tf.keras.metrics.SpecificityAtSensitivity

Computes best specificity where sensitivity is >= specified value.

Inherits From: Metric

tf.keras.metrics.SpecificityAtSensitivity(
    sensitivity, num_thresholds=200, class_id=None, name=None, dtype=None
)

Sensitivity measures the proportion of actual positives that are correctly identified as such (tp / (tp + fn)). Specificity measures the proportion of actual negatives that are correctly identified as such (tn / (tn + fp)).

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

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

If class_id is specified, we calculate precision by considering only the entries in the batch for which class_id is above the threshold predictions, and computing the fraction of them for which class_id is indeed a correct label.

For additional information about specificity and sensitivity, see the following.

sensitivity A scalar value in range [0, 1].
num_thresholds (Optional) Defaults to 200. The number of thresholds to use for matching the given sensitivity.
class_id (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

Example:

m = keras.metrics.SpecificityAtSensitivity(0.5)
m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
m.result()
0.66666667
 
m.reset_state()
m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
               sample_weight=[1, 1, 2, 2, 2])
m.result()
0.5

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='binary_crossentropy',
    metrics=[keras.metrics.SpecificityAtSensitivity()])

dtype

variables

Methods

add_variable

View source

add_variable(
    shape, initializer, dtype=None, aggregation='sum', name=None
)

add_weight

View source

add_weight(
    shape=(), initializer=None, dtype=None, name=None
)

from_config

View source

@classmethod
from_config(
    config
)

get_config

View source

get_config()

Return the serializable config of the metric.

reset_state

View source

reset_state()

Reset all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

View source

result()

Compute the current metric value.

Returns
A scalar tensor, or a dictionary of scalar tensors.

stateless_reset_state

View source

stateless_reset_state()

stateless_result

View source

stateless_result(
    metric_variables
)

stateless_update_state

View source

stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

View source

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates confusion matrix statistics.

Args
y_true The ground truth values.
y_pred The predicted values.
sample_weight Optional weighting of each example. Defaults to 1. Can be a tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

__call__

View source

__call__(
    *args, **kwargs
)

Call self as a function.