_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

in_column (str) –
out_column (str) –
change_points_model (etna.transforms.decomposition.change_points_based.change_points_models.base.BaseChangePointsModelAdapter) –

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