AbstractModel

class AbstractModel

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

Interface for model with fit method.

Inherited-members

Methods

fit(ts)	Fit model.
get_model()	Get internal model/models that are used inside etna class.
load(path)	Load an object.
params_to_tune()	Get 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.

Attributes

context_size

Context size of the model.

abstract fit(ts: etna.datasets.tsdataset.TSDataset) → etna.models.base.AbstractModel

Fit model.

Parameters

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

Returns

Model after fit

Return type

etna.models.base.AbstractModel

abstract get_model() → Union[Any, Dict[str, Any]]

Get internal model/models that are 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

The result can be of two types:

• if model is multi-segment, then the result is internal model

• if model is per-segment, then the result is dictionary where key is segment and value is internal model

Return type

Union[Any, Dict[str, Any]]

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

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns

Empty grid.

Return type

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

abstract property context_size: int

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