[utils.measure] Fix docstring

bob/ip/binseg/utils/measure.py

@@ -29,55 +29,51 @@ class SmoothedValue:
 
 
 def base_measures(tp, fp, tn, fn):
-    """
-    Calculates a bunch of measures from true/false positive and negative counts
+    """Calculates 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`_.
+    entry on `Precision and Recall
+    <https://en.wikipedia.org/wiki/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).
 
-        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).
 
-        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).
 
-        accuracy : float
-            A, :math:`\frac{tp + tn}{p + n} = \frac{tp + tn}{tp + fp + tn + fn}`
+    accuracy : float
+        A
 
-        jaccard : float
-            J, :math:`\frac{tp}{tp+fp+fn}`, see `Jaccard Index`_
+    jaccard : float
+        J, see `Jaccard Index <https://en.wikipedia.org/wiki/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, see `F1-score <https://en.wikipedia.org/wiki/F1_score>`_
 
     """
 

doc/api.rst

@@ -185,3 +185,6 @@ Datasets
 
    bob.ip.binseg.configs.datasets.drionsdb.expert1
    bob.ip.binseg.configs.datasets.drionsdb.expert2
+
+
+.. include:: links.rst

doc/links.rst

@@ -9,9 +9,6 @@
 .. _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/