teehr.ProbabilisticMetrics#

class teehr.ProbabilisticMetrics[source]#

Bases: object

Define and customize probalistic metrics.

Notes

Probabilistic metrics compare a value against a distribution of predicted values, such as ensemble forecasts. Available probabilistic metrics include:

  • CRPS (Continuous Ranked Probability Score)

Methods

class CRPS(*, return_type: str | ~pyspark.sql.types.ArrayType | ~pyspark.sql.types.MapType = None, unpack_results: bool = False, unpack_function: ~typing.Callable = None, estimator: ~teehr.models.metrics.basemodels.CRPSEstimators = 'pwm', backend: str = 'numba', output_field_name: str = 'mean_crps_ensemble', func: ~typing.Callable = <function create_crps_func>, summary_func: ~typing.Callable | None = None, input_field_names: str | ~teehr.models.str_enum.StrEnum | ~typing.List[str | ~teehr.models.str_enum.StrEnum] = ['primary_value', 'secondary_value', 'value_time'], attrs: ~typing.Dict = {'category': MetricCategories.Probabilistic, 'display_name': 'Continuous Ranked Probability Score - Ensemble', 'optimal_value': 0.0, 'short_name': 'crps_ensemble', 'value_range': [None, None]})#

Bases: ProbabilisticBasemodel

Continous Ranked Probability Score - Ensemble.

Parameters:

  • estimator (str) – CRPS estimator, can be (“pwm”, “nrg”, or “fair”). Default is “pwm”.

  • backend (str) – The backend to use, by default “numba”. Can be (“numba” or “numpy”).

  • summary_func (Callable) – The function to apply to the results, by default np.mean.

  • output_field_name (str) – The output field name, by default “mean_crps_ensemble”.

  • func (Callable) – The function to apply to the data, by default probabilistic_funcs.ensemble_crps().

  • input_field_names (Union[str, StrEnum, List[Union[str, StrEnum]]]) – The input field names, by default [“primary_value”, “secondary_value”, “value_time”].

  • attrs (Dict) – The static attributes for the metric.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'validate_assignment': True}#

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