Fit and transform Dataframe.

May be reimplemented. But it is not recommended.

Parameters

df (pandas.core.frame.DataFrame) – Dataframe in etna long format to transform.

Returns

Transformed Dataframe.

Return type

pandas.core.frame.DataFrame

inverse_transform(df: pandas.core.frame.DataFrame) pandas.core.frame.DataFrame

Split df to intervals of stable trend according to previous change point detection and add trend to each one.

Parameters

df (pandas.core.frame.DataFrame) – one segment dataframe to turn trend back

Returns

df – df with restored trend in in_column

Return type

pd.DataFrame

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.

transform(df: pandas.core.frame.DataFrame) pandas.core.frame.DataFrame

Transform data from df.

Parameters

df (pandas.core.frame.DataFrame) – dataframe to apply transformation to

Returns

dataframe with applied transformation

Return type

transformed_df