# tf.compat.v1.metrics.auc

Computes the approximate AUC via a Riemann sum. (deprecated)

``````tf.compat.v1.metrics.auc(
labels,
predictions,
weights=None,
num_thresholds=200,
metrics_collections=None,
curve='ROC',
name=None,
summation_method='trapezoidal',
thresholds=None
)
``````

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.
• `RuntimeError`: If eager execution is enabled.