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# tf.keras.metrics.PrecisionAtRecall

Computes best precision where recall is >= specified value.

Inherits From: Metric, Layer, Module

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

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

recall A scalar value in range [0, 1].
num_thresholds (Optional) Defaults to 200. The number of thresholds to use for matching the given recall.
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

#### Standalone usage:

m = tf.keras.metrics.PrecisionAtRecall(0.5)
m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
m.result().numpy()
0.5
m.reset_states()
m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
sample_weight=[2, 2, 2, 1, 1])
m.result().numpy()
0.33333333

Usage with compile() API:

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

## Methods

### reset_states

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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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Computes and returns the metric value tensor.

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

### update_state

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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.