tf.keras.metrics.RecallAtPrecision

Computes best recall where precision is >= specified value.

Inherits From: Metric, Layer, Module

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.RecallAtPrecision

tf.keras.metrics.RecallAtPrecision(
    precision, num_thresholds=200, class_id=None, name=None, dtype=None
)

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 four local variables, true_positives, true_negatives, 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.

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.

Args
precision	A scalar value in range [0, 1].
num_thresholds	(Optional) Defaults to 200. The number of thresholds to use for matching the given precision.
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.

Standalone usage:

m = tf.keras.metrics.RecallAtPrecision(0.8)
m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
m.result().numpy()
0.5

m.reset_state()
m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
               sample_weight=[1, 0, 0, 1])
m.result().numpy()
1.0

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.RecallAtPrecision(precision=0.8)])

Methods

merge_state

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merge_state(
    metrics
)

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])

m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])

m2.merge_state([m1])
m2.result().numpy()
0.75

Args
metrics	an iterable of metrics. The metrics must have compatible state.

Raises
ValueError	If the provided iterable does not contain metrics matching the metric's required specifications.

reset_state

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reset_state()

Resets all of the metric state variables.

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

result

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result()

Computes and returns the scalar metric value tensor or a dict of scalars.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

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

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.

Returns
Update op.