remove incorrect rmse refs

src/streamlit_app.py

@@ -54,13 +54,13 @@ if show_home:
     if current_scoring_mode not in scoring_options:
         current_scoring_mode = scoring_options[0]
 
-    current_rmse_weight_percent = int(st.session_state.get("rmse_weight_percent", 80))
-    current_rmse_weight_percent = max(0, min(100, current_rmse_weight_percent))
+    current_fwdruns_weight_percent = int(st.session_state.get("fwdruns_weight_percent", 80))
+    current_fwdruns_weight_percent = max(0, min(100, current_fwdruns_weight_percent))
 
     selected_target = current_target
     scoring_mode = current_scoring_mode
-    rmse_weight = current_rmse_weight_percent / 100.0
-    ensemble_weight = 1.0 - rmse_weight
+    fwdruns_weight = current_fwdruns_weight_percent / 100.0
+    ensemble_weight = 1.0 - fwdruns_weight
 
     def build_scored_table(input_df, add_rank=True):
         ranking_source = input_df if selected_target == "All targets" else input_df[input_df["rmse_target_str"] == selected_target]
@@ -138,7 +138,7 @@ if show_home:
             ascending = [True, True, True]
         else:
             scored_df["Score"] = (
-                rmse_weight * scored_df["mean_runs_score"]
+                fwdruns_weight * scored_df["mean_runs_score"]
                 + ensemble_weight * scored_df["ensemble_score"]
             )
             sort_columns = ["Score", "Mean Forward Model Runs", "Optimal Ensemble Size", "abbreviation"]
@@ -216,7 +216,7 @@ if show_home:
     else:
         score_basis = (
             "weighted blend of normalized forward-model-runs score and normalized ensemble-size score "
-            f"(forward-runs weight {rmse_weight:.0%}, ensemble-size weight {ensemble_weight:.0%})"
+            f"(forward-runs weight {fwdruns_weight:.0%}, ensemble-size weight {ensemble_weight:.0%})"
         )
 
     if selected_benchmark == "All":
@@ -294,7 +294,7 @@ if show_home:
                 min_value=0,
                 max_value=100,
                 step=5,
-                key="rmse_weight_percent",
+                key="fwdruns_weight_percent",
                 help="Higher forward-runs weight prioritizes fewer model evaluations; higher ensemble-size weight prioritizes smaller ensembles.",
             )