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.
|