LinearPerSegmentModel#

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

Bases: PerSegmentModelMixin, NonPredictionIntervalContextIgnorantModelMixin, NonPredictionIntervalContextIgnorantAbstractModel

Class holding per segment sklearn.linear_model.LinearRegression.

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).

Methods

`fit`(ts)	Fit model.
`forecast`(ts[, return_components])	Make predictions.
`get_model`()	Get internal models that are 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

This class stores its __init__ parameters as attributes.

context_size

Context size of the model.

fit(ts: TSDataset) → PerSegmentModelMixin[source]#

Fit model.

Parameters:: ts (TSDataset) – Dataset with features
Returns:: Model after fit
Return type:: PerSegmentModelMixin

forecast(ts: TSDataset, return_components: bool = False) → TSDataset[source]#

Make predictions.

Parameters:

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

Returns:

Dataset with predictions

Return type:

TSDataset

get_model() → Dict[str, Any][source]#

Get internal 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:: dictionary where key is segment and value is internal model
Return type:: Dict[str, Any]

classmethod load(path: Path) → Self[source]#

Load an object.

Parameters:: path (Path) – Path to load object from.
Returns:: Loaded object.
Return type:: Self

params_to_tune() → Dict[str, BaseDistribution][source]#

Get default grid for tuning hyperparameters.

Returns:: Grid to tune.
Return type:: Dict[str, BaseDistribution]

predict(ts: TSDataset, return_components: bool = False) → TSDataset[source]#

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

Parameters:

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

Returns:

Dataset with predictions

Return type:

TSDataset

save(path: Path)[source]#

Save the object.

Parameters:: path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

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 (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

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()[source]#: Collect all information about etna object in dict.

property context_size: int[source]#

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

Zero for this model.