PerIntervalModel

class PerIntervalModel

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

Class to handle intervals in change point based transforms.

PerIntervalModel is a class to process intervals between change points in change_points_based transforms.

Inherited-members

Methods

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

abstract fit(features: numpy.ndarray, target: numpy.ndarray, *args, **kwargs) → etna.transforms.decomposition.change_points_based.per_interval_models.base.PerIntervalModel

Fit per interval model with given params.

Parameters

• features (numpy.ndarray) –

• target (numpy.ndarray) –

Return type

etna.transforms.decomposition.change_points_based.per_interval_models.base.PerIntervalModel

abstract predict(features: numpy.ndarray, *args, **kwargs) → numpy.ndarray

Make prediction with per interval model.

Parameters

features (numpy.ndarray) –

Return type

numpy.ndarray

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.