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observation_table: Union[ObservationTable, DataFrame],
serving_names_mapping: Optional[Dict[str, str]]=None,
skip_entity_validation_checks: bool=False
) -> DataFrame


Returns a DataFrame with target values for analysis, model training, or evaluation. The target request data consists of an observation set that combines points-in-time and key values of the primary entity from the target.

Associated serving entities can also be utilized.


  • observation_table: Union[ObservationTable, DataFrame]
    Observation set DataFrame or ObservationTable object, which combines points-in-time and values of the target primary entity or its descendant (serving entities). The column containing the point-in-time values should be named POINT_IN_TIME, while the columns representing entity values should be named using accepted serving names for the entity.

  • serving_names_mapping: Optional[Dict[str, str]]
    Optional serving names mapping if the training events table has different serving name columns than those defined in Entities, mapping from original serving name to new name.

  • skip_entity_validation_checks: bool
    default: False
    Whether to skip entity validation checks.


  • DataFrame
    Materialized target.

Note: POINT_IN_TIME values will be converted to UTC time.


>>> target = catalog.get_target("target")
>>> target.compute_targets(observation_table)