tfma.metrics.AttributionsMetric

Base type for attribution metrics.

Inherits From: Metric

tfma.metrics.AttributionsMetric(
    create_computations_fn: Callable[..., tfma.metrics.MetricComputations],
    **kwargs
)

create_computations_fn Function to create the metrics computations (e.g. mean_label, etc). This function should take the args passed to init as as input along with any of eval_config, schema, model_names, output_names, sub_keys, aggregation_type, or query_key (where needed).
**kwargs Any additional kwargs to pass to create_computations_fn. These should only contain primitive types or lists/dicts of primitive types. The kwargs passed to computations have precendence over these kwargs.

compute_confidence_interval Whether to compute confidence intervals for this metric.

Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method.

Methods

computations

View source

computations(
    eval_config: Optional[tfma.EvalConfig] = None,
    schema: Optional[schema_pb2.Schema] = None,
    model_names: Optional[List[str]] = None,
    output_names: Optional[List[str]] = None,
    sub_keys: Optional[List[Optional[SubKey]]] = None,
    aggregation_type: Optional[AggregationType] = None,
    class_weights: Optional[Dict[int, float]] = None,
    example_weighted: bool = False,
    query_key: Optional[str] = None
) -> tfma.metrics.MetricComputations

Creates computations associated with metric.

from_config

View source

@classmethod
from_config(
    config: Dict[str, Any]
) -> 'Metric'

get_config

View source

get_config() -> Dict[str, Any]

Returns serializable config.