tfma.evaluators.AnalysisTableEvaluator

Creates an Evaluator for returning Extracts data for analysis.

tfma.evaluators.AnalysisTableEvaluator(
    key: str = constants.ANALYSIS_KEY,
    run_after: str = extractor.LAST_EXTRACTOR_STAGE_NAME,
    include: Optional[Union[Iterable[str], Dict[str, Any]]] = None,
    exclude: Optional[Union[Iterable[str], Dict[str, Any]]] = None
) -> tfma.evaluators.Evaluator

If both include and exclude are None then tfma.INPUT_KEY extracts will be excluded by default.

key Name to use for key in Evaluation output.
run_after Extractor to run after (None means before any extractors).
include List or map of keys to include in output. Keys starting with '_' are automatically filtered out at write time. If a map of keys is passed then the keys and sub-keys that exist in the map will be included in the output. An empty dict behaves as a wildcard matching all keys or the value itself. Since matching on feature values is not currently supported, an empty dict must be used to represent the leaf nodes. For example: {'key1': {'key1-subkey': {} }, 'key2': {} }.
exclude List or map of keys to exclude from output. If a map of keys is passed then the keys and sub-keys that exist in the map will be excluded from the output. An empty dict behaves as a wildcard matching all keys or the value itself. Since matching on feature values is not currently supported, an empty dict must be used to represent the leaf nodes. For example: {'key1': {'key1-subkey': {} }, 'key2': {} }.

Evaluator for collecting analysis data. The output is stored under the key 'analysis'.

ValueError If both include and exclude are used.