Spaces:
Sleeping
Sleeping
James McCool
commited on
Commit
·
17b7fee
1
Parent(s):
05f2b9c
Enhance predict_dupes function to include League of Legends (LOL) alongside CS2 for sport-specific logic, updating conditions for duplicate count calculations and own ratio nerf adjustments to improve accuracy in player predictions.
Browse files
global_func/predict_dupes.py
CHANGED
@@ -435,7 +435,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
|
|
435 |
np.round(portfolio['dupes_calc'], 0) - 1
|
436 |
)
|
437 |
elif type_var == 'Classic':
|
438 |
-
if sport_var == 'CS2':
|
439 |
dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
|
440 |
own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
|
441 |
calc_columns = ['own_product', 'own_average', 'own_sum', 'avg_own_rank', 'dupes_calc', 'low_own_count', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio']
|
@@ -481,7 +481,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
|
|
481 |
0,
|
482 |
np.round(portfolio['dupes_calc'], 0) - 1
|
483 |
)
|
484 |
-
elif sport_var != 'CS2':
|
485 |
num_players = len([col for col in portfolio.columns if col not in ['salary', 'median', 'Own']])
|
486 |
dup_count_columns = [f'player_{i}_percent_rank' for i in range(1, num_players + 1)]
|
487 |
own_columns = [f'player_{i}_own' for i in range(1, num_players + 1)]
|
@@ -515,9 +515,9 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
|
|
515 |
)
|
516 |
percentile_cut_scalar = portfolio['median'].max() # Get scalar value
|
517 |
if type_var == 'Classic':
|
518 |
-
if sport_var == 'CS2':
|
519 |
own_ratio_nerf = 2
|
520 |
-
elif sport_var != 'CS2':
|
521 |
own_ratio_nerf = 1.5
|
522 |
elif type_var == 'Showdown':
|
523 |
own_ratio_nerf = 1.5
|
|
|
435 |
np.round(portfolio['dupes_calc'], 0) - 1
|
436 |
)
|
437 |
elif type_var == 'Classic':
|
438 |
+
if sport_var == 'CS2' or sport_var == 'LOL':
|
439 |
dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
|
440 |
own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
|
441 |
calc_columns = ['own_product', 'own_average', 'own_sum', 'avg_own_rank', 'dupes_calc', 'low_own_count', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio']
|
|
|
481 |
0,
|
482 |
np.round(portfolio['dupes_calc'], 0) - 1
|
483 |
)
|
484 |
+
elif sport_var != 'CS2' and sport_var != 'LOL':
|
485 |
num_players = len([col for col in portfolio.columns if col not in ['salary', 'median', 'Own']])
|
486 |
dup_count_columns = [f'player_{i}_percent_rank' for i in range(1, num_players + 1)]
|
487 |
own_columns = [f'player_{i}_own' for i in range(1, num_players + 1)]
|
|
|
515 |
)
|
516 |
percentile_cut_scalar = portfolio['median'].max() # Get scalar value
|
517 |
if type_var == 'Classic':
|
518 |
+
if sport_var == 'CS2' or sport_var == 'LOL':
|
519 |
own_ratio_nerf = 2
|
520 |
+
elif sport_var != 'CS2' and sport_var != 'LOL':
|
521 |
own_ratio_nerf = 1.5
|
522 |
elif type_var == 'Showdown':
|
523 |
own_ratio_nerf = 1.5
|