tfma.metrics.RecallAtPrecision

Computes best recall where precision is >= specified value.

Inherits From: Metric

For a given score-label-distribution the required precision might not be achievable, in this case 0.0 is returned as recall.

This metric creates three local variables, true_positives, false_positives and false_negatives that are used to compute the recall at the given precision. The threshold for the given precision value is computed and used to evaluate the corresponding recall.

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

precision A scalar value in range [0, 1].
num_thresholds (Optional) Defaults to 1000. The number of thresholds to use for matching the given precision.
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.
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.

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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Creates computations associated with metric.

get_config

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Returns serializable config.