tfma.metrics.MultiLabelConfusionMatrixPlot

Multi-label confusion matrix.

Inherits From: Metric

tfma.metrics.MultiLabelConfusionMatrixPlot(
    thresholds: Optional[List[float]] = None,
    num_thresholds: Optional[int] = None,
    name: str = MULTI_LABEL_CONFUSION_MATRIX_PLOT_NAME
)

For each actual class (positive label) a confusion matrix is computed for each class based on the associated predicted values such that:

TP = positive_prediction_class_label & positive_prediction TN = negative_prediction_class_label & negative_prediction FP = negative_prediction_class_label & positive_prediction FN = positive_prediction_class_label & negative_prediction

For example, given classes 0, 1 and a given threshold, the following matrices will be computed:

Actual: class_0 Predicted: class_0 TP = is_class_0 & is_class_0 & predict_class_0 TN = is_class_0 & not_class_0 & predict_not_class_0 FN = is_class_0 & is_class_0 & predict_not_class_0 FP = is_class_0 & not_class_0 & predict_class_0 Actual: class_0 Predicted: class_1 TP = is_class_0 & is_class_1 & predict_class_1 TN = is_class_0 & not_class_1 & predict_not_class_1 FN = is_class_0 & is_class_1 & predict_not_class_1 FP = is_class_0 & not_class_1 & predict_class_1 Actual: class_1 Predicted: class_0 TP = is_class_1 & is_class_0 & predict_class_0 TN = is_class_1 & not_class_0 & predict_not_class_0 FN = is_class_1 & is_class_0 & predict_not_class_0 FP = is_class_1 & not_class_0 & predict_class_0 Actual: class_1 Predicted: class_1 TP = is_class_1 & is_class_1 & predict_class_1 TN = is_class_1 & not_class_1 & predict_not_class_1 FN = is_class_1 & is_class_1 & predict_not_class_1 FP = is_class_1 & not_class_1 & predict_class_1

Note that unlike the multi-class confusion matrix, the inputs are assumed to be multi-label whereby the predictions may not necessarily sum to 1.0 and multiple classes can be true as the same time.

thresholds Optional thresholds. Only one of either thresholds or num_thresholds should be used. If both are unset, then [0.5] 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[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.