add class label option to write metric report to improve readability … (#7249)

add class label option to write metric report to improve readability,
without that option in case of many classes the resulting report is very
hard to interpret.


### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).

---------

Signed-off-by: elitap <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: elitap

monai/handlers/utils.py

@@ -61,6 +61,7 @@ def write_metrics_reports(
     summary_ops: str | Sequence[str] | None,
     deli: str = ",",
     output_type: str = "csv",
+    class_labels: list[str] | None = None,
 ) -> None:
     """
     Utility function to write the metrics into files, contains 3 parts:
@@ -94,6 +95,8 @@ class    mean    median    max    5percentile 95percentile  notnans
         deli: the delimiter character in the saved file, default to "," as the default output type is `csv`.
             to be consistent with: https://docs.python.org/3/library/csv.html#csv.Dialect.delimiter.
         output_type: expected output file type, supported types: ["csv"], default to "csv".
+        class_labels: list of class names used to name the classes in the output report, if None,
+            "class0", ..., "classn" are used, default to None.
 
     """
     if output_type.lower() != "csv":
@@ -118,7 +121,12 @@ class    mean    median    max    5percentile 95percentile  notnans
                 v = v.reshape((-1, 1))
 
             # add the average value of all classes to v
-            class_labels = ["class" + str(i) for i in range(v.shape[1])] + ["mean"]
+            if class_labels is None:
+                class_labels = ["class" + str(i) for i in range(v.shape[1])]
+            else:
+                class_labels = [str(i) for i in class_labels]  # ensure to have a list of str
+
+            class_labels += ["mean"]
             v = np.concatenate([v, np.nanmean(v, axis=1, keepdims=True)], axis=1)
 
             with open(os.path.join(save_dir, f"{k}_raw.csv"), "w") as f: