minor refactoring

swisstopo · Apr 10, 2024 · 10e9c6e · 10e9c6e
1 parent 3f37f57
commit 10e9c6e
Showing 1 changed file with 13 additions and 8 deletions.
diff --git a/src/stratigraphy/benchmark/score.py b/src/stratigraphy/benchmark/score.py
@@ -79,6 +79,10 @@ def evaluate_matching(predictions: dict, number_of_truth_values: dict) -> tuple[
         "Number Elements": [],
         "Number wrong elements": [],
     }
+    # separate list to calculate the overall depth interval accuracy is required,
+    # as the depth interval accuracy is not calculated for documents with no correct
+    # material predictions.
+    depth_interval_accuracies = []
     for filename, file_prediction in predictions.items():
         hits = 0
         depth_interval_hits = 0
@@ -93,7 +97,7 @@ def evaluate_matching(predictions: dict, number_of_truth_values: dict) -> tuple[
                     depth_interval_occurences += 1
 
             if parse_text(layer.material_description.text) == "":
-                print("Empty string found in predictions")
+                logger.warning("Empty string found in predictions")
         tp = hits
         fp = len(file_prediction.layers) - tp
         fn = number_of_truth_values[filename] - tp
@@ -110,22 +114,24 @@ def evaluate_matching(predictions: dict, number_of_truth_values: dict) -> tuple[
         document_level_metrics["F1"].append(f1(precision, recall))
         document_level_metrics["Number Elements"].append(number_of_truth_values[filename])
         document_level_metrics["Number wrong elements"].append(fn + fp)
-        document_level_metrics["Depth_interval_accuracy"].append(depth_interval_hits / depth_interval_occurences)
+        try:
+            document_level_metrics["Depth_interval_accuracy"].append(depth_interval_hits / depth_interval_occurences)
+            depth_interval_accuracies.append(depth_interval_hits / depth_interval_occurences)
+        except ZeroDivisionError:
+            document_level_metrics["Depth_interval_accuracy"].append(None)
 
     if len(document_level_metrics["precision"]):
         overall_precision = sum(document_level_metrics["precision"]) / len(document_level_metrics["precision"])
         overall_recall = sum(document_level_metrics["recall"]) / len(document_level_metrics["recall"])
-        overall_depth_interval_accuracy = sum(document_level_metrics["Depth_interval_accuracy"]) / len(
-            document_level_metrics["Depth_interval_accuracy"]
-        )
+        overall_depth_interval_accuracy = sum(depth_interval_accuracies) / len(depth_interval_accuracies)
     else:
         overall_precision = 0
         overall_recall = 0
 
     logging.info("Macro avg:")
     logging.info(
-        f"F1: {f1(overall_precision, overall_recall):.1%},"
-        f"precision: {overall_precision:.1%}, recall: {overall_recall:.1%},"
+        f"F1: {f1(overall_precision, overall_recall):.1%}, "
+        f"precision: {overall_precision:.1%}, recall: {overall_recall:.1%}, "
         f"depth_interval_accuracy: {overall_depth_interval_accuracy:.1%}"
     )
 
@@ -191,7 +197,6 @@ def create_predictions_objects(predictions: dict, ground_truth_path: Path) -> tu
 
         predictions_objects[file_name] = prediction_object
         if ground_truth_is_present:
-            print(file_name)
             predictions_objects[file_name].evaluate(ground_truth.for_file(file_name))
             number_of_truth_values[file_name] = ground_truth.for_file(file_name).num_layers