Computes the approximate AUC via a Riemann sum.

tf.metrics.auc( labels, predictions, weights=None, num_thresholds=200, metrics_collections=None, updates_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. |

© 2020 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/versions/r1.15/api_docs/python/tf/metrics/auc