tf.contrib.metrics.auc_using_histogram(boolean_labels, scores, score_range, nbins=100, collections=None, check_shape=True, name=None)

tf.contrib.metrics.auc_using_histogram(boolean_labels, scores, score_range, nbins=100, collections=None, check_shape=True, name=None)

See the guide: Metrics (contrib) > Metric Ops

AUC computed by maintaining histograms.

Rather than computing AUC directly, this Op maintains Variables containing histograms of the scores associated with True and False labels. By comparing these the AUC is generated, with some discretization error. See: "Efficient AUC Learning Curve Calculation" by Bouckaert.

This AUC Op updates in O(batch_size + nbins) time and works well even with large class imbalance. The accuracy is limited by discretization error due to finite number of bins. If scores are concentrated in a fewer bins, accuracy is lower. If this is a concern, we recommend trying different numbers of bins and comparing results.

Args:

• boolean_labels: 1-D boolean Tensor. Entry is True if the corresponding record is in class.
• scores: 1-D numeric Tensor, same shape as boolean_labels.
• score_range: Tensor of shape [2], same dtype as scores. The min/max values of score that we expect. Scores outside range will be clipped.
• nbins: Integer number of bins to use. Accuracy strictly increases as the number of bins increases.
• collections: List of graph collections keys. Internal histogram Variables are added to these collections. Defaults to [GraphKeys.LOCAL_VARIABLES].
• check_shape: Boolean. If True, do a runtime shape check on the scores and labels.
• name: A name for this Op. Defaults to "auc_using_histogram".

Returns:

• auc: float32 scalar Tensor. Fetching this converts internal histograms to auc value.
• update_op: Op, when run, updates internal histograms.

Defined in tensorflow/contrib/metrics/python/ops/histogram_ops.py.