Implemented metric: Hinge loss

miseval/__init__.py

@@ -53,6 +53,8 @@
 from miseval.mcc import *
 # Boundary Distances
 from miseval.boundary_distance import *
+# Hinge loss
+from miseval.hinge import *
 
 #-----------------------------------------------------#
 #         Access Functions to Metric Functions        #
@@ -112,7 +114,10 @@
     "MCC_absolute": calc_MCC_Absolute,
     "aMCC": calc_MCC_Absolute,
     "BoundaryDistance": calc_Boundary_Distance,
-    "BD": calc_Boundary_Distance
+    "Distance": calc_Boundary_Distance,
+    "BD": calc_Boundary_Distance,
+    "Hinge": calc_Hinge,
+    "HingeLoss": calc_Hinge,
 }
 
 #-----------------------------------------------------#

miseval/boundary_distance.py

@@ -32,7 +32,7 @@
 
 List of available distances:
     Bhattacharyya distance 	            bhattacharyya
-    Bhattacharyya coefficient 	        bhattacharyya_coefficient
+    Bhattacharyya coefficient           bhattacharyya_coefficient
     Canberra distance 	                canberra
     Chebyshev distance 	                chebyshev
     Chi Square distance 	            chi_square

miseval/hinge.py

@@ -0,0 +1,51 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External modules
+import numpy as np
+
+#-----------------------------------------------------#
+#            Calculate : asd            #
+#-----------------------------------------------------#
+""" Compute Hinge loss between truth and prediction probabilities.
+
+    In machine learning, the hinge loss is a loss function used for training classifiers.
+    The hinge loss is used for "maximum-margin" classification.
+
+Pooling (how to combine computed Hinge losses to a single value):
+    Distance Sum                        sum
+    Distance Averaging                  mean
+    Minimum Distance                    amin
+    Maximum Distance                    amax
+
+"""
+def calc_Hinge(truth, pred_prob, c=1, pooling="mean"):
+    # Obtain binary classification
+    prob = pred_prob[:,:,c]
+    gt = np.equal(truth, c).astype(int)
+    # Convert ground truth 0/1 format to -1/+1 format
+    gt = np.where(gt==0, -1, gt)
+    # Compute Hinge
+    hinge_total = np.maximum(1 - gt * prob, 0)
+    # Apply pooling function across all pixel classifications
+    res = getattr(np, pooling)(res_dist)
+    # Return Hinge
+    return hinge

tests/test_boundary_distance.py

@@ -57,6 +57,8 @@ def test_calc_BoundaryDistance_General(self):
         # Check existance in metric_dict
         self.assertTrue("BoundaryDistance" in metric_dict)
         self.assertTrue(callable(metric_dict["BoundaryDistance"]))
+        self.assertTrue("Distance" in metric_dict)
+        self.assertTrue(callable(metric_dict["Distance"]))
         self.assertTrue("BD" in metric_dict)
         self.assertTrue(callable(metric_dict["BD"]))
 

tests/test_hinge.py

@@ -0,0 +1,61 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External modules
+import numpy as np
+import unittest
+# Internal modules
+from miseval import *
+
+#-----------------------------------------------------#
+#            Unittest: Area under the ROC             #
+#-----------------------------------------------------#
+class TEST_AUC(unittest.TestCase):
+    @classmethod
+    def setUpClass(self):
+        # Create ground truth
+        np.random.seed(1)
+        self.gt_bi = np.random.randint(2, size=(32,32))
+        self.gt_mc = np.random.randint(5, size=(32,32))
+        # Create prediction mask
+        np.random.seed(2)
+        self.pd_bi = np.random.randint(2, size=(32,32))
+        self.pd_mc = np.random.randint(5, size=(32,32))
+        # Create prediction probability
+        self.prob_bi = np.random.rand(32,32,2)
+        self.prob_mc = np.random.rand(32,32,5)
+
+    #-------------------------------------------------#
+    #                Calculate : Hinge                #
+    #-------------------------------------------------#
+    def test_calc_Hinge(self):
+        # Check binary score
+        score_bi = calc_Hinge(self.gt_bi, self.prob_bi, c=1)
+        self.assertTrue(isinstance(score_bi, np.float64))
+        # Check multi-class score
+        for i in range(5):
+            score_mc = calc_Hinge(self.gt_mc, self.prob_mc, c=i)
+            self.assertTrue(isinstance(score_mc, np.float64))
+        # Check existance in metric_dict
+        self.assertTrue("Hinge" in metric_dict)
+        self.assertTrue(callable(metric_dict["Hinge"]))
+        self.assertTrue("HingeLoss" in metric_dict)
+        self.assertTrue(callable(metric_dict["HingeLoss"]))