tf.compat.v1.metrics.precision_at_thresholds

Computes precision values for different thresholds on predictions.

The precision_at_thresholds function creates four local variables, true_positives, true_negatives, false_positives and false_negatives for various values of thresholds. precision[i] is defined as the total weight of values in predictions above thresholds[i] whose corresponding entry in labels is True, divided by the total weight of values in predictions above thresholds[i] (true_positives[i] / (true_positives[i] + false_positives[i])).

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the precision.

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

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].
thresholds A python list or tuple of float thresholds in [0, 1].
weights Optional