tfma.metrics.BinaryAccuracy

Calculates how often predictions match binary labels.

Inherits From: Metric

tfma.metrics.BinaryAccuracy(
    threshold: Optional[float] = None,
    top_k: Optional[int] = None,
    class_id: Optional[int] = None,
    name: Optional[str] = None
)

This metric computes the accuracy based on (TP + TN) / (TP + FP + TN + FN).

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Args
threshold	(Optional) A float value in [0, 1]. The threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). If neither threshold nor top_k are set, the default is to calculate with threshold=0.5.
top_k	(Optional) Used with a multi-class model to specify that the top-k values should be used to compute the confusion matrix. The net effect is that the non-top-k values are set to -inf and the matrix is then constructed from the average TP, FP, TN, FN across the classes. When top_k is used, metrics_specs.binarize settings must not be present. Only one of class_id or top_k should be configured. When top_k is set, the default thresholds are [float('-inf')].
class_id	(Optional) Used with a multi-class model to specify which class to compute the confusion matrix for. When class_id is used, metrics_specs.binarize settings must not be present. Only one of class_id or top_k should be configured.
name	(Optional) string name of the metric instance.

Attributes
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

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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

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get_config() -> Dict[str, Any]

Returns serializable config.

result

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

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