Add unittests for compute_metrics_reloaded.py

.github/workflows/ci.yml

@@ -34,7 +34,12 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -r dataset_conversion/requirements.txt
+          git clone https://github.com/valosekj/MetricsReloaded.git
+          cd MetricsReloaded
+          git checkout jv/add_rel_vol_error_metric
+          python -m pip install .
 
       - name: Run tests with unittest
         run: |
-          python -m unittest tests/test_convert_bids_to_nnUNetV2.py
+          python -m unittest tests/test_convert_bids_to_nnUNetV2.py
+          python -m unittest tests/test_compute_metrics_reloaded.py

tests/test_compute_metrics_reloaded.py

@@ -0,0 +1,147 @@
+#######################################################################
+#
+# Tests for the `compute_metrics/compute_metrics_reloaded.py` script
+#
+# RUN BY:
+#   python -m unittest tests/test_compute_metrics_reloaded.py
+#######################################################################
+
+import unittest
+import os
+import numpy as np
+import nibabel as nib
+from compute_metrics.compute_metrics_reloaded import compute_metrics_single_subject
+import tempfile
+
+METRICS = ['dsc', 'fbeta', 'nsd', 'vol_diff', 'rel_vol_error']
+
+
+class TestComputeMetricsReloaded(unittest.TestCase):
+    def setUp(self):
+        # Use tempfile.NamedTemporaryFile to create temporary nifti files
+        self.ref_file = tempfile.NamedTemporaryFile(suffix=".nii.gz", delete=False)
+        self.pred_file = tempfile.NamedTemporaryFile(suffix=".nii.gz", delete=False)
+        self.metrics = METRICS
+
+    def create_dummy_nii(self, file_obj, data):
+        img = nib.Nifti1Image(data, np.eye(4))
+        nib.save(img, file_obj.name)
+        file_obj.seek(0)  # Move back to the beginning of the file
+
+    def tearDown(self):
+        # Close and remove temporary files
+        self.ref_file.close()
+        os.unlink(self.ref_file.name)
+        self.pred_file.close()
+        os.unlink(self.pred_file.name)
+
+    def assert_metrics(self, metrics_dict, expected_metrics):
+        for metric in self.metrics:
+            # if value is nan, use np.isnan to check
+            if np.isnan(expected_metrics[metric]):
+                self.assertTrue(np.isnan(metrics_dict[1][metric]))
+            # if value is inf, use np.isinf to check
+            elif np.isinf(expected_metrics[metric]):
+                self.assertTrue(np.isinf(metrics_dict[1][metric]))
+            else:
+                self.assertAlmostEqual(metrics_dict[1][metric], expected_metrics[metric])
+
+    def test_empty_ref_and_pred(self):
+        """
+        Empty reference and empty prediction
+        """
+
+        expected_metrics = {'EmptyPred': True,
+                            'EmptyRef': True,
+                            'dsc': 1,
+                            'fbeta': 1,
+                            'nsd': np.nan,
+                            'rel_vol_error': 0,
+                            'vol_diff': np.nan}
+
+        # Create empty reference
+        self.create_dummy_nii(self.ref_file, np.zeros((10, 10, 10)))
+        # Create empty prediction
+        self.create_dummy_nii(self.pred_file, np.zeros((10, 10, 10)))
+        # Compute metrics
+        metrics_dict = compute_metrics_single_subject(self.pred_file.name, self.ref_file.name, self.metrics)
+        # Assert metrics
+        self.assert_metrics(metrics_dict, expected_metrics)
+
+    def test_empty_ref(self):
+        """
+        Empty reference and non-empty prediction
+        """
+
+        expected_metrics = {'EmptyPred': False,
+                            'EmptyRef': True,
+                            'dsc': 0.0,
+                            'fbeta': 0,
+                            'nsd': 0.0,
+                            'rel_vol_error': 100,
+                            'vol_diff': np.inf}
+
+        # Create empty reference
+        self.create_dummy_nii(self.ref_file, np.zeros((10, 10, 10)))
+        # Create non-empty prediction
+        pred = np.zeros((10, 10, 10))
+        pred[5:7, 2:5] = 1
+        self.create_dummy_nii(self.pred_file, pred)
+        # Compute metrics
+        metrics_dict = compute_metrics_single_subject(self.pred_file.name, self.ref_file.name, self.metrics)
+        # Assert metrics
+        self.assert_metrics(metrics_dict, expected_metrics)
+
+    def test_empty_pred(self):
+        """
+        Non-empty reference and empty prediction
+        """
+
+        expected_metrics = {'EmptyPred': True,
+                            'EmptyRef': False,
+                            'dsc': 0.0,
+                            'fbeta': 0,
+                            'nsd': 0.0,
+                            'rel_vol_error': -100.0,
+                            'vol_diff': 1.0}
+
+        # Create non-empty reference
+        ref = np.zeros((10, 10, 10))
+        ref[5:7, 2:5] = 1
+        self.create_dummy_nii(self.ref_file, ref)
+        # Create empty prediction
+        self.create_dummy_nii(self.pred_file, np.zeros((10, 10, 10)))
+        # Compute metrics
+        metrics_dict = compute_metrics_single_subject(self.pred_file.name, self.ref_file.name, self.metrics)
+        # Assert metrics
+        self.assert_metrics(metrics_dict, expected_metrics)
+
+    def test_non_empty_ref_and_pred(self):
+        """
+        Non-empty reference and non-empty prediction
+        """
+
+        expected_metrics = {'EmptyPred': False,
+                            'EmptyRef': False,
+                            'dsc': 0.26666666666666666,
+                            'fbeta': 0.26666667461395266,
+                            'nsd': 0.5373134328358209,
+                            'rel_vol_error': 300.0,
+                            'vol_diff': 3.0}
+
+        # Create non-empty reference
+        ref = np.zeros((10, 10, 10))
+        ref[4:5, 3:6] = 1
+        self.create_dummy_nii(self.ref_file, ref)
+        # Create non-empty prediction
+        pred = np.zeros((10, 10, 10))
+        pred[4:8, 2:5] = 1
+        self.create_dummy_nii(self.pred_file, pred)
+        # Compute metrics
+        metrics_dict = compute_metrics_single_subject(self.pred_file.name, self.ref_file.name, self.metrics)
+        # Assert metrics
+        self.assert_metrics(metrics_dict, expected_metrics)
+
+
+if __name__ == '__main__':
+    unittest.main()