UNKNOWN: Simple Input #7

carterlasalle · 2023-10-25T05:03:18Z

Hey!

Thanks so much for making this. It looks awesome. Im simply trying to create a config for an 86 squad with at least 1 prem player. How can I do this. I keep getting unknown. Thanks @Regista6 !

I only have these two uncommented. 
`model = create_league_constraint(df, model, player, map_idx, players_grouped, num_cnts)`
`model = create_league_constraint(df, model, player, map_idx, players_grouped, num_cnts)`

My config:
 `FORMATION = "4-3-2-1"
USE_PREFERRED_POSITION = False  
USE_ALTERNATE_POSITIONS = True

NUM_PLAYERS = 11 

FIX_PLAYERS = 1

LEAGUE = [["Premier League"]]
NUM_LEAGUE = [1]  

SQUAD_RATING = 86

# Comment out other constraints 
CLUB = []  
NUM_CLUB = []

COUNTRY = []
NUM_COUNTRY = []

RARITY_1 = []
NUM_RARITY_1 = []

RARITY_2 = []
NUM_RARITY_2 = []

MIN_OVERALL = []
NUM_MIN_OVERALL = []

MAX_NUM_CLUB = 0  
MIN_NUM_CLUB = 0
NUM_UNIQUE_CLUB = [0, "Exactly"]

MAX_NUM_LEAGUE = 0 
MIN_NUM_LEAGUE = 0
NUM_UNIQUE_LEAGUE = [0, "Exactly"]

MAX_NUM_COUNTRY = 0
MIN_NUM_COUNTRY = 0 
NUM_UNIQUE_COUNTRY = [0, "Exactly"]

CHEMISTRY = 0
CHEM_PER_PLAYER = 0

'''INPUTS'''

formation_dict = {
    "3-4-1-2": ["GK", "CB", "CB", "CB", "LM", "CM", "CM", "RM", "CAM", "ST", "ST"],
    "3-4-2-1": ["GK", "CB", "CB", "CB", "LM", "CM", "CM", "RM", "CF", "ST", "CF"],
    "3-1-4-2": ["GK", "CB", "CB", "CB", "LM", "CM", "CDM", "CM", "RM", "ST", "ST"],
    "3-4-3": ["GK", "CB", "CB", "CB", "LM", "CM", "CM", "RM", "CAM", "ST", "ST"],
    "3-5-2": ["GK", "CB", "CB", "CB", "CDM", "CDM", "LM", "CAM", "RM", "ST", "ST"],
    "3-4-3": ["GK", "CB", "CB", "CB", "LM", "CM", "CM", "RM", "LW", "ST", "RW"],
    "4-1-2-1-2": ["GK", "LB", "CB", "CB", "RB", "CDM", "LM", "CAM", "RM", "ST", "ST"],
    "4-1-2-1-2[2]": ["GK", "LB", "CB", "CB", "RB", "CDM", "CM", "CAM", "CM", "ST", "ST"],
    "4-1-4-1": ["GK", "LB", "CB", "CB", "RB", "CDM", "LM", "CM", "CM", "RM", "ST"],
    "4-2-1-3": ["GK", "LB", "CB", "CB", "RB", "CDM", "CDM", "CAM", "LW", "ST", "RW"],
    "4-2-3-1": ["GK", "LB", "CB", "CB", "RB", "CDM", "CDM", "CAM", "CAM", "CAM", "ST"],
    "4-2-3-1[2]": ["GK", "LB", "CB", "CB", "RB", "CDM", "CDM", "CAM", "LM", "ST", "RM"],
    "4-2-2-2": ["GK", "LB", "CB", "CB", "RB", "CDM", "CDM", "CAM", "CAM", "ST", "ST"],
    "4-2-4": ["GK", "LB", "CB", "CB", "RB", "CM", "CM", "LW", "ST", "ST", "RW"],
    "4-3-1-2": ["GK", "CB", "CB", "LB", "RB", "CM", "CM", "CM", "CAM", "ST", "ST"],
    "4-1-3-2": ["GK", "LB", "CB", "CB", "RB", "CDM", "LM", "CM", "RM", "ST", "ST"],
    "4-3-2-1": ["GK", "LB", "CB", "CB", "RB", "CM", "CM", "CM", "CF", "ST", "CF"],
    "4-3-3": ["GK", "LB", "CB", "CB", "RB", "CM", "CM", "CM", "LW", "ST", "RW"],
    "4-3-3[2]": ["GK", "LB", "CB", "CB", "RB", "CM", "CDM", "CM", "LW", "ST", "RW"],
    "4-3-3[3]": ["GK", "LB", "CB", "CB", "RB", "CDM", "CDM", "CM", "LW", "ST", "RW"],
    "4-3-3[4]": ["GK", "LB", "CB", "CB", "RB", "CM", "CM", "CAM", "LW", "ST", "RW"],
    "4-3-3[5]": ["GK", "LB", "CB", "CB", "RB", "CDM", "CM", "CM", "LW", "CF", "RW"],
    "4-4-1-1": ["GK", "LB", "CB", "CB", "RB", "CM", "CM", "LM", "CF", "RM", "ST"],
    "4-4-1-1[2]": ["GK", "LB", "CB", "CB", "RB", "CM", "CM", "LM", "CAM", "RM", "ST"],
    "4-4-2": ["GK", "LB", "CB", "CB", "RB", "LM", "CM", "CM", "RM", "ST", "ST"],
    "4-4-2[2]": ["GK", "LB", "CB", "CB", "RB", "LM", "CDM", "CDM", "RM", "ST", "ST"],
    "4-5-1": ["GK", "CB", "CB", "LB", "RB", "CM", "LM", "CAM", "CAM", "RM", "ST"],
    "4-5-1[2]": ["GK", "CB", "CB", "LB", "RB", "CM", "LM", "CM", "CM", "RM", "ST"],
    "5-2-1-2":["GK", "LWB", "CB", "CB", "CB", "RWB", "CM", "CM", "CAM", "ST", "ST"],
    "5-2-2-1": ["GK", "LWB", "CB", "CB", "CB", "RWB", "CM", "CM", "LW", "ST", "RW"],
    "5-3-2": ["GK", "LWB", "CB", "CB", "CB", "RWB", "CM", "CDM", "CM", "ST", "ST"],
    "5-4-1": ["GK", "LWB", "CB", "CB", "CB", "RWB", "CM", "CM", "LM", "RM", "ST"]
    }

