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tf.contrib.metrics.streaming_curve_points

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Computes curve (ROC or PR) values for a prespecified number of points.

tf.contrib.metrics.streaming_curve_points(
    labels=None,
    predictions=None,
    weights=None,
    num_thresholds=200,
    metrics_collections=None,
    updates_collections=None,
    curve='ROC',
    name=None
)

The streaming_curve_points function creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the curve values. To discretize the curve, a linearly spaced set of thresholds is used to compute pairs of recall and precision values.

For best results, predictions should be distributed approximately uniformly in the range [0, 1] and not peaked around 0 or 1.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables.

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.

Returns:

  • points: A Tensor with shape [num_thresholds, 2] that contains points of the curve.
  • update_op: An operation that increments the true_positives, true_negatives, false_positives and false_negatives variables.

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.

TODO(chizeng): Consider rewriting this method to make use of logic within the precision_recall_at_equal_thresholds method (to improve run time).