From 5e3dbf1ef613af79dc7f5c333a031c1c96ce580f Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Tue, 5 May 2020 21:25:45 +0200
Subject: [PATCH] [utils.metric] Implement proper AUC for our RecallxPrecision
curves
---
bob/ip/binseg/utils/metric.py | 51 +++++++++++++++++++++++++++--------
1 file changed, 40 insertions(+), 11 deletions(-)
diff --git a/bob/ip/binseg/utils/metric.py b/bob/ip/binseg/utils/metric.py
index 903836f6..b49f4ede 100644
--- a/bob/ip/binseg/utils/metric.py
+++ b/bob/ip/binseg/utils/metric.py
@@ -2,6 +2,7 @@
# -*- coding: utf-8 -*-
from collections import deque
+import numpy
import torch
@@ -64,24 +65,52 @@ def base_metrics(tp, fp, tn, fn):
return [precision, recall, specificity, accuracy, jaccard, f1_score]
-def auc(precision, recall):
+def auc(x, y):
"""Calculates the area under the precision-recall curve (AUC)
- .. todo:: Integrate this to metrics reporting in compare.py
- """
+ This function requires a minimum of 2 points and will use the trapezoidal
+ method to calculate the area under a curve bound between ``[0.0, 1.0]``.
+ It interpolates missing points if required. The input ``x`` should be
+ continuously increasing or decreasing.
+
- rec_unique, rec_unique_ndx = numpy.unique(recall, return_index=True)
+ Parameters
+ ----------
- prec_unique = precision[rec_unique_ndx]
+ x : numpy.ndarray
+ A 1D numpy array containing continuously increasing or decreasing
+ values for the X coordinate.
+
+ y : numpy.ndarray
+ A 1D numpy array containing the Y coordinates of the X values provided
+ in ``x``.
+
+ """
- if rec_unique.shape[0] > 1:
- prec_interp = numpy.interp(
- numpy.arange(0, 1, 0.01),
- rec_unique,
- prec_unique,
+ assert len(x) == len(y)
+
+ dx = numpy.diff(x)
+ if numpy.any(dx < 0):
+ if numpy.all(dx <= 0):
+ # invert direction
+ x = x[::-1]
+ y = y[::-1]
+ else:
+ raise ValueError("x is neither increasing nor decreasing "
+ ": {}.".format(x))
+
+ # avoids repeated sums for every y
+ y_unique, y_unique_ndx = numpy.unique(y, return_index=True)
+ x_unique = x[y_unique_ndx]
+
+ if y_unique.shape[0] > 1:
+ x_interp = numpy.interp(
+ numpy.arange(0, 1, 0.001),
+ y_unique,
+ x_unique,
left=0.0,
right=0.0,
)
- return prec_interp.sum() * 0.01
+ return x_interp.sum() * 0.001
return 0.0
--
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