etna.transforms.ChangePointsSegmentationTransform#
- class ChangePointsSegmentationTransform(in_column: str, change_points_model: BaseChangePointsModelAdapter | None = None, out_column: str | None = None)[source]#
Bases:
IrreversibleChangePointsTransform
Transform that makes label encoding of change-point intervals.
Transform divides each segment into intervals using
change_points_model
. Each interval is enumerated based on its index from the start of the segment. New column is created with number of interval for each timestamp.Warning
This transform can suffer from look-ahead bias. For transforming data at some timestamp it uses information from the whole train part.
Init ChangePointsSegmentationTransform.
- Parameters:
in_column (str) – name of column to fit change point model
change_points_model (BaseChangePointsModelAdapter | None) – model to get change points, by default
ruptures.detection.binseg.Binseg
in a wrapper withn_bkps=5
is usedout_column (str | None) – result column name. If not given use
self.__repr__()
Methods
fit
(ts)Fit the transform.
fit_transform
(ts)Fit and transform TSDataset.
Return the list with regressors created by the transform.
Inverse transform TSDataset.
load
(path)Load an object.
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.out_column
- 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.
- classmethod load(path: Path) Self [source]#
Load an object.
Warning
This method uses
dill
module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.- 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:
- 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 aPipeline
.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, )