Add Area Under ROC Curve (AUC)

Add the function called bob.measure.roc_auc_score.
Add the AUC computation to ``bob measure metrics`` command.

bob/measure/__init__.py

@@ -474,6 +474,34 @@ 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):
+  """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:
+  https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
+
+  Parameters
+  ----------
+  negatives : array_like
+      The negative scores.
+  positives : array_like
+      The positive scores.
+  npoints : int, optional
+      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.
+
+  Returns
+  -------
+  float
+      The ROC AUC
+  """
+  fpr, fnr = roc(negatives, positives, npoints, min_far=min_far)
+  tpr = 1 - fnr
+  area = -1 * numpy.trapz(tpr, fpr)
+  return area
+
+
 def get_config():
   """Returns a string containing the configuration information.
   """

bob/measure/script/commands.py

@@ -13,7 +13,7 @@ CRITERIA = ('eer', 'min-hter', 'far')
 
 @common_options.metrics_command(
     common_options.METRICS_HELP.format(
-        names='FPR, FNR, precision, recall, F1-score',
+        names='FPR, FNR, precision, recall, F1-score, AUC ROC',
         criteria=CRITERIA, score_format=SCORE_FORMAT,
         hter_note=' ',
         command='bob measure metrics'),

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')):
+                        'Precision', 'Recall', 'F1-score', 'Area Under ROC Curve')):
         super(Metrics, self).__init__(ctx, scores, evaluation, func_load)
         self.names = names
         self._tablefmt = ctx.meta.get('tablefmt')
@@ -209,7 +209,7 @@ class Metrics(MeasureBase):
         return utils.get_thres(criterion, dev_neg, dev_pos, far)
 
     def _numbers(self, neg, pos, threshold, fta):
-        from .. import (farfrr, precision_recall, f_score)
+        from .. import (farfrr, precision_recall, f_score, roc_auc_score)
         # fpr and fnr
         fmr, fnmr = farfrr(neg, pos, threshold)
         hter = (fmr + fnmr) / 2.0
@@ -226,8 +226,11 @@ class Metrics(MeasureBase):
 
         # f_score
         f1_score = f_score(neg, pos, threshold, 1)
+
+        # AUC ROC
+        auc = roc_auc_score(neg, pos)
         return (fta, fmr, fnmr, hter, far, frr, fm, ni, fnm, nc, precision,
-                recall, f1_score)
+                recall, f1_score, auc)
 
     def _strings(self, metrics):
         n_dec = '.%df' % self._decimal
@@ -242,9 +245,10 @@ class Metrics(MeasureBase):
         prec_str = "%s" % format(metrics[10], n_dec)
         recall_str = "%s" % format(metrics[11], n_dec)
         f1_str = "%s" % format(metrics[12], n_dec)
+        auc_str = "%s" % format(metrics[13], n_dec)
 
         return (fta_str, fmr_str, fnmr_str, far_str, frr_str, hter_str,
-                prec_str, recall_str, f1_str)
+                prec_str, recall_str, f1_str, auc_str)
 
     def _get_all_metrics(self, idx, input_scores, input_names):
         ''' Compute all metrics for dev and eval scores'''
@@ -297,11 +301,14 @@ class Metrics(MeasureBase):
             LOGGER.warn("NaNs scores (%s) were found in %s amd removed",
                         all_metrics[0][0], dev_file)
         headers = [' ' or title, 'Development']
-        rows = [[self.names[0], all_metrics[0][1]],
-                [self.names[1], all_metrics[0][2]],
-                [self.names[2], all_metrics[0][6]],
-                [self.names[3], all_metrics[0][7]],
-                [self.names[4], all_metrics[0][8]]]
+        rows = [
+            [self.names[0], all_metrics[0][1]],
+            [self.names[1], all_metrics[0][2]],
+            [self.names[2], all_metrics[0][6]],
+            [self.names[3], all_metrics[0][7]],
+            [self.names[4], all_metrics[0][8]],
+            [self.names[5], all_metrics[0][9]],
+        ]
 
         if self._eval:
             eval_file = input_names[1]
@@ -317,6 +324,7 @@ class Metrics(MeasureBase):
             rows[2].append(all_metrics[1][6])
             rows[3].append(all_metrics[1][7])
             rows[4].append(all_metrics[1][8])
+            rows[5].append(all_metrics[1][9])
 
         click.echo(tabulate(rows, headers, self._tablefmt), file=self.log_file)
 

bob/measure/test_error.py

@@ -503,3 +503,17 @@ def test_mindcf():
   assert mindcf< 1.0 + 1e-8
 
 
+def test_roc_auc_score():
+  from bob.measure import roc_auc_score
+  positives = bob.io.base.load(F('nonsep-positives.hdf5'))
+  negatives = bob.io.base.load(F('nonsep-negatives.hdf5'))
+  auc = roc_auc_score(negatives, positives)
+
+  # commented out sklearn computation to avoid adding an extra test dependency
+  # from sklearn.metrics import roc_auc_score as oracle_auc
+  # y_true = numpy.concatenate([numpy.ones_like(positives), numpy.zeros_like(negatives)], axis=0)
+  # y_score = numpy.concatenate([positives, negatives], axis=0)
+  # oracle = oracle_auc(y_true, y_score)
+  oracle = 0.9326
+
+  assert numpy.allclose(auc, oracle), f"Expected {oracle} but got {auc} instead."

bob/measure/utils.py

@@ -115,7 +115,7 @@ def get_thres(criter, neg, pos, far=None):
     elif criter == 'far':
         if far is None:
             raise ValueError("FAR value must be provided through "
-                             "``--far-value`` option.")
+                             "``--far-value`` or ``--fpr-value`` option.")
         from . import far_threshold
         return far_threshold(neg, pos, far)
     else:

doc/guide.rst

@@ -284,6 +284,9 @@ town. To plot an ROC curve, in possession of your **negatives** and
    >>> pyplot.ylabel('FNR (%)') # doctest: +SKIP
    >>> pyplot.grid(True)
    >>> pyplot.show() # doctest: +SKIP
+   >>> # You can also compute the area under the ROC curve:
+   >>> bob.measure.roc_auc_score(negatives, positives)
+   0.8958
 
 You should see an image like the following one:
 

doc/nitpick-exceptions.txt

 # ignores stuff that does not exist in Python 2.7 manual
 py:class list
+# ignores stuff that does not exist but makes sense
+py:class array
+py:class array_like
+py:class optional
+py:class callable

doc/py_api.rst

@@ -49,6 +49,7 @@ Curves
 
 .. autosummary::
    bob.measure.roc
+   bob.measure.roc_auc_score
    bob.measure.rocch
    bob.measure.roc_for_far
    bob.measure.det