status_dict = {
    0: "UNKNOWN: The status of the model is still unknown. A search limit has been reached before any of the statuses below could be determined.",
    1: "MODEL_INVALID: The given CpModelProto didn't pass the validation step.",
    2: "FEASIBLE: A feasible solution has been found. But the search was stopped before we could prove optimality.",
    3: "INFEASIBLE: The problem has been proven infeasible.",
    4: "OPTIMAL: An optimal feasible solution has been found."
}

def calc_squad_rating(rating):
    '''https://www.reddit.com/r/EASportsFC/comments/5osq7k/new_overall_rating_figured_out'''
    rat_sum = sum(rating)
    avg_rat = rat_sum / NUM_PLAYERS
    excess = sum(max(rat - avg_rat, 0) for rat in rating)
    return round(rat_sum + excess) // NUM_PLAYERS

LOG_RUNTIME = True
`

The text was updated successfully, but these errors were encountered:

carterlasalle · 2023-10-25T06:32:25Z

@Regista6 Actually I got it working, but its not optimizing

'''INPUTS'''

FORMATION = "4-3-2-1"

NUM_PLAYERS = 11

FIX_PLAYERS = 0  # FIX_PLAYERS = 1 => players will be picked based on the formation and 0 otherwise.

# Set only one of the below to True and the other to False. Both can't be False.
USE_PREFERRED_POSITION = False
USE_ALTERNATE_POSITIONS = True

# Set only one of the below to True and the others to False if duplicates are to be prioritized.
USE_ALL_DUPLICATES = False
USE_AT_LEAST_HALF_DUPLICATES = False
USE_AT_LEAST_ONE_DUPLICATE = False

LEAGUE = [["Premier League"]]
NUM_LEAGUE = [1]  # Exactly 1 player from Premier League

MIN_OVERALL = [86]
NUM_MIN_OVERALL = [11]  # All players should have a minimum OVR of 86

SQUAD_RATING = 86  # Squad Rating: Min 86

CHEMISTRY = 0  # Squad Total Chemistry Points: Min X
               # If there is no constraint on total chemistry, then set this to 0.

