OneSegmentTransform¶

class OneSegmentTransform[source]¶

Bases: abc.ABC, etna.core.mixins.BaseMixin

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

Inherited-members

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.

abstract fit(df: pandas.core.frame.DataFrame)[source]¶

Fit the transform.

Should be implemented by user.

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

fit_transform(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶

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

abstract inverse_transform(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶

Inverse transform Dataframe.

Should be reimplemented in the subclasses where necessary.

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

abstract transform(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶

Transform dataframe.

Should be implemented by user

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