Implemented metric: Cross-Entropy

miseval/entropy.py

@@ -0,0 +1,56 @@
+#==============================================================================#
+#  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 : Cross-Entropy              #
+#-----------------------------------------------------#
+""" Compute cross-entropy between truth and prediction probabilities.
+
+    In information theory, the cross-entropy between two probability distributions p and q
+    over the same underlying set of events measures the average number of bits needed to
+    identify an event drawn from the set if a coding scheme used for the set is optimized
+    for an estimated probability distribution q, rather than the true distribution p.
+
+    Source: https://en.wikipedia.org/wiki/Cross_entropy
+
+Pooling (how to combine computed cross-entropy to a single value):
+    Distance Sum                        sum
+    Distance Averaging                  mean
+    Minimum Distance                    amin
+    Maximum Distance                    amax
+"""
+def calc_CrossEntropy(truth, pred_prob, c=1, pooling="mean", provided_prob=True,
+                      **kwargs):
+    # Obtain binary classification
+    if provided_prob : prob = np.take(pred_prob, c, axis=-1)
+    else : prob = np.equal(pred_prob, c)
+    gt = np.equal(truth, c).astype(int)
+    # Add epsilon to probability to avoid zero divisions for log()
+    prob = prob + np.finfo(np.float32).eps
+    # Compute cross-entropy
+    cross_entropy = - gt * np.log(prob)
+    # Apply pooling function across all pixel classifications
+    res = getattr(np, pooling)(cross_entropy)
+    # Return Cross-Entropy
+    return res

tests/test_entropy.py

@@ -0,0 +1,63 @@
+#==============================================================================#
+#  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: Entropy-based metrics           #
+#-----------------------------------------------------#
+class TEST_Entropy(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 : Cross-Entropy            #
+    #-------------------------------------------------#
+    def test_calc_CrossEntropy(self):
+        # Check binary score
+        score_bi = calc_CrossEntropy(self.gt_bi, self.prob_bi, c=1,
+                                     provided_prob=True)
+        self.assertTrue(isinstance(score_bi, np.float64))
+        # Check multi-class score
+        for i in range(5):
+            score_mc = calc_CrossEntropy(self.gt_mc, self.prob_mc, c=i,
+                                         provided_prob=True)
+            self.assertTrue(isinstance(score_mc, np.float64))
+        # Check existance in metric_dict
+        self.assertTrue("CE" in metric_dict)
+        self.assertTrue(callable(metric_dict["CE"]))
+        self.assertTrue("CrossEntropy" in metric_dict)
+        self.assertTrue(callable(metric_dict["CrossEntropy"]))