tf.metrics.auc

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

Defined in tensorflow/python/ops/metrics_impl.py.

Computes the approximate AUC via a Riemann sum.

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.

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.

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.