Refactor similarity score calculation in predict_dupes.py by removing the reference to duplicate adjustments. This change clarifies the computation of player selection diversity while preserving the overall prediction model's integrity.

global_func/predict_dupes.py

@@ -425,7 +425,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
     portfolio['Weighted Own'] = portfolio[own_columns].apply(calculate_weighted_ownership_wrapper, axis=1)
     portfolio['Geomean'] = np.power((portfolio[own_columns] * 100).product(axis=1), 1 / len(own_columns))
     
-    # Calculate similarity score based on actual player selection and dupes
+    # Calculate similarity score based on actual player selection
     portfolio['Diversity'] = calculate_player_similarity_score_chunked(portfolio, player_columns)
     # check_portfolio = portfolio.copy()
     portfolio = portfolio.drop(columns=dup_count_columns)