CHEM_PER_PLAYER = 0  # Chemistry Points Per Player: Min X.` 

OUTPUT:
`Processing time create_var: 0.01 seconds
Processing time create_basic_constraints: 0.0 seconds
Processing time create_league_constraint: 0.0 seconds
Processing time create_squad_rating_constraint_2: 0.08 seconds
Processing time create_chemistry_constraint: 0.05 seconds
Processing time set_objective: 0.0 seconds
Solve Started

Starting CP-SAT solver v9.7.2996
Parameters: max_time_in_seconds: 600 log_search_progress: true num_search_workers: 8

Initial optimization model '': (model_fingerprint: 0xdf950cf225fe2571)
#Variables: 1'916 (#bools: 158 in objective)
  - 1'354 Booleans in [0,1]
  - 404 in [0,3]
  - 158 in [0,70]
#kExactlyOne: 88 (#literals: 352)
#kIntProd: 668
#kLinMax: 158 (#expressions: 316)
#kLinear0: 16 (#enforced: 16)
#kLinear1: 883 (#enforced: 726)
#kLinear2: 127 (#enforced: 84)
#kLinear3: 234 (#enforced: 220)
#kLinearN: 182 (#enforced: 168) (#terms: 1'744)

Starting presolve at 0.00s
[ExtractEncodingFromLinear] #potential_supersets=38 #potential_subsets=0 #at_most_one_encodings=0 #exactly_one_encodings=0 #unique_terms=0 #multiple_terms=0 #literals=0 time=2.127e-05s
[SAT presolve] num removable Booleans: 867 / 1916
[SAT presolve] num trivial clauses: 0
[SAT presolve] [0s] clauses:1537 literals:3670 vars:701 one_side_vars:43 simple_definition:359 singleton_clauses:0
[SAT presolve] [0.0001525s] clauses:1525 literals:3586 vars:701 one_side_vars:43 simple_definition:359 singleton_clauses:0
[SAT presolve] [0.00051287s] clauses:1304 literals:3128 vars:604 one_side_vars:44 simple_definition:326 singleton_clauses:0
[Probing] implications and bool_or (work_done=66207).
[MaxClique] Merged 907(1858 literals) into 395(834 literals) at_most_ones. time=0.00078376s
[DetectDuplicateConstraints] #duplicates=265 #without_enforcements=65 time=0.00179708s
[DetectDominatedLinearConstraints] #relevant_constraints=5 #work_done=2222 #num_inclusions=1 #num_redundant=0 time=6.304e-05s
[ProcessSetPPC] #relevant_constraints=526 #num_inclusions=354 work=27564 time=0.00075596s
[FindBigHorizontalLinearOverlap] #blocks=0 #saved_nz=0 #linears=48 #work_done=23264/1e+09 time=0.00011919s
[FindBigVerticalLinearOverlap] #blocks=1 #nz_reduction=24 #work_done=162756 time=0.00034801s
[MergeClauses] #num_collisions=18 #num_merges=18 #num_saved_literals=40 work=523/100000000 time=0.00010899s
[SAT presolve] num removable Booleans: 1215 / 1917
[SAT presolve] num trivial clauses: 0
[SAT presolve] [0s] clauses:493 literals:1142 vars:383 one_side_vars:154 simple_definition:131 singleton_clauses:0
[SAT presolve] [0.0001301s] clauses:487 literals:1125 vars:383 one_side_vars:154 simple_definition:131 singleton_clauses:0
[SAT presolve] [0.00044299s] clauses:398 literals:921 vars:341 one_side_vars:156 simple_definition:122 singleton_clauses:0
[Probing] implications and bool_or (work_done=57999).
[MaxClique] Merged 358(760 literals) into 319(713 literals) at_most_ones. time=0.00084823s
[DetectDuplicateConstraints] #duplicates=33 #without_enforcements=30 time=0.00213823s
[DetectDominatedLinearConstraints] #relevant_constraints=6 #work_done=2231 #num_inclusions=1 #num_redundant=0 time=5.944e-05s
[ProcessSetPPC] #relevant_constraints=387 #num_inclusions=182 work=25129 time=0.00095384s
[FindBigHorizontalLinearOverlap] #blocks=0 #saved_nz=0 #linears=48 #work_done=23264/1e+09 time=0.00011659s
[FindBigVerticalLinearOverlap] #blocks=0 #nz_reduction=0 #work_done=160803 time=0.000334s
[MergeClauses] #num_collisions=20 #num_merges=20 #num_saved_literals=42 work=221/100000000 time=8.91e-05s
[SAT presolve] num removable Booleans: 1247 / 1917
[SAT presolve] num trivial clauses: 0
[SAT presolve] [0s] clauses:333 literals:765 vars:307 one_side_vars:187 simple_definition:78 singleton_clauses:0
[SAT presolve] [8.447e-05s] clauses:327 literals:753 vars:307 one_side_vars:187 simple_definition:78 singleton_clauses:0
[SAT presolve] [0.00026847s] clauses:327 literals:753 vars:307 one_side_vars:187 simple_definition:78 singleton_clauses:0
[Probing] implications and bool_or (work_done=57420).
[MaxClique] Merged 298(655 literals) into 258(593 literals) at_most_ones. time=0.00055135s
[DetectDuplicateConstraints] #duplicates=0 #without_enforcements=33 time=0.00153481s
[DetectDominatedLinearConstraints] #relevant_constraints=6 #work_done=2231 #num_inclusions=1 #num_redundant=0 time=7.152e-05s
[ProcessSetPPC] #relevant_constraints=389 #num_inclusions=178 work=25105 time=0.00061105s
[FindBigHorizontalLinearOverlap] #blocks=0 #saved_nz=0 #linears=48 #work_done=23264/1e+09 time=0.00011494s
[FindBigVerticalLinearOverlap] #blocks=0 #nz_reduction=0 #work_done=160767 time=0.00032618s
[MergeClauses] #num_collisions=20 #num_merges=20 #num_saved_literals=42 work=219/100000000 time=0.00011822s
[ExpandObjective] #propagations=0 #entries=112 #tight_variables=44 #tight_constraints=13 #expands=0 #issues=0 time=0.00014449s

