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Computes the AUC and asymptotic normally distributed confidence interval.

USAGE NOTE: this approach requires storing all of the predictions and labels for a single evaluation in memory, so it may not be usable when the evaluation batch size and/or the number of evaluation steps is very large.

Computes the area under the ROC curve and its confidence interval using placement values. This has the advantage of being resilient to the distribution of predictions by aggregating across batches, accumulating labels and predictions and performing the final calculation using all of the concatenated values.

labels A Tensor of ground truth labels with the same shape as labels and with values of 0 or 1 whose values are castable to int64.
predictions A Tensor of predictions whose values are castable to float64. Will be flattened into a 1-D Tensor.
weights Optional Tensor whose rank is either 0, or the same rank as labels.
alpha Confidence interval level desired.
logit_transformation A boolean value indicating whether the estimate should be logit transformed prior to calculating the confidence interval. Doing so enforces the restriction that the AUC should never be outside the interval [0,1].
metrics_collections An optional iterable of collections that auc should be added to.
updates_collections An optional iterable of collections that update_op should be added to.
name An optional name for the variable_scope that contains the metric variables.

auc A 1-D Tensor containing the current area-under-curve, lower, and upper confidence interval values.
update_op An operation that concatenates the input labels and predictions to the accumulated values.

ValueError If labels, predictions, and weights have mismatched shapes or if alpha isn't in the range (0,1).