featurebyte.Target.compute_target_table¶
compute_target_table(
observation_table: Union[ObservationTable, DataFrame],
observation_table_name: str,
serving_names_mapping: Optional[Dict[str, str]]=None,
skip_entity_validation_checks: bool=False
) -> ObservationTableDescription¶
Materialize feature list using an observation table asynchronously. The targets will be materialized into a target table.
Parameters¶
- observation_table: Union[ObservationTable, DataFrame]
Observation set withPOINT_IN_TIME
and serving names columns. This can be either an ObservationTable of a pandas DataFrame. - observation_table_name: str
Name of the observation table to be created with the target values - serving_names_mapping: Optional[Dict[str, str]]
Optional serving names mapping if the training events table has different serving name. - skip_entity_validation_checks: bool
default: False
Whether to skip entity validation checks
Returns¶
- ObservationTable