Presolve summary:
  - 394 affine relations were detected.
  - rule 'TODO dual: add implied bound' was applied 902 times.
  - rule 'TODO dual: only one blocking constraint?' was applied 2752 times.
  - rule 'TODO dual: only one blocking enforced constraint?' was applied 902 times.
  - rule 'TODO dual: tighten at most one' was applied 125 times.
  - rule 'TODO duplicate: identical constraint with different enforcements' was applied 128 times.
  - rule 'TODO lin_max: linear expression above max.' was applied 1580 times.
  - rule 'TODO linear2: contains a Boolean.' was applied 362 times.
  - rule 'affine: new relation' was applied 394 times.
  - rule 'at_most_one: removed literals' was applied 103 times.
  - rule 'at_most_one: size one' was applied 103 times.
  - rule 'at_most_one: transformed into max clique.' was applied 3 times.
  - rule 'bool_and: fixed literals' was applied 1 time.
  - rule 'bool_and: removed duplicate literal' was applied 1 time.
  - rule 'bool_and: x => x' was applied 163 times.
  - rule 'bool_or: always true' was applied 64 times.
  - rule 'bool_or: implications' was applied 62 times.
  - rule 'bool_or: only one literal' was applied 191 times.
  - rule 'bool_or: removed enforcement literal' was applied 86 times.
  - rule 'deductions: 748 stored' was applied 1 time.
  - rule 'dual: add implication' was applied 12 times.
  - rule 'dual: enforced equivalence' was applied 63 times.
  - rule 'dual: fix variable' was applied 1 time.
  - rule 'dual: reduced domain' was applied 1 time.
  - rule 'duplicate: removed constraint' was applied 298 times.
  - rule 'enforcement: false literal' was applied 262 times.
  - rule 'enforcement: true literal' was applied 100 times.
  - rule 'exactly_one: removed literals' was applied 83 times.
  - rule 'exactly_one: size one' was applied 36 times.
  - rule 'exactly_one: size two' was applied 36 times.
  - rule 'exactly_one: x and not(x)' was applied 4 times.
  - rule 'incompatible linear: add implication' was applied 291 times.
  - rule 'int_prod: all Boolean.' was applied 440 times.
  - rule 'int_prod: constant product' was applied 141 times.
  - rule 'int_prod: expanded product with Boolean var' was applied 87 times.
  - rule 'int_prod: removed constant expressions.' was applied 211 times.
  - rule 'lin_max: target domain reduced' was applied 158 times.
  - rule 'linear + amo: added implications' was applied 7 times.
  - rule 'linear + amo: extracted enforcement literal' was applied 12 times.
  - rule 'linear + amo: fixed literal implied by enforcement' was applied 8 times.
  - rule 'linear matrix: common vertical rectangle' was applied 1 time.
  - rule 'linear1: is boolean implication' was applied 109 times.
  - rule 'linear2: implied ax + by = cte has only one solution' was applied 40 times.
  - rule 'linear: always true' was applied 42 times.
  - rule 'linear: empty' was applied 177 times.
  - rule 'linear: fixed or dup variables' was applied 560 times.
  - rule 'linear: infeasible' was applied 4 times.
  - rule 'linear: negative clause' was applied 70 times.
  - rule 'linear: positive at most one' was applied 21 times.
  - rule 'linear: positive clause' was applied 2 times.
  - rule 'linear: positive reified and' was applied 13 times.
  - rule 'linear: reduced variable domains' was applied 177 times.
  - rule 'linear: remapped using affine relations' was applied 218 times.
  - rule 'linear: simplified rhs' was applied 374 times.
  - rule 'linear: small Boolean expression' was applied 17 times.
  - rule 'linear: variable substitution 1' was applied 16 times.
  - rule 'presolve: 834 unused variables removed.' was applied 1 time.
  - rule 'presolve: iteration' was applied 3 times.
  - rule 'probing: bool_or reduced to implication' was applied 20 times.
  - rule 'probing: simplified clauses.' was applied 23 times.
  - rule 'probing: simplified enforcement list.' was applied 1 time.
  - rule 'setppc: bool_or in at_most_one.' was applied 20 times.
  - rule 'variables: detect half reified value encoding' was applied 487 times.

