Add geometric mean calculation in predict_dupes: introduce a new column 'Goemean' to compute the geometric mean of ownership percentages, enhancing portfolio analysis capabilities.

global_func/predict_dupes.py

@@ -3,6 +3,7 @@ import numpy as np
 import pandas as pd
 import time
 from fuzzywuzzy import process
+import math
 
 def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, strength_var):
     if strength_var == 'Weak':
@@ -182,6 +183,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
     portfolio['Lineup Edge'] = portfolio.apply(lambda row: row['Lineup Edge'] / (row['Dupes'] + 1) if row['Dupes'] > 0 else row['Lineup Edge'], axis=1)
     portfolio['Lineup Edge'] = portfolio['Lineup Edge'] - portfolio['Lineup Edge'].mean()
     portfolio['Weighted Own'] = (portfolio['Own'] * ((portfolio[own_columns].max(axis=1) - portfolio[own_columns].min(axis=1)) / 100)) * 200
+    portfolio['Goemean'] = math.pow((portfolio[own_columns] * 100).product(axis=1), 1 / len(own_columns))
     portfolio = portfolio.drop(columns=dup_count_columns)
     portfolio = portfolio.drop(columns=own_columns)
     portfolio = portfolio.drop(columns=calc_columns)