ChangePointsTrendTransform#

class ChangePointsTrendTransform(in_column: str, change_points_model: BaseChangePointsModelAdapter | None = None, per_interval_model: PerIntervalModel | None = None)[source]#

Bases: ReversibleChangePointsTransform

Transform that makes a detrending of change-point intervals.

This class differs from ChangePointsLevelTransform only by default values for change_points_model and per_interval_model.

Transform divides each segment into intervals using change_points_model. Then a separate model is fitted on each interval using per_interval_model. Values predicted by the model are subtracted from each interval.

Evaluated function can be linear, mean, median, etc. Look at the signature to find out which models can be used.

Warning

This transform can suffer from look-ahead bias. For transforming data at some timestamp it uses information from the whole train part.

Init ChangePointsTrendTransform.

Parameters:

in_column (str) – name of column to apply transform to
change_points_model (BaseChangePointsModelAdapter | None) – model to get trend change points, by default ruptures.detection.Binseg in a wrapper with n_bkps=5 is used
per_interval_model (PerIntervalModel | None) – model to process intervals of segment, by default sklearn.linear_models.LinearRegression in a wrapper is used

Methods

`fit`(ts)	Fit the transform.
`fit_transform`(ts)	Fit and transform TSDataset.
`get_regressors_info`()	Return the list with regressors created by the transform.
`inverse_transform`(ts)	Inverse transform TSDataset.
`load`(path)	Load an object.
`params_to_tune`()	Get default grid for tuning hyperparameters.
`save`(path)	Save the object.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.
`transform`(ts)	Transform TSDataset inplace.

Attributes

This class stores its __init__ parameters as attributes.

fit(ts: TSDataset) → Transform[source]#

Fit the transform.

Parameters:: ts (TSDataset) – Dataset to fit the transform on.
Returns:: The fitted transform instance.
Return type:: Transform

fit_transform(ts: TSDataset) → TSDataset[source]#

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters:: ts (TSDataset) – TSDataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset

get_regressors_info() → List[str][source]#

Return the list with regressors created by the transform.

Return type:: List[str]

inverse_transform(ts: TSDataset) → TSDataset[source]#

Inverse transform TSDataset.

Apply the _inverse_transform method.

Parameters:: ts (TSDataset) – TSDataset to be inverse transformed.
Returns:: TSDataset after applying inverse transformation.
Return type:: TSDataset

classmethod load(path: Path) → Self[source]#

Load an object.

Parameters:: path (Path) – Path to load object from.
Returns:: Loaded object.
Return type:: Self

params_to_tune() → Dict[str, BaseDistribution][source]#

Get default grid for tuning hyperparameters.

If self.change_points_model is equal to default then this grid tunes parameters: change_points_model.change_points_model.model, change_points_model.n_bkps. Other parameters are expected to be set by the user.

Returns:: Grid to tune.
Return type:: Dict[str, BaseDistribution]

save(path: Path)[source]#

Save the object.

Parameters:: path (Path) – Path to save object to.

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 = 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.

transform(ts: TSDataset) → TSDataset[source]#

Transform TSDataset inplace.

Parameters:: ts (TSDataset) – Dataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset