Update my_pages/multiverse.py

my_pages/multiverse.py

@@ -269,38 +269,35 @@ def render():
     
         y_pred = model.predict(X_test)
         return y_pred
-        
-    ### Main Code Starts Here
-    scaler, feature_sel, arch, seed = selected_path[0], selected_path[1], selected_path[2], int(selected_path[3])
 
-    all_preds = []
-    for scaler in choices_list[0]["options"]:
-        for feature_sel in choices_list[1]["options"]:
-            for arch in choices_list[2]["options"]:
-                for seed in choices_list[3]["options"]:
-                    seed = int(seed)
-                    y_pred_local = model_train_and_pred(scaler, feature_sel, arch, seed)
-                    all_preds.append(y_pred_local)
-                st.markdown(scaler + feature_sel + arch)
-                # all_preds_numpy = np.array(all_preds)
-                # np.save("all_predictions.npy", all_preds_numpy)
-
-    all_preds_numpy = np.array(all_preds)
-
-    from io import BytesIO
+    # all_preds = []
+    # for scaler in choices_list[0]["options"]:
+    #     for feature_sel in choices_list[1]["options"]:
+    #         for arch in choices_list[2]["options"]:
+    #             for seed in choices_list[3]["options"]:
+    #                 seed = int(seed)
+    #                 y_pred_local = model_train_and_pred(scaler, feature_sel, arch, seed)
+    #                 all_preds.append(y_pred_local)
+    #             st.markdown(scaler + feature_sel + arch)
+
+    # all_preds_numpy = np.array(all_preds)
+    # from io import BytesIO
     
-    # Create a BytesIO object
-    buffer = BytesIO()
-    np.save(buffer, all_preds_numpy)
-    buffer.seek(0)  # move to start
+    # # Create a BytesIO object
+    # buffer = BytesIO()
+    # np.save(buffer, all_preds_numpy)
+    # buffer.seek(0)  # move to start
     
-    st.download_button(
-        label="Download predictions",
-        data=buffer,
-        file_name="all_predictions.npy",
-        mime="application/octet-stream"
-    )
-    # np.save("all_predictions.npy", all_preds_numpy)
+    # st.download_button(
+    #     label="Download predictions",
+    #     data=buffer,
+    #     file_name="all_predictions.npy",
+    #     mime="application/octet-stream"
+    # )
+
+    ### Main Code Starts Here
+    scaler, feature_sel, arch, seed = selected_path[0], selected_path[1], selected_path[2], int(selected_path[3])
+    all_preds_numpy = np.load("all_predictions.npy")
     
     uniq_perc = 10.5
     add_red_text(f"""