flexmeasures.data.schemas.forecasting.pipeline

Classes

NB cli-exclusive fields are not exposed via the API (removed by make_openapi_compatible).

resolve_config(data: dict, original_data: dict | None = None, **kwargs) → dict

Resolve timing parameters, using sensible defaults and choices.

Defaults: 1. predict-period defaults to minimum of (FM planning horizon and max-forecast-horizon) only if there is a single default viewpoint. 2. max-forecast-horizon defaults to the predict-period 3. forecast-frequency defaults to minimum of (FM planning horizon, predict-period, max-forecast-horizon)

Choices: 1. If max-forecast-horizon < predict-period, we raise a ValidationError due to incomplete coverage 2. retraining-frequency becomes the maximum of (FM planning horizon and forecast-frequency, this is capped by the predict-period.

class flexmeasures.data.schemas.forecasting.pipeline.ForecastingTriggerSchema(*, only: Sequence[str] | AbstractSet[str] | None = None, exclude: Sequence[str] | AbstractSet[str] = (), many: bool | None = None, context: dict | None = None, load_only: Sequence[str] | AbstractSet[str] = (), dump_only: Sequence[str] | AbstractSet[str] = (), partial: bool | Sequence[str] | AbstractSet[str] | None = None, unknown: str | None = None)

class flexmeasures.data.schemas.forecasting.pipeline.TrainPredictPipelineConfigSchema(*, only: Sequence[str] | AbstractSet[str] | None = None, exclude: Sequence[str] | AbstractSet[str] = (), many: bool | None = None, context: dict | None = None, load_only: Sequence[str] | AbstractSet[str] = (), dump_only: Sequence[str] | AbstractSet[str] = (), partial: bool | Sequence[str] | AbstractSet[str] | None = None, unknown: str | None = None)