Some more bug fixes in statistical tests

MStarmans91 · Aug 15, 2023 · bc8a1e9 · bc8a1e9
1 parent eed5ae3
commit bc8a1e9
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Showing 2 changed files with 13 additions and 3 deletions.
diff --git a/WORC/featureprocessing/StatisticalTestFeatures.py b/WORC/featureprocessing/StatisticalTestFeatures.py
@@ -80,7 +80,6 @@ def StatisticalTestFeatures(features, patientinfo, config, output_csv=None,
     if type(output_tex) is list:
         output_tex = ''.join(output_tex)
 
-    print(output_png, output_tex)
     # Create output folder if required
     if not os.path.exists(os.path.dirname(output_csv)):
         os.makedirs(os.path.dirname(output_csv))
@@ -153,7 +152,12 @@ def StatisticalTestFeatures(features, patientinfo, config, output_csv=None,
             pvalueswelch.append(ttest_ind(class1, class2, equal_var=False)[1])
             pvalueswil.append(ranksums(class1, class2)[1])
             try:
-                pvaluesmw.append(mannwhitneyu(class1, class2)[1])
+                pmwu = mannwhitneyu(class1, class2)[1]
+                if pmwu == 0.0:
+                    print("[WORC Warning] Mann-Whtiney U test resulted in a p-value of exactly 0.0, which is not valid. Replacing metric value by NaN.")
+                    pvaluesmw.append(np.nan)
+                else:
+                    pvaluesmw.append(pmwu)
             except ValueError as e:
                 print("[WORC Warning] " + str(e) + '. Replacing metric value by NaN.')
                 pvaluesmw.append(np.nan)

diff --git a/WORC/plotting/plot_pvalues_features.py b/WORC/plotting/plot_pvalues_features.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-# Copyright 2016-2020 Biomedical Imaging Group Rotterdam, Departments of
+# Copyright 2016-2023 Biomedical Imaging Group Rotterdam, Departments of
 # Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -115,6 +115,12 @@ def manhattan_importance(values, labels, feature_labels,
                  (1, y_value_annotated),
                  xytext=(1, y_value_annotated*0.95), size=8, color='magenta')
 
+    if 0.05 > 10**-yminlim:
+        plt.hlines(0.05, 0, max(positions),  linestyles='dashed', linewidth=1, color='magenta')
+        plt.annotate('p=0.05',
+                     (1, 0.05),
+                     xytext=(1, 0.05*0.95), size=8, color='magenta')
+
     plt.xlabel("Feature groups", size=12)
     plt.ylabel("P-value Mann-Whitney U", size=12)