Docs tests

MetricsReloaded/metrics/pairwise_measures.py

@@ -1200,6 +1200,7 @@ def measured_distance(self):
         and masd
 
         """
+
         if "hd_perc" in self.dict_args.keys():
             perc = self.dict_args["hd_perc"]
         else:
@@ -1208,13 +1209,19 @@ def measured_distance(self):
         if np.sum(self.pred + self.ref) == 0:
             warnings.warn("Prediction and reference empty - distances set to 0")
             return 0, 0, 0, 0
+        if np.sum(self.pred) == 0 and np.sum(self.ref)>0:
+            warnings.warn("Prediction empty but reference not empty - need to set to worse case in aggregation")
+            return np.nan, np.nan, np.nan, np.nan
+        if np.sum(self.ref) == 0 and np.sum(self.pred)>0:
+            warnings.warn('Prediction not empty but reference empty - non existing output - need be set to WORSE case in aggregation')
+            return np.nan, np.nan, np.nan, np.nan
         (
             ref_border_dist,
             pred_border_dist,
             ref_border,
             pred_border,
         ) = self.border_distance()
-        print(ref_border_dist)
+        # print(ref_border_dist)
         average_distance = (np.sum(ref_border_dist) + np.sum(pred_border_dist)) / (
             np.sum(pred_border + ref_border)
         )

test/test_metrics/test_calibration_metrics.py

@@ -71,6 +71,7 @@ def test_brier_score():
     expected_bs = 0.4
     assert_allclose(expected_bs, value_test, atol=0.01)
 
+#To use SN 2.14 p 99 of Metrics Reloaded
 
 def test_top_label_classification_error():
     ref_tce = [1, 0, 2, 1]

test/test_metrics/test_pairwise_measures.py

@@ -46,7 +46,7 @@
 ppm212_1 = PM(pred212, ref212)
 ppm212_2 = PM(pred212,ref212,dict_args={'boundary_dist':2})
 
-#Data for figure 5c (Hausdoff with annotation error p14 Pitfalls)
+#Data for figure 5c (Hausdorff with annotation error p14 Pitfalls)
 ref5c = np.zeros([14, 14])
 ref5c[1, 1] = 1
 ref5c[9:12, 9:12] = 1
@@ -630,6 +630,7 @@ def test_mcc():
     assert mcc < 1
 
 
+
 def test_distance_empty():
     """
     Testing that output is 0 when reference and prediction empty for calculation of distance
@@ -833,6 +834,30 @@ def test_hausdorff_distance_5c():
         hausdorff_distance_perc, expected_hausdorff_distance_perc, atol=0.01
     )
 
+def test_distance_empty_ref():
+    ppm1 = PM(pred29_1, ref29_1*0)
+    hd, hd_perc, masd, assd = ppm1.measured_distance()
+    assert np.isnan(hd)
+    assert np.isnan(hd_perc)
+    assert np.isnan(masd)
+    assert np.isnan(assd)
+
+def test_distance_empty_pred():
+    ppm1 = PM(pred29_1*0, ref29_1)
+    hd, hd_perc, masd, assd = ppm1.measured_distance()
+    assert np.isnan(hd)
+    assert np.isnan(hd_perc)
+    assert np.isnan(masd)
+    assert np.isnan(assd)
+
+
+def test_distance_empty_pred_and_ref():
+    ppm1 = PM(pred29_1*0, ref29_1*0)
+    hd, hd_perc, masd, assd = ppm1.measured_distance()
+    assert hd == 0
+    assert hd_perc == 0
+    assert masd == 0
+    assert assd == 0
 
 def test_boundary_iou():
     """

test/test_processes/test_mixed_measures_processes.py

@@ -9,26 +9,26 @@
 from sklearn.metrics import cohen_kappa_score as cks
 from sklearn.metrics import matthews_corrcoef as mcc
 
-# panoptic quality
-pq_pred1 = np.zeros([21, 21])
-pq_pred1[5:7, 2:5] = 1
-pq_pred2 = np.zeros([21, 21])
-pq_pred2[14:18, 4:6] = 1
-pq_pred2[16, 3] = 1
-pq_pred3 = np.zeros([21, 21])
-pq_pred3[14:18, 7:12] = 1
-pq_pred4 = np.zeros([21, 21])
-pq_pred4[2:8, 13:16] = 1
-pq_pred4[2:4, 12] = 1
+# Data for panoptic quality Figure 3.51 p96
+pq_pred1 = np.zeros([18, 18])
+pq_pred1[ 3:7,1:3] = 1
+pq_pred1[3:6,3:7]=1
+pq_pred2 = np.zeros([18, 18])
+pq_pred2[13:16,4:6] = 1
+pq_pred3 = np.zeros([18, 18])
+pq_pred3[7:12,13:17] = 1
+pq_pred4 = np.zeros([18, 18])
+pq_pred4[13:15,13:17] = 1
+pq_pred4[15,15] = 1
 
-pq_ref1 = np.zeros([21, 21])
-pq_ref1[8:11, 3] = 1
-pq_ref1[9, 2:5] = 1
-pq_ref2 = np.zeros([21, 21])
-pq_ref2[14:19, 7:13] = 1
-pq_ref3 = np.zeros([21, 21])
-pq_ref3[2:7, 14:17] = 1
-pq_ref3[2:4, 12:14] = 1
+pq_ref1 = np.zeros([18, 18])
+pq_ref1[2:7, 1:3] = 1
+pq_ref1[2:5,3:6] = 1
+pq_ref2 = np.zeros([18, 18])
+pq_ref2[6:12,12:17] = 1
+pq_ref3 = np.zeros([18, 18])
+pq_ref3[14:15:,7:10] = 1
+pq_ref3[13:16,8:9] = 1
 
 
 def test_mismatch_category():

test/test_utility/test_assignment_localization.py

@@ -27,7 +27,11 @@
 pred_boxes_6a = [pred6a1, pred6a2, pred6a3, pred6a4, pred6a5]
 ref_boxes_6a = [ref6a1, ref6a2, ref6a3, ref6a4]
 
-#Data from Panoptic Quality - 3.51 p96
+#Data from SN2.17 from Metrics Reloaded
+
+
+
+#Data from Panoptic Quality - 3.51 p96 of Pitfalls
 #Figure 3.51 p96
 pq_pred1 = np.zeros([18, 18])
 pq_pred1[ 3:7,1:3] = 1