tf.contrib.metrics.recall_at_precision

tf.contrib.metrics.recall_at_precision(
    labels,
    predictions,
    precision,
    weights=None,
    num_thresholds=200,
    metrics_collections=None,
    updates_collections=None,
    name=None
)

Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py.

Computes recall at precision.

The recall_at_precision function creates four local variables, tp (true positives), fp (false positives) and fn (false negatives) that are used to compute the recall at the given precision value. The threshold for the given precision value is computed and used to evaluate the corresponding recall.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the recall. update_op increments the tp, fp and fn 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.

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].
• precision: 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 precision.
• metrics_collections: An optional list of collections that recall 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:

• recall: A scalar Tensor representing the recall at the given precision value.
• update_op: An operation that increments the tp, fp and fn variables appropriately and whose value matches recall.

Raises:

• ValueError: If predictions and labels have mismatched shapes, if weights is not None and its shape doesn't match predictions, or if precision is not between 0 and 1, or if either metrics_collections or updates_collections are not a list or tuple.