From 3ebea75ea45f6e47a52dfdd3c6ec54c682b93f1b Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Wed, 6 May 2020 13:53:17 +0200
Subject: [PATCH] [utils.measure] Fix docs
---
bob/ip/binseg/utils/measure.py | 55 +++++++++++++++++-----------------
doc/links.rst | 3 ++
2 files changed, 30 insertions(+), 28 deletions(-)
diff --git a/bob/ip/binseg/utils/measure.py b/bob/ip/binseg/utils/measure.py
index 881ac7c8..35bbd18f 100644
--- a/bob/ip/binseg/utils/measure.py
+++ b/bob/ip/binseg/utils/measure.py
@@ -33,52 +33,51 @@ def base_measures(tp, fp, tn, fn):
Calculates a bunch of measures from true/false positive and negative counts
This function can return standard machine learning measures from true and
- false positive counts of positives and negatives.
-
- For a thorough look into these and alternate names for the returned values,
- please check Wikipedia's entry on `Precision and Recall`_.
+ false positive counts of positives and negatives. For a thorough look into
+ these and alternate names for the returned values, please check Wikipedia's
+ entry on `Precision and Recall`_.
Parameters
----------
- tp : int
- True positive count, AKA "hit"
+ tp : int
+ True positive count, AKA "hit"
- fp : int
- False positive count, AKA, "correct rejection"
+ fp : int
+ False positive count, AKA, "correct rejection"
- tn : int
- True negative count, AKA "false alarm", or "Type I error"
+ tn : int
+ True negative count, AKA "false alarm", or "Type I error"
- fn : int
- False Negative count, AKA "miss", or "Type II error"
+ fn : int
+ False Negative count, AKA "miss", or "Type II error"
Returns
-------
- precision : float
- P, AKA positive predictive value (PPV)
- :math:`\frac{tp}{tp+fp}`
+ precision : float
+ P, AKA positive predictive value (PPV)
+ :math:`\frac{tp}{tp+fp}`
- recall : float
- R, AKA sensitivity, hit rate, or true positive rate (TPR)
- :math:`\frac{tp}{p} = \frac{tp}{tp+fn}`
+ recall : float
+ R, AKA sensitivity, hit rate, or true positive rate (TPR)
+ :math:`\frac{tp}{p} = \frac{tp}{tp+fn}`
- specificity : float
- S, AKA selectivity or true negative rate (TNR).
- :math:`\frac{tn}{n} = \frac{tn}{tn+fp}`
+ specificity : float
+ S, AKA selectivity or true negative rate (TNR).
+ :math:`\frac{tn}{n} = \frac{tn}{tn+fp}`
- accuracy : float
- A, :math:`\frac{tp + tn}{p + n} = \frac{tp + tn}{tp + fp + tn + fn}`
+ accuracy : float
+ A, :math:`\frac{tp + tn}{p + n} = \frac{tp + tn}{tp + fp + tn + fn}`
- jaccard : float
- J, :math:`\frac{tp}{tp+fp+fn}`, see `Jaccard Index`_
+ jaccard : float
+ J, :math:`\frac{tp}{tp+fp+fn}`, see `Jaccard Index`_
- f1_score : float
- F1, :math:`\frac{2 P R}{P + R} = \frac{2tp}{2tp + fp + fn}`, see
- `F1-score`_
+ f1_score : float
+ F1, :math:`\frac{2 P R}{P + R} = \frac{2tp}{2tp + fp + fn}`, see
+ `F1-score`_
"""
diff --git a/doc/links.rst b/doc/links.rst
index ce2c72d6..5d0ffb98 100644
--- a/doc/links.rst
+++ b/doc/links.rst
@@ -9,6 +9,9 @@
.. _pytorch: https://pytorch.org
.. _tabulate: https://pypi.org/project/tabulate/
.. _our paper: https://arxiv.org/abs/1909.03856
+.. _precision and recall: https://en.wikipedia.org/wiki/Precision_and_recall
+.. _f1-score: https://en.wikipedia.org/wiki/F1_score
+.. _jaccard index: https://en.wikipedia.org/wiki/Jaccard_index
.. Raw data websites
.. _drive: https://www.isi.uu.nl/Research/Databases/DRIVE/
--
GitLab