# tf.contrib.metrics.precision_recall_at_equal_thresholds

tf.contrib.metrics.precision_recall_at_equal_thresholds(
labels,
predictions,
weights=None,
num_thresholds=None,
use_locking=None,
name=None
)


A helper method for creating metrics related to precision-recall curves.

These values are true positives, false negatives, true negatives, false positives, precision, and recall. This function returns a data structure that contains ops within it.

Unlike _streaming_confusion_matrix_at_thresholds (which exhibits O(T * N) space and run time), this op exhibits O(T + N) space and run time, where T is the number of thresholds and N is the size of the predictions tensor. Hence, it may be advantageous to use this function when predictions is big.

For instance, prefer this method for per-pixel classification tasks, for which the predictions tensor may be very large.

Each number in predictions, a float in [0, 1], is compared with its corresponding label in labels, and counts as a single tp/fp/tn/fn value at each threshold. This is then multiplied with weights which can be used to reweight certain values, or more commonly used for masking values.

#### Args:

• labels: A bool Tensor whose shape matches predictions.
• predictions: A floating point Tensor of arbitrary shape and whose values are in the range [0, 1].
• weights: Optional; If provided, a Tensor that has the same dtype as, and broadcastable to, predictions. This tensor is multiplied by counts.
• num_thresholds: Optional; Number of thresholds, evenly distributed in [0, 1]. Should be >= 2. Defaults to 201. Note that the number of bins is 1 less than num_thresholds. Using an even num_thresholds value instead of an odd one may yield unfriendly edges for bins.
• use_locking: Optional; If True, the op will be protected by a lock. Otherwise, the behavior is undefined, but may exhibit less contention. Defaults to True.
• name: Optional; variable_scope name. If not provided, the string 'precision_recall_at_equal_threshold' is used.

#### Returns:

• result: A named tuple (See PrecisionRecallData within the implementation of this function) with properties that are variables of shape [num_thresholds]. The names of the properties are tp, fp, tn, fn, precision, recall, thresholds.
• update_op: An op that accumulates values.

#### Raises:

• ValueError: If predictions and labels have mismatched shapes, or if weights is not None and its shape doesn't match predictions, or if includes contains invalid keys.