Evaluation table shows if threshold chosen a priori or posteriori

src/ptbench/engine/evaluator.py

@@ -48,7 +48,7 @@ def eer_threshold(neg: Iterable[float], pos: Iterable[float]) -> float:
     fpr, tpr, thresholds = metrics.roc_curve(y_true, y_predictions, pos_label=1)
 
     eer = brentq(lambda x: 1.0 - x - interp1d(fpr, tpr)(x), 0.0, 1.0)
-    return interp1d(fpr, thresholds)(eer)
+    return float(interp1d(fpr, thresholds)(eer))
 
 
 def posneg(
@@ -198,12 +198,11 @@ def run(
     ---------
 
     name:
-        the local name of this dataset (e.g. ``train``, or ``test``), to be
-        used when saving measures files.
+        The name of subset to load.
 
     predictions_folder:
-        folder where predictions for the dataset images has been previously
-        stored
+        Folder where predictions for the dataset images has been previously
+        stored.
 
     f1_thresh:
         This number should come from
@@ -224,11 +223,17 @@ def run(
     Returns
     -------
 
-    maxf1_threshold : float
-        Threshold to achieve the highest possible F1-score for this dataset
+    pred_data:
+        The loaded predictions for the specified subset.
 
-    post_eer_threshold : float
-        Threshold achieving Equal Error Rate for this dataset
+    fig_scores:
+        Figure of the histogram distributions of true-positive/true-negative scores.
+
+    maxf1_threshold:
+        Threshold to achieve the highest possible F1-score for this dataset.
+
+    post_eer_threshold:
+        Threshold achieving Equal Error Rate for this dataset.
     """
     predictions_path = os.path.join(predictions_folder, f"{name}.csv")
 

src/ptbench/scripts/evaluate.py

@@ -224,9 +224,12 @@ def evaluate(
         for subset_name in dataloader.keys():
             data[subset_name] = {
                 "df": results_dict["pred_data"][subset_name],
-                "threshold": results_dict["post_eer_threshold"][  # type: ignore
-                    threshold
-                ].item(),
+                "threshold": results_dict["post_eer_threshold"][threshold]
+                if isinstance(threshold, str)
+                else eer_threshold,
+                "threshold_type": f"posteriori [{threshold}]"
+                if isinstance(threshold, str)
+                else "priori",
             }
 
         output_figure = os.path.join(output_folder, "plots.pdf")

src/ptbench/utils/table.py

@@ -47,6 +47,7 @@ def performance_table(data, fmt):
     headers = [
         "Dataset",
         "T",
+        "T Type",
         "F1 (95% CI)",
         "Prec (95% CI)",
         "Recall/Sen (95% CI)",
@@ -61,6 +62,7 @@ def performance_table(data, fmt):
         entry = [
             k,
             v["threshold"],
+            v["threshold_type"],
         ]
 
         df = v["df"]