etna.transforms.decomposition.SklearnRegressionPerIntervalModel#

class SklearnRegressionPerIntervalModel(model: RegressorMixin | None = None)[source]#

Bases: PerIntervalModel

SklearnRegressionPerIntervalModel applies PerIntervalModel interface for sklearn-like regression models.

Init SklearnPerIntervalModel.

Parameters:: model (RegressorMixin | None) – model with sklearn interface to use for interval processing

Methods

`fit`(features, target, args, *kwargs)	Fit model with given features and targets.
`predict`(features, args, *kwargs)	Make prediction for given features.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.

Attributes

This class stores its __init__ parameters as attributes.

fit(features: ndarray, target: ndarray, *args, **kwargs) → SklearnRegressionPerIntervalModel[source]#

Fit model with given features and targets.

Parameters:

features (ndarray) – features to fit model with
target (ndarray) – targets to fit model

Returns:

fitted SklearnRegressionPerIntervalModel

Return type:

self

predict(features: ndarray, *args, **kwargs) → ndarray[source]#

Make prediction for given features.

Parameters:: features (ndarray) – features to make prediction for
Returns:: model’s prediction for given features
Return type:: prediction

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.