tfma.metrics.MinLabelPosition

Min label position metric.

Inherits From: Metric

tfma.metrics.MinLabelPosition(
    name=MIN_LABEL_POSITION_NAME, label_key: Optional[str] = None
)

Calculates the least index in a query which has a positive label. The final returned value is the weighted average over all queries in the evaluation set which have at least one labeled entry. Note, ranking is indexed from one, so the optimal value for this metric is one. If there are no labeled rows in the evaluation set, the final output will be zero.

This is a query/ranking based metric so a query_key must also be provided in the associated metrics spec.

Args
name	Metric name.
label_key	Optional label key to override default label.

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

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.