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 )
Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py
.
Computes curve (ROC or PR) values for a prespecified number of points.
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.
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.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.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).
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/metrics/streaming_curve_points