Add log-scale variant of ROC AUC

bob/measure/__init__.py

@@ -474,7 +474,7 @@ def eer(negatives, positives, is_sorted=False, also_farfrr=False):
   return (far + frr) / 2.0
 
 
-def roc_auc_score(negatives, positives, npoints=2000, min_far=-8):
+def roc_auc_score(negatives, positives, npoints=2000, min_far=-8, log_scale=False):
   """Area Under the ROC Curve.
   Computes the area under the ROC curve. This is useful when you want to report one
   number that represents an ROC curve. For more information, see:
@@ -490,14 +490,23 @@ def roc_auc_score(negatives, positives, npoints=2000, min_far=-8):
       Number of points in the ROC curve. Higher numbers leads to more accurate ROC.
   min_far : float, optional
       Min FAR and FRR values to consider when calculating ROC.
+  log_scale : bool, optional
+      If True, converts the x axis (FPR) to log10 scale before calculating AUC. This is
+      useful in cases where len(negatives) >> len(positives)
 
   Returns
   -------
   float
-      The ROC AUC
+      The ROC AUC. If ``log_scale`` is False, the value should be between 0 and 1.
   """
   fpr, fnr = roc(negatives, positives, npoints, min_far=min_far)
   tpr = 1 - fnr
+
+  if log_scale:
+    fpr_pos = fpr > 0
+    fpr, tpr = fpr[fpr_pos], tpr[fpr_pos]
+    fpr = numpy.log10(fpr)
+
   area = -1 * numpy.trapz(tpr, fpr)
   return area
 

bob/measure/script/figure.py

@@ -183,7 +183,7 @@ class Metrics(MeasureBase):
 
     def __init__(self, ctx, scores, evaluation, func_load,
                  names=('False Positive Rate', 'False Negative Rate',
-                        'Precision', 'Recall', 'F1-score', 'Area Under ROC Curve')):
+                        'Precision', 'Recall', 'F1-score', 'Area Under ROC Curve', 'Area Under ROC Curve (log scale)')):
         super(Metrics, self).__init__(ctx, scores, evaluation, func_load)
         self.names = names
         self._tablefmt = ctx.meta.get('tablefmt')
@@ -229,8 +229,9 @@ class Metrics(MeasureBase):
 
         # AUC ROC
         auc = roc_auc_score(neg, pos)
+        auc_log = roc_auc_score(neg, pos, log_scale=True)
         return (fta, fmr, fnmr, hter, far, frr, fm, ni, fnm, nc, precision,
-                recall, f1_score, auc)
+                recall, f1_score, auc, auc_log)
 
     def _strings(self, metrics):
         n_dec = '.%df' % self._decimal
@@ -246,9 +247,10 @@ class Metrics(MeasureBase):
         recall_str = "%s" % format(metrics[11], n_dec)
         f1_str = "%s" % format(metrics[12], n_dec)
         auc_str = "%s" % format(metrics[13], n_dec)
+        auc_log_str = "%s" % format(metrics[14], n_dec)
 
         return (fta_str, fmr_str, fnmr_str, far_str, frr_str, hter_str,
-                prec_str, recall_str, f1_str, auc_str)
+                prec_str, recall_str, f1_str, auc_str, auc_log_str)
 
     def _get_all_metrics(self, idx, input_scores, input_names):
         ''' Compute all metrics for dev and eval scores'''
@@ -308,6 +310,7 @@ class Metrics(MeasureBase):
             [self.names[3], all_metrics[0][7]],
             [self.names[4], all_metrics[0][8]],
             [self.names[5], all_metrics[0][9]],
+            [self.names[6], all_metrics[0][10]],
         ]
 
         if self._eval:
@@ -325,6 +328,7 @@ class Metrics(MeasureBase):
             rows[3].append(all_metrics[1][7])
             rows[4].append(all_metrics[1][8])
             rows[5].append(all_metrics[1][9])
+            rows[6].append(all_metrics[1][10])
 
         click.echo(tabulate(rows, headers, self._tablefmt), file=self.log_file)
 

bob/measure/test_error.py

@@ -517,3 +517,8 @@ def test_roc_auc_score():
   oracle = 0.9326
 
   assert numpy.allclose(auc, oracle), f"Expected {oracle} but got {auc} instead."
+
+  # test the function on log scale as well
+  auc = roc_auc_score(negatives, positives, log_scale=True)
+  oracle = 1.4183699583300993
+  assert numpy.allclose(auc, oracle), f"Expected {oracle} but got {auc} instead."