tfma.metrics.TNR

Alias for Specificity.

Inherits From: Specificity, Metric

tfma.metrics.TNR(
    thresholds: Optional[Union[float, List[float]]] = None,
    name: Optional[str] = None,
    top_k: Optional[int] = None,
    class_id: Optional[int] = None
)

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.

result

View source

result(
    tp: float, tn: float, fp: float, fn: float
) -> float

Function for computing metric value from TP, TN, FP, FN values.