ConstantPerIntervalModel

class ConstantPerIntervalModel

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

ConstantPerIntervalModel gives a constant prediction it was fitted with.

Init ConstantPerIntervalModel.

Inherited-members

Methods

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

fit(features: numpy.ndarray, target: numpy.ndarray, *args, **kwargs) → etna.transforms.decomposition.change_points_based.per_interval_models.constant.ConstantPerIntervalModel

Fit constant model.

Parameters

• features (numpy.ndarray) – features to fit model, will be ignored

• target (numpy.ndarray) – target to fit model, will be ignored

Returns

fitted ConstantPerIntervalModel

Return type

self

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

Predict with constant.

Parameters

features (numpy.ndarray) – features to make prediction for

Returns

constant array of features’ len

Return type

prediction

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.