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.

Args
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.

Attributes
attributions
combined_features
example_weight	Same as example_weights (DEPRECATED - use example_weights).
example_weights
features
inputs
label	Same as labels (DEPRECATED - use labels).
labels
prediction	Same as predictions (DEPRECATED - use predictions).
predictions
transformed_features

Methods

clear

clear()

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

get

get(
    key, default=None
)

Retrieve the corresponding layout by the string key.

When there isn't an exact match, all the existing keys in the layout map will be treated as a regex and map against the input key again. The first match will be returned, based on the key insertion order. Return None if there isn't any match found.

Args
key	the string key as the query for the layout.

Returns
Corresponding layout based on the query.

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_combined_features

View source

get_combined_features(
    model_name: Optional[str] = None
) -> Mapping[str, Any]

Returns a combined extract of transformed features and features.

In case of name collision, transformed features is looked up first and the value is returned when found.

Args
model_name	Optionally, the model name assosicated to the transformed feature. This has no effect on the raw features extract.

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_model_and_output_names

View source

get_model_and_output_names(
    eval_config: tfma.EvalConfig
) -> List[Tuple[Optional[str], Optional[str]]]

Returns a list of model_name-output_name tuples present in extracts.

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.

set_labels

View source

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

Sets tfma.LABELS_KEY extract for a given model and output.

set_predictions

View source

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

Sets tfma.PREDICTIONS_KEY extract for a given model and output.

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(
    other, /, **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