LinearMultiSegmentModel

class LinearMultiSegmentModel(fit_intercept: bool = True, **kwargs)[source]

Bases: etna.models.mixins.MultiSegmentModelMixin, etna.models.mixins.NonPredictionIntervalContextIgnorantModelMixin, etna.models.base.NonPredictionIntervalContextIgnorantAbstractModel

Class holding sklearn.linear_model.LinearRegression for all segments.

Notes

Target components are formed as the terms from linear regression formula.

Create instance of LinearModel with given parameters.

Parameters

fit_intercept (bool) – Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

Inherited-members

Methods

fit(ts)

Fit model.

forecast(ts[, return_components])

Make predictions.

get_model()

Get internal model that is used inside etna class.

load(path)

Load an object.

params_to_tune()

Get default grid for tuning hyperparameters.

predict(ts[, return_components])

Make predictions with using true values as autoregression context if possible (teacher forcing).

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.

Attributes

context_size

Context size of the model.

fit(ts: etna.datasets.tsdataset.TSDataset) etna.models.mixins.MultiSegmentModelMixin

Fit model.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

Returns

Model after fit

Return type

etna.models.mixins.MultiSegmentModelMixin

forecast(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False) etna.datasets.tsdataset.TSDataset

Make predictions.

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

get_model() Any

Get internal model that is used inside etna class.

Internal model is a model that is used inside etna to forecast segments, e.g. catboost.CatBoostRegressor or sklearn.linear_model.Ridge.

Returns

Internal model

Return type

Any

classmethod load(path: pathlib.Path) typing_extensions.Self

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 (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

params_to_tune() Dict[str, etna.distributions.distributions.BaseDistribution][source]

Get default grid for tuning hyperparameters.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]

predict(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False) etna.datasets.tsdataset.TSDataset

Make predictions with using true values as autoregression context if possible (teacher forcing).

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

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.

property context_size: int

Context size of the model. Determines how many history points do we ask to pass to the model.

Zero for this model.