openstef.data_classes package

Submodules

openstef.data_classes.data_prep module

Specifies the split function dataclass.

class openstef.data_classes.data_prep.DataPrepDataClass(**data)

Bases: BaseModel

Class that allows to specify a custom class to prepare the data (feature engineering , etc …).

arguments: str | dict[str, Any]

klass: str | type[DataPrepClass]

load(required_arguments=None)

Load the function and its arguments.

If the function and the arguments are given as strings in the instane attributes, load them as Python objects otherwise just return them from the instance attributes.

Parameters:

required_arguments (list[str]) – list of arguments the loaded class must have

Return type:

tuple[type[TypeVar(DataPrepClass)], dict[str, Any]]

Returns:

• class (type[AbstractDataPreparation])

• arguments (dict[str, Any])

model_config: ClassVar[ConfigDict] = {}

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

openstef.data_classes.model_specifications module

Specifies the dataclass for model specifications.

class openstef.data_classes.model_specifications.ModelSpecificationDataClass(**data)

Bases: BaseModel

Holds all information regarding the training procces of a specific model.

feature_modules: list | None

feature_names: list | None

hyper_params: dict | None

id: int | str

model_config: ClassVar[ConfigDict] = {}

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

openstef.data_classes.prediction_job module

Specifies the prediction job dataclass.

class openstef.data_classes.prediction_job.PredictionJobDataClass(**data)

Bases: BaseModel

Holds all information about the specific forecast that has to be made.

alternative_forecast_model_pid: int | str | None

backtest_split_func: SplitFuncDataClass | None

completeness_threshold: float

data_balancing_ratio: float | None

data_prep_class: DataPrepDataClass | None

default_modelspecs: ModelSpecificationDataClass | None

depends_on: list[int | str] | None

description: str | None

detect_non_zero_flatliner: bool

electricity_bidding_zone: BiddingZone | None

flatliner_threshold_minutes: int

forecast_type: str

get(key, default=None)

Allows to use the get functions similar to a python dict.

Return type:

Any

horizon_minutes: int | None

hub_height: float | None

id: int | str

lat: float | None

lon: float | None

minimal_table_length: int

model: str

model_config: ClassVar[ConfigDict] = {}

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

model_kwargs: dict | None

n_turbines: float | None

name: str

pipelines_to_run: list[PipelineType]

quantiles: list[float] | None

resolution_minutes: int

rolling_aggregate_features: list[AggregateFunction] | None

save_train_forecasts: bool

sid: str | None

train_components: bool | None

train_horizons_minutes: list[int] | None

train_split_func: SplitFuncDataClass | None

turbine_type: str | None

openstef.data_classes.split_function module

Specifies the split function dataclass.

class openstef.data_classes.split_function.SplitFuncDataClass(**data)

Bases: BaseModel

Class that allows to specify a custom function to generate a train, test and validation set.

arguments: str | dict[str, Any]

function: str | Callable

load(required_arguments=None)

Load the function and its arguments.

If the function and the arguments are given as strings in the instane attributes, load them as Python objects otherwise just return them from the instance attributes.

Parameters:

required_arguments (list[str]) – list of arguments the loaded function must have

Return type:

tuple[Callable, dict[str, Any]]

Returns:

• function (Callable)

• arguments (dict[str, Any])

model_config: ClassVar[ConfigDict] = {}

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

Module contents