improve case eval docu

README.md

@@ -210,8 +210,8 @@ task: Task000D3_Example
 name: "Example" # [Optional]
 dim: 3 # number of spatial dimensions of the data
 
-# TODO: check these
-target_class: # define class of interest for patient level evaluations # TODO: check if this should be included
+# Note: need to use integer value which is defined below of target class!
+target_class: 1 # [Optional] define class of interest for patient level evaluations
 test_labels: True # manually splitted test set
 
 labels: # classes of data set; need to start at 0

nndet/evaluator/case.py

@@ -15,7 +15,7 @@ limitations under the License.
 """
 
 from collections import defaultdict
-from typing import Dict, Sequence, Callable, Tuple, Union, Mapping
+from typing import Dict, Sequence, Callable, Tuple, Union, Mapping, Optional
 
 import numpy as np
 from loguru import logger
@@ -35,7 +35,7 @@ class _CaseEvaluator(AbstractEvaluator):
                  class_metrics_scalar: Mapping[str, Callable] = None,
                  score_metrics_curve: Mapping[str, Callable] = None,
                  class_metrics_curve: Mapping[str, Callable] = None,
-                 target_class: int = None,
+                 target_class: Optional[int] = None,
                  ):
         """
         Compute case level evaluation metrics
@@ -73,7 +73,10 @@ class _CaseEvaluator(AbstractEvaluator):
         self.score_metrics_curve = score_metrics_curve if score_metrics_curve is not None else {}
         self.class_metrics_curve = class_metrics_curve if class_metrics_curve is not None else {}
 
-        self.target_class = target_class
+        if isinstance(target_class, str):
+            raise ValueError(f"Need integer value of target class not the name!")
+
+        self.target_class = int(target_class)
         self.classes = classes
         self.num_classes = len(classes)
 

nndet/utils/check.py

@@ -115,6 +115,13 @@ def check_dataset_file(task_name: str):
         if ic != idx:
             raise ValueError("Found wrong order of modalities in dataset info."
                              f"Found {found_mods} but expected {list(range(len(found_mods)))}")
+    
+    # check target class
+    target_class = cfg.get("target_class", None)
+    if target_class is not None and not isinstance(target_class, int):
+        raise ValueError("If target class is defined, it needs to be an integer, "
+                         f"found {type(target_class)} : {target_class}")
+
     print("Dataset info check complete.")