etna.transforms.OneSegmentTransform#

class OneSegmentTransform[source]#

Bases: ABC, BaseMixin

Base class to create one segment transforms to apply to data.

Methods

`fit`(df)	Fit the transform.
`fit_transform`(df)	Fit and transform Dataframe.
`inverse_transform`(df)	Inverse transform Dataframe.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.
`transform`(df)	Transform dataframe.

Attributes

This class stores its __init__ parameters as attributes.

abstract fit(df: DataFrame)[source]#

Fit the transform.

Should be implemented by user.

Parameters:: df (DataFrame) – Dataframe in etna long format.

fit_transform(df: DataFrame) → DataFrame[source]#

Fit and transform Dataframe.

May be reimplemented. But it is not recommended.

Parameters:: df (DataFrame) – Dataframe in etna long format to transform.
Returns:: Transformed Dataframe.
Return type:: DataFrame

abstract inverse_transform(df: DataFrame) → DataFrame[source]#

Inverse transform Dataframe.

Should be reimplemented in the subclasses where necessary.

Parameters:: df (DataFrame) – Dataframe in etna long format to be inverse transformed.
Returns:: Dataframe after applying inverse transformation.
Return type:: DataFrame

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.

abstract transform(df: DataFrame) → DataFrame[source]#

Transform dataframe.

Should be implemented by user

Parameters:: df (DataFrame) – Dataframe in etna long format.
Returns:: Transformed Dataframe in etna long format.
Return type:: DataFrame