diff --git a/main.py b/main.py
index 733b3f4..76da15f 100644
--- a/main.py
+++ b/main.py
@@ -241,7 +241,7 @@ def main_visualization():
         logger.warning(f"could not generate training curves: {e}")
 
     client_pred, server_pred = dataset.load_predictions(args.future_prediction)
-    client_pred, server_pred = client_pred.value, server_pred.value
+    client_pred, server_pred = client_pred.value.flatten(), server_pred.value.flatten()
     logger.info("plot pr curve")
     visualize.plot_clf()
     visualize.plot_precision_recall(client_val, client_pred)
@@ -259,7 +259,7 @@ def main_visualization():
 
     df_val = pd.DataFrame(data={"names": name_val, "client_val": client_val})
     user_vals = df_val.groupby(df_val.names).max().client_val.as_matrix().astype(float)
-    df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_val})
+    df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_pred})
     user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float)
 
     visualize.plot_clf()
@@ -276,10 +276,10 @@ def main_visualization():
     # visualize.plot_confusion_matrix(server_val.argmax(1), server_pred.argmax(1),
     #                                 "{}/server_cov.png".format(args.model_path),
     #                                 normalize=False, title="Server Confusion Matrix")
-    logger.info("visualize embedding")
-    domain_encs, labels = dataset.load_or_generate_domains(args.test_data, args.domain_length)
-    domain_embedding = np.load(args.model_path + "/domain_embds.npy")
-    visualize.plot_embedding(domain_embedding, labels, path="{}/embd.png".format(args.model_path))
+    # logger.info("visualize embedding")
+    # domain_encs, labels = dataset.load_or_generate_domains(args.test_data, args.domain_length)
+    # domain_embedding = np.load(args.model_path + "/domain_embds.npy")
+    # visualize.plot_embedding(domain_embedding, labels, path="{}/embd.png".format(args.model_path))
 
 
 def main_visualize_all():
@@ -303,6 +303,29 @@ def main_visualize_all():
     visualize.plot_legend()
     visualize.plot_save("all_client_roc.png")
 
+    df_val = pd.DataFrame(data={"names": name_val, "client_val": client_val})
+    user_vals = df_val.groupby(df_val.names).max().client_val.as_matrix().astype(float)
+
+    logger.info("plot user pr curves")
+    visualize.plot_clf()
+    for model_args in get_model_args(args):
+        client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"])
+        df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_pred.value.flatten()})
+        user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float)
+        visualize.plot_precision_recall(user_vals, user_preds, model_args["model_path"])
+    visualize.plot_legend()
+    visualize.plot_save("all_user_client_prc.png")
+
+    logger.info("plot user roc curves")
+    visualize.plot_clf()
+    for model_args in get_model_args(args):
+        client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"])
+        df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_pred.value.flatten()})
+        user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float)
+        visualize.plot_roc_curve(user_vals, user_preds, model_args["model_path"])
+    visualize.plot_legend()
+    visualize.plot_save("all_user_client_roc.png")
+
 
 def main_data():
     char_dict = dataset.get_character_dict()