GBLinearHyperParams#

class openstef_models.models.forecasting.gblinear_forecaster.GBLinearHyperParams(**data: Any) → None[source]

Bases: HyperParams

Hyperparameter configuration for GBLinear forecaster.

Parameters:

data (Any)

n_steps: Annotated[int]
updater: Annotated[str]
learning_rate: Annotated[float]
objective: Union[TypeAliasType, Literal['reg:quantileerror']]
evaluation_metric: TypeAliasType
reg_alpha: Annotated[float]
reg_lambda: Annotated[float]
feature_selector: Annotated[str]
top_k: int
random_state: int | None
early_stopping_rounds: int | None
classmethod forecaster_class() → type[GBLinearForecaster][source]

Forecaster class for these hyperparams.

Return type:

type[GBLinearForecaster]

Returns:

Forecaster class associated with this configuration.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': False, 'extra': 'ignore', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) – The BaseModel instance.

  • context (Any) – The context.

  • self

  • context

Return type:

None