minor

epdb/models.py

@@ -2815,18 +2815,55 @@ class PackageBasedModel(EPModel):
     )
     multigen_eval = models.BooleanField(null=False, blank=False, default=False)
 
+    def parameters(self):
+        params = {
+            "Model Evaluation Threshold": f"{self.threshold:.2f}",
+            "Multi Gen Evaluation": "Yes" if self.multigen_eval else "No",
+        }
+
+        if self.app_domain:
+            params["Applicability Domain Num Neighbors"] = f"{self.app_domain.num_neighbours:.2f}"
+            params["Applicability Domain Reliability Threshold"] = (
+                f"{self.app_domain.reliability_threshold:.2f}"
+            )
+            params["Applicability Domain Local Compatibility Threshold"] = (
+                f"{self.app_domain.local_compatibilty_threshold:.2f}"
+            )
+
+        return params
+
     def statistics(self):
+        from sklearn.metrics import auc
+
+        recall = list(self.eval_results["average_recall_per_threshold"].values())
+        precision = list(self.eval_results["average_precision_per_threshold"].values())
+        mg_recall = list(
+            self.eval_results.get("multigen_average_recall_per_threshold", {}).values()
+        )
+        mg_precision = list(
+            self.eval_results.get("multigen_average_precision_per_threshold", {}).values()
+        )
+
         return {
-            'accuracy': [
+            "accuracy": [
                 self.eval_results["average_accuracy"],
-                self.eval_results.get("multigen_average_accuracy")],
-            'precision': [
-                self.eval_results["average_precision_per_threshold"][f"{self.threshold:.2f}"],
-                self.eval_results.get("multigen_average_precision_per_threshold", {}).get(f"{self.threshold:.2f}")
+                self.eval_results.get("multigen_average_accuracy"),
             ],
-            'recall': [
+            "precision": [
+                self.eval_results["average_precision_per_threshold"][f"{self.threshold:.2f}"],
+                self.eval_results.get("multigen_average_precision_per_threshold", {}).get(
+                    f"{self.threshold:.2f}"
+                ),
+            ],
+            "recall": [
                 self.eval_results["average_recall_per_threshold"][f"{self.threshold:.2f}"],
-                self.eval_results.get("multigen_average_recall_per_threshold", {}).get(f"{self.threshold:.2f}")
+                self.eval_results.get("multigen_average_recall_per_threshold", {}).get(
+                    f"{self.threshold:.2f}"
+                ),
+            ],
+            "Area under PR Curve": [
+                auc(recall, precision),
+                auc(mg_recall, mg_precision) if self.multigen_eval else None,
             ],
         }
 
@@ -3053,8 +3090,6 @@ class PackageBasedModel(EPModel):
                 thresholds.append(np.float64(threshold))
                 thresholds.sort()
 
-            logger.info(f"Thresholds: {thresholds}")
-
             precision = {f"{t:.2f}": [] for t in thresholds}
             recall = {f"{t:.2f}": [] for t in thresholds}
 
@@ -3103,14 +3138,18 @@ class PackageBasedModel(EPModel):
             for t in thresholds:
                 for true, pred in zip(pathways, pred_pathways):
                     acc, pre, rec = multigen_eval(true, pred, t)
-                    if abs(t - threshold) < 0.01:
-                        mg_acc = acc
+
+                    if f"{t:.2f}" == f"{threshold:.2f}":
+                        mg_acc += acc
+
                     precision[f"{t:.2f}"].append(pre)
                     recall[f"{t:.2f}"].append(rec)
 
+            avg_mg_acc = mg_acc / len(root_compounds)
             precision = {k: sum(v) / len(v) if len(v) > 0 else 0 for k, v in precision.items()}
             recall = {k: sum(v) / len(v) if len(v) > 0 else 0 for k, v in recall.items()}
-            return mg_acc, precision, recall
+            logger.info("Average Multigen Accuracy: {:.2f}".format(avg_mg_acc))
+            return avg_mg_acc, precision, recall
 
         # If there are eval packages perform single generation evaluation on them instead of random splits
         if self.eval_packages.count() > 0:

epdb/tasks.py

@@ -477,8 +477,7 @@ def batch_predict(
             limit=None,
             setting_overrides={
                 "max_nodes": num_tps,
-                "max_depth": num_tps,
-                "model_threshold": 0.001,
+                "model_threshold": 0.0,
             },
         )