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Computes best precision where recall is >= specified value.

Source: R/metrics.R
metric_precision_at_recall.Rd

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 NULL, 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.

Usage

metric_precision_at_recall(
  ...,
  recall,
  num_thresholds = 200L,
  class_id = NULL,
  name = NULL,
  dtype = NULL
)

Arguments

...

For forward/backward compatability.

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.

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.

Value

a Metric instance is returned. The Metric instance can be passed directly to compile(metrics = ), or used as a standalone object. See ?Metric for example usage.

Usage

Standalone usage:

m <- metric_precision_at_recall(recall = 0.5)
m$update_state(c(0,   0,   0,   1,   1),
               c(0, 0.3, 0.8, 0.3, 0.8))
m$result()

## tf.Tensor(0.5, shape=(), dtype=float32)


m$reset_state()
m$update_state(c(0,   0,   0,   1,   1),
               c(0, 0.3, 0.8, 0.3, 0.8),
               sample_weight = c(2, 2, 2, 1, 1))
m$result()

## tf.Tensor(0.33333334, shape=(), dtype=float32)

Usage with compile() API:

model |> compile(
  optimizer = 'sgd',
  loss = 'binary_crossentropy',
  metrics = list(metric_precision_at_recall(recall = 0.8))
)

See also

Other confusion metrics:
metric_auc()
metric_false_negatives()
metric_false_positives()
metric_precision()
metric_recall()
metric_recall_at_precision()
metric_sensitivity_at_specificity()
metric_specificity_at_sensitivity()
metric_true_negatives()
metric_true_positives()

Other metrics:
Metric()
custom_metric()
metric_auc()
metric_binary_accuracy()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_binary_iou()
metric_categorical_accuracy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_cosine_similarity()
metric_f1_score()
metric_false_negatives()
metric_false_positives()
metric_fbeta_score()
metric_hinge()
metric_huber()
metric_iou()
metric_kl_divergence()
metric_log_cosh()
metric_log_cosh_error()
metric_mean()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_iou()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_mean_wrapper()
metric_one_hot_iou()
metric_one_hot_mean_iou()
metric_poisson()
metric_precision()
metric_r2_score()
metric_recall()
metric_recall_at_precision()
metric_root_mean_squared_error()
metric_sensitivity_at_specificity()
metric_sparse_categorical_accuracy()
metric_sparse_categorical_crossentropy()
metric_sparse_top_k_categorical_accuracy()
metric_specificity_at_sensitivity()
metric_squared_hinge()
metric_sum()
metric_top_k_categorical_accuracy()
metric_true_negatives()
metric_true_positives()