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tfma.metrics.StandardMetricInputs

Standard inputs used by most metric computations.

tfma.metrics.StandardMetricInputs(
    extracts: Optional[tfma.types.Extracts] = None,
    **kwargs
)

StandardMetricInputs is a wrapper around Extracts where only the extracts keys used by one or more ExtractsPreprocessors will be present.

extracts Reference to existing extracts to use.
**kwargs Name/value pairs to create new extracts from. Only one of either extracts or kwargs should be used.

attributions Returns tfma.ATTRIBUTIONS_KEY extract.
example_weight Same as example_weights (DEPRECATED - use example_weights).
example_weights Returns tfma.EXAMPLE_WEIGHTS_KEY extract.
features Returns tfma.FEATURES_KEY extract.
inputs Returns tfma.INPUT_KEY extract.
label Same as labels (DEPRECATED - use labels).
labels Returns tfma.LABELS_KEY extract.
prediction Same as predictions (DEPRECATED - use predictions).
predictions Returns tfma.PREDICTIONS_KEY extract.
transformed_features Returns tfma.TRANSFORMED_FEATURES_KEY extract.

Methods

clear

clear()

D.clear() -> None. Remove all items from D.

get

get(
    key, default=None
)

D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.

get_attributions

View source

get_attributions(
    model_name: Optional[str] = None, output_name: Optional[str] = None
) -> Optional[tfma.types.DictOfTensorValueMaybeDict]

Returns tfma.ATTRIBUTIONS_KEY extract.

get_by_key

View source

get_by_key(
    key: str,
    model_name: Optional[str] = None,
    output_name: Optional[str] = None
) -> Any

Returns item for key possibly filtered by model and/or output names.

get_example_weights

View source

get_example_weights(
    model_name: Optional[str] = None, output_name: Optional[str] = None
) -> Optional[types.TensorValueMaybeMultiLevelDict]

Returns tfma.EXAMPLE_WEIGHTS_KEY extract.

get_features

View source

get_features() -> Optional[tfma.types.DictOfTensorValueMaybeDict]

Returns tfma.FEATURES_KEY extract.

get_inputs

View source

get_inputs() -> Any

Returns tfma.INPUT_KEY extract.

get_labels

View source

get_labels(
    model_name: Optional[str] = None, output_name: Optional[str] = None
) -> Optional[types.TensorValueMaybeMultiLevelDict]

Returns tfma.LABELS_KEY extract.

get_predictions

View source

get_predictions(
    model_name: Optional[str] = None, output_name: Optional[str] = None
) -> Optional[types.TensorValueMaybeMultiLevelDict]

Returns tfma.PREDICTIONS_KEY extract.

get_transformed_features

View source

get_transformed_features(
    model_name: Optional[str] = None
) -> Optional[tfma.types.DictOfTensorValueMaybeDict]

Returns tfma.TRANSFORMED_FEATURES_KEY extract.

items

items()

D.items() -> a set-like object providing a view on D's items

keys

keys()

D.keys() -> a set-like object providing a view on D's keys

pop

pop(
    key, default=__marker
)

D.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem

popitem()

D.popitem() -> (k, v), remove and return some (key, value) pair as a 2-tuple; but raise KeyError if D is empty.

setdefault

setdefault(
    key, default=None
)

D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D

update

update(
    *args, **kwds
)

D.update([E, ]**F) -> None. Update D from mapping/iterable E and F. If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values

values()

D.values() -> an object providing a view on D's values