The auc function creates four local variables, true_positives,
true_negatives, false_positives and false_negatives that are used to
compute the AUC. To discretize the AUC curve, a linearly spaced set of
thresholds is used to compute pairs of recall and precision values. The area
under the ROC-curve is therefore computed using the height of the recall
values by the false positive rate, while the area under the PR-curve is the
computed using the height of the precision values by the recall.
This value is ultimately returned as auc, an idempotent operation that
computes the area under a discretized curve of precision versus recall values
(computed using the aforementioned variables). The num_thresholds variable
controls the degree of discretization with larger numbers of thresholds more
closely approximating the true AUC. The quality of the approximation may vary
dramatically depending on num_thresholds.
For best results, predictions should be distributed approximately uniformly
in the range [0, 1] and not peaked around 0 or 1. The quality of the AUC
approximation may be poor if this is not the case. Setting summation_method
to 'minoring' or 'majoring' can help quantify the error in the approximation
by providing lower or upper bound estimate of the AUC. The thresholds
parameter can be used to manually specify thresholds which split the
predictions more evenly.
For estimation of the metric over a stream of data, the function creates an
update_op operation that updates these variables and returns the auc.
If weights is None, weights default to 1. Use weights of 0 to mask values.
Args
labels
A Tensor whose shape matches predictions. Will be cast to
bool.
predictions
A floating point Tensor of arbitrary shape and whose values
are in the 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 when discretizing the roc
curve.
metrics_collections
An optional list of collections that auc should be
added to.
updates_collections
An optional list of collections that update_op should
be added to.
curve
Specifies the name of the curve to be computed, 'ROC' [default] or
'PR' for the Precision-Recall-curve.
name
An optional variable_scope name.
summation_method
Specifies the Riemann summation method used
(https://en.wikipedia.org/wiki/Riemann_sum): 'trapezoidal' [default] that
applies the trapezoidal rule; 'careful_interpolation', a variant of it
differing only by a more correct interpolation scheme for PR-AUC -
interpolating (true/false) positives but not the ratio that is precision;
'minoring' that applies left summation for increasing intervals and right
summation for decreasing intervals; 'majoring' that does the opposite.
Note that 'careful_interpolation' is strictly preferred to 'trapezoidal'
(to be deprecated soon) as it applies the same method for ROC, and a
better one (see Davis & Goadrich 2006 for details) for the PR curve.
thresholds
An optional list of floating point values to use as the
thresholds for discretizing the curve. If set, the num_thresholds
parameter is ignored. Values should be in [0, 1]. Endpoint thresholds
equal to {-epsilon, 1+epsilon} for a small positive epsilon value will be
automatically included with these to correctly handle predictions equal to
exactly 0 or 1.
Returns
auc
A scalar Tensor representing the current area-under-curve.
update_op
An operation that increments the true_positives,
true_negatives, false_positives and false_negatives variables
appropriately and whose value matches auc.
Raises
ValueError
If predictions and labels have mismatched shapes, or if
weights is not None and its shape doesn't match predictions, or if
either metrics_collections or updates_collections are not a list or
tuple.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-03-17 UTC."],[],[]]