tfma.metrics.MultiClassConfusionMatrixPlot

Multi-class confusion matrix plot.

Inherits From: Metric

tfma.metrics.MultiClassConfusionMatrixPlot(
    thresholds: Optional[List[float]] = None,
    num_thresholds: Optional[int] = None,
    name: Text = MULTI_CLASS_CONFUSION_MATRIX_PLOT_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. 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 tfma.metrics.NO_PREDICTED_CLASS_ID). Only one of either thresholds or num_thresholds should be used. If both are unset, then [0.0] will be assumed.
num_thresholds Number of thresholds to use. The thresholds will be evenly spaced between 0.0 and 1.0 and inclusive of the boundaries (i.e. to configure the thresholds to [0.0, 0.25, 0.5, 0.75, 1.0], the parameter should be set to 5). Only one of either thresholds or num_thresholds should be used.
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[Text]] = None,
    output_names: Optional[List[Text]] = None,
    sub_keys: Optional[List[Optional[tfma.metrics.SubKey]]] = None,
    aggregation_type: Optional[AggregationType] = None,
    class_weights: Optional[Dict[int, float]] = None,
    query_key: Optional[Text] = None,
    is_diff: Optional[bool] = False
) -> tfma.metrics.MetricComputations

Creates computations associated with metric.

get_config

View source

get_config() -> tfma.types.Extracts

Returns serializable config.