Update my_pages/multiverse.py

my_pages/multiverse.py

@@ -297,11 +297,15 @@ def render():
 
     ### Main Code Starts Here
     scaler, feature_sel, arch, seed = selected_path[0], selected_path[1], selected_path[2], int(selected_path[3])
+    y_pred = model_train_and_pred(scaler, feature_sel, arch, seed)
     all_preds_numpy = np.load("all_predictions.npy")
+
+    prop_ones = np.mean(all_preds == 1, axis=0)
+    condition = (y_pred == 0) & (prop_ones >= 0.8)
+    uniq_perc = 100 * np.sum(condition) / len(y_pred)
     
-    uniq_perc = 10.5
     add_red_text(f"""
-        <b>Multiplicity of your predictions:</b> Your chosen model rejected {uniq_perc}% loans that would have been accepted by more than 90% of the other models.<br><br>
+        <b>Multiplicity of your predictions:</b> Your chosen model rejected {uniq_perc}% loans that would have been accepted by more than 80% of the other models.<br><br>
         You might want higher multiplicity, to avoid competiting for the same loan applicants with others and to give a chance to unique applicants.<br><br>
         On the other hand, you might instead want lower multiplicity, to take lower risks and to avoid facing a justification crisis, i.e., 
         not able to explain why you rejected certain applicants when they were accepted by everyone else.<br><br>