tf.compat.v1.metrics.specificity_at_sensitivity

Computes the specificity at a given sensitivity.

tf.compat.v1.metrics.specificity_at_sensitivity(
    labels, predictions, sensitivity, weights=None, num_thresholds=200,
    metrics_collections=None, updates_collections=None, name=None
)

The specificity_at_sensitivity function creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the specificity at the given sensitivity value. The threshold for the given sensitivity value is computed and used to evaluate the corresponding specificity.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the specificity. update_op increments the true_positives, true_negatives, false_positives and false_negatives counts with the weight of each case found in the predictions and labels.

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

For additional information about specificity and sensitivity, see the following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity

Args
labels	The ground truth values, a Tensor whose dimensions must match predictions. Will be cast to bool.
predictions	A floating point Tensor of arbitrary shape and whose values are in the range [0, 1].
sensitivity	A scalar value in range [0, 1].
weights	Optional Tensor whose rank is either 0, or the same rank as labels, and must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding labels dimension).
num_thresholds	The number of thresholds to use for matching the given sensitivity.
metrics_collections	An optional list of collections that specificity should be added to.
updates_collections	An optional list of collections that update_op should be added to.
name	An optional variable_scope name.

Returns
specificity	A scalar Tensor representing the specificity at the given sensitivity value.
update_op	An operation that increments the true_positives, true_negatives, false_positives and false_negatives variables appropriately and whose value matches specificity.

Raises
ValueError	If predictions and labels have mismatched shapes, if weights is not None and its shape doesn't match predictions, or if sensitivity is not between 0 and 1, or if either metrics_collections or updates_collections are not a list or tuple.
RuntimeError	If eager execution is enabled.