Presolved optimization model '': (model_fingerprint: 0xb4052d02ac0f790c)
#Variables: 673 (#bools: 158 in objective)
  - 358 Booleans in [0,1]
  - 6 in [0][8,70]
  - 43 in [0][22,70]
  - 13 in [0,2]
  - 143 in [0,3]
  - 1 in [0,6]
  - 49 in [0,10][32,70]
  - 23 in [0,20][42,70]
  - 25 in [0,30][52,70]
  - 4 in [0,40][62,70]
  - 6 in [0,50]
  - 2 in [0,60]
#kAtMostOne: 33 (#literals: 127)
#kBoolAnd: 245 (#enforced: 245 #multi: 17) (#literals: 566)
#kBoolOr: 23 (#literals: 104)
#kExactlyOne: 12 (#literals: 42)
#kLinMax: 158 (#expressions: 316)
#kLinear1: 214 (#enforced: 214 #multi: 6)
#kLinear2: 105 (#enforced: 105)
#kLinear3: 75 (#enforced: 74)
#kLinearN: 85 (#enforced: 80) (#terms: 1'134)

Preloading model.
#Bound   0.50s best:inf   next:[0,946250] initial_domain
#Model   0.50s var:673/673 constraints:950/950

Starting search at 0.50s with 8 workers.
6 full problem subsolvers: [core, default_lp, max_lp, no_lp, pseudo_costs, reduced_costs]
1 first solution subsolver: [jump]
8 incomplete subsolvers: [feasibility_pump, graph_arc_lns, graph_cst_lns, graph_dec_lns, graph_var_lns, rins/rens, rnd_cst_lns, rnd_var_lns]
3 helper subsolvers: [neighborhood_helper, synchronization_agent, update_gap_integral]
#Bound   0.97s best:inf   next:[7650,946250] default_lp
#Bound   1.05s best:inf   next:[7700,946250] reduced_costs
#Model  87.57s var:671/673 constraints:950/950
#1      88.93s best:120300 next:[7700,120250] rens_lp_lns(d=0.87 s=230 t=0.10 p=0.56 stall=0)
#2      94.81s best:117000 next:[7700,116950] rnd_var_lns(d=0.50 s=231 t=0.10 p=0.00 stall=0)
#Model  96.42s var:661/673 constraints:938/950
#Model 107.79s var:660/673 constraints:938/950
#Bound 109.86s best:117000 next:[7750,116950] reduced_costs
#3     144.53s best:107100 next:[7750,107050] rnd_cst_lns(d=0.50 s=232 t=0.10 p=0.00 stall=0)
#Bound 151.14s best:107100 next:[7800,107050] max_lp
#Model 151.21s var:655/673 constraints:938/950
#Model 156.21s var:653/673 constraints:938/950
#Model 161.63s var:651/673 constraints:938/950
#Bound 163.81s best:107100 next:[7850,107050] reduced_costs
#Model 170.35s var:650/673 constraints:938/950
#Bound 193.31s best:107100 next:[7900,107050] max_lp
#Model 193.32s var:649/673 constraints:938/950
60 seconds without improvement in objective. 

Task timing                        n [     min,      max]      avg      dev     time         n [     min,      max]      avg      dev    dtime
  'synchronization_agent':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns
    'neighborhood_helper':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns
             'default_lp':         1 [   3.40m,    3.40m]    3.40m   0.00ns    3.40m         1 [  12.08s,   12.08s]   12.08s   0.00ns   12.08s
                  'no_lp':         1 [   3.40m,    3.40m]    3.40m   0.00ns    3.40m         1 [  18.48s,   18.48s]   18.48s   0.00ns   18.48s
                 'max_lp':         1 [   3.40m,    3.40m]    3.40m   0.00ns    3.40m         1 [  23.62s,   23.62s]   23.62s   0.00ns   23.62s
                   'core':         1 [   3.40m,    3.40m]    3.40m   0.00ns    3.40m         1 [  16.53s,   16.53s]   16.53s   0.00ns   16.53s
          'reduced_costs':         1 [   3.40m,    3.40m]    3.40m   0.00ns    3.40m         1 [  10.57s,   10.57s]   10.57s   0.00ns   10.57s
           'pseudo_costs':         1 [   3.40m,    3.40m]    3.40m   0.00ns    3.40m         1 [  24.38s,   24.38s]   24.38s   0.00ns   24.38s
       'feasibility_pump':       112 [303.07us,  97.62ms]   1.88ms  10.45ms 211.07ms       111 [183.04us,  10.33ms] 274.45us 958.70us  30.46ms
              'rins/rens':       112 [140.57us,    9.94s] 783.49ms    2.41s    1.46m        11 [277.75us, 100.17ms]  72.92ms  44.29ms 802.15ms
            'rnd_var_lns':         2 [ 18.50ms,    5.88s]    2.95s    2.93s    5.90s         1 [100.10ms, 100.10ms] 100.10ms   0.00ns 100.10ms
            'rnd_cst_lns':         2 [939.85ms,   49.74s]   25.34s   24.40s   50.68s         2 [ 65.90us,  35.22ms]  17.64ms  17.58ms  35.28ms
          'graph_var_lns':         1 [   6.25s,    6.25s]    6.25s   0.00ns    6.25s         1 [100.01ms, 100.01ms] 100.01ms   0.00ns 100.01ms
          'graph_arc_lns':         1 [   6.40s,    6.40s]    6.40s   0.00ns    6.40s         1 [100.01ms, 100.01ms] 100.01ms   0.00ns 100.01ms
          'graph_cst_lns':         1 [  46.31s,   46.31s]   46.31s   0.00ns   46.31s         1 [100.02ms, 100.02ms] 100.02ms   0.00ns 100.02ms
          'graph_dec_lns':         1 [ 80.48ms,  80.48ms]  80.48ms   0.00ns  80.48ms         1 [  4.94us,   4.94us]   4.94us   0.00ns   4.94us
    'update_gap_integral':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns

Search stats        Bools  Conflicts  Branches  Restarts  BoolPropag  IntegerPropag
     'default_lp':    532     81'090   185'359     1'801   4'919'042      8'848'063
          'no_lp':    532    109'806   272'520     2'238   2'320'415      7'770'476
         'max_lp':    690      1'103     5'199     1'082      32'043         76'398
           'core':    532    114'708   277'308     1'065   2'772'134      7'711'895
  'reduced_costs':    690      4'409    11'024     1'110     652'389      1'235'589
   'pseudo_costs':    692        858     4'881     1'078      34'820         70'700

LNS stats           Improv/Calls  Closed  Difficulty  TimeLimit
      'rins/rens':         17/17     53%        0.83       0.10
    'rnd_var_lns':           1/2     50%        0.46       0.10
    'rnd_cst_lns':           2/2     50%        0.54       0.10
  'graph_var_lns':           0/1      0%        0.29       0.10
  'graph_arc_lns':           0/1      0%        0.29       0.10
  'graph_cst_lns':           0/1      0%        0.29       0.10
  'graph_dec_lns':           0/1    100%        0.71       0.10

Solutions (3)     Num   Rank
  'rens_lp_lns':    1  [1,1]
  'rnd_cst_lns':    1  [3,3]
  'rnd_var_lns':    1  [2,2]

Objective bounds     Num
      'default_lp':    1
  'initial_domain':    1
          'max_lp':    2
   'reduced_costs':    3

Solution repositories    Added  Queried  Ignored  Synchro
  'feasible solutions':      5       