Obtain Max Silohuette

app.py

@@ -603,7 +603,7 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
 
         df_heat = pd.read_csv(f"data/heatmaps_idefics2-patient.csv")
         # feature_options = [col for col in df_heat.columns if col != "name"]
-        # feature_options = ["grades", "table_pos", "v_density", "v_info_blocks", "layout", "columns"]
+        feature_options = ["grades", "table_pos", "v_density", "v_info_blocks", "layout", "columns"]
         feature_options = ["v_density"]
         
         silhouette_vals = []
@@ -612,7 +612,7 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
             silhouette = silhouette_score(reduced_real, labels)
             silhouette_vals.append(silhouette)
 
-        silhouette = np.mean(silhouette_vals)
+        silhouette = np.max(silhouette_vals)
 
     dfs_reduced, unique_subsets = split_versions(df_combined, reduced)