tfma.metrics.MultiClassConfusionMatrixAtThresholds

Multi-class confusion matrix metrics at thresholds.

Inherits From: Metric

tfma.metrics.MultiClassConfusionMatrixAtThresholds(
    thresholds: Optional[List[float]] = None,
    name: str = MULTI_CLASS_CONFUSION_MATRIX_AT_THRESHOLDS_NAME
)

Computes weighted example counts for all combinations of actual / (top) predicted classes.

The inputs are assumed to contain a single positive label per example (i.e. only one class can be true at a time) while the predictions are assumed to sum to 1.0.

thresholds Optional thresholds, defaults to 0.5 if not specified. If the top prediction is less than a threshold then the associated example will be assumed to have no prediction associated with it (the predicted_class_id will be set to NO_PREDICTED_CLASS_ID).
name Metric name.

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.