NonPredictionIntervalContextIgnorantAbstractModel¶

class NonPredictionIntervalContextIgnorantAbstractModel[source]¶

Bases: etna.models.base.AbstractModel

Interface for models that don’t support prediction intervals and don’t need context for prediction.

Inherited-members

Methods

`fit`(ts)	Fit model.
`forecast`(ts[, return_components])	Make predictions.
`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.
`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.

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 forecast(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False) → etna.datasets.tsdataset.TSDataset[source]¶

Make predictions.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features
return_components (bool) – If True additionally returns forecast components

Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

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]]

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]¶

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns: Empty grid.
Return type: Dict[str, etna.distributions.distributions.BaseDistribution]

abstract predict(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False) → etna.datasets.tsdataset.TSDataset[source]¶

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

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features
return_components (bool) – If True additionally returns prediction components

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.