RMAEPeakHoursProvider#

class openstef_beam.evaluation.metric_providers.RMAEPeakHoursProvider(**data: Any) → None[source]

Bases: MetricProvider

Provides Relative Mean Absolute Error metrics for peak hours only (8:00-20:00).

Normalizes MAE using specified quantile bounds but only considers data points between 8:00 and 20:00 hours.

Parameters:

data (Any)

property metric_names: frozenset[str]

Declared metric names that this provider produces.

Override in subclasses to enable eager metric-name validation (e.g. in the hyperparameter tuner).

lower_quantile: float
upper_quantile: float
start_peak_hours: int
end_peak_hours: int
__call__(subset: ForecastDataset) → dict[Quantile | Literal['global'], dict[str, Annotated[float, BeforeValidator(func=_convert_none_to_nan, json_schema_input_type=PydanticUndefined)]]][source]

Process an evaluation subset and return metrics.

Extracts predictions and ground truth from the subset, then computes metrics for all relevant quantiles.

Parameters:

  • subset (ForecastDataset) – Evaluation subset containing predictions and ground truth data.

  • subset

Returns:

QuantileMetricsDict mapping peak/off-peak periods to computed metric values.

Return type:

dict[Union[Quantile, Literal['global']], dict[str, float]]

compute_deterministic(y_true: ndarray[tuple[Any, ...], dtype[floating]], y_pred: ndarray[tuple[Any, ...], dtype[floating]], quantile: float) → dict[str, Annotated[float, BeforeValidator(func=_convert_none_to_nan, json_schema_input_type=PydanticUndefined)]][source]

Compute metrics for a single quantile prediction.

Must be implemented by subclasses that provide deterministic metrics (per quantile).

Parameters:
Return type:

dict[str, float]

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].