fix: working predict.py for a single model

aviary/predict.py

@@ -111,9 +111,9 @@ def make_ensemble_predictions(
         else:
             df[pred_col] = preds
 
-    if len(checkpoint_paths) > 1:
-        df_preds = df.filter(regex=r"_pred_\d")
+    df_preds = df.filter(regex=r"_pred_\d")
 
+    if len(checkpoint_paths) > 1:
         pred_ens_col = f"{target_col}_pred_ens" if target_col else "pred_ens"
         df[pred_ens_col] = ensemble_preds = df_preds.mean(axis=1)
 
@@ -134,7 +134,7 @@ def make_ensemble_predictions(
             ).mean(axis=1)
             df[pred_tot_std_ens] = (epistemic_std**2 + aleatoric_std**2) ** 0.5
 
-    if target_col:
+    if target_col is not None:
         targets = df[target_col]
         all_model_metrics = [
             get_metrics(targets, df_preds[col], task_type) for col in df_preds
@@ -145,11 +145,12 @@ def make_ensemble_predictions(
             print("\nSingle model performance:")
             print(df_metrics.describe().round(4).loc[["mean", "std"]])
 
-            ensemble_metrics = get_metrics(targets, ensemble_preds, task_type)
+            if len(checkpoint_paths) > 1:
+                ensemble_metrics = get_metrics(targets, ensemble_preds, task_type)
 
-            print("\nEnsemble performance:")
-            for key, val in ensemble_metrics.items():
-                print(f"{key:<8} {val:.3}")
+                print("\nEnsemble performance:")
+                for key, val in ensemble_metrics.items():
+                    print(f"{key:<8} {val:.3}")
         return df, df_metrics
 
     return df
@@ -208,7 +209,7 @@ def predict_from_wandb_checkpoints(
         if not os.path.isfile(checkpoint_path):
             run.file(f"{checkpoint_filename}").download(root=out_dir)
 
-    if target_col in kwargs:
+    if target_col is not None:
         df, ensemble_metrics = make_ensemble_predictions(checkpoint_paths, **kwargs)
         # round to save disk space and speed up cloud storage uploads
         return df.round(6), ensemble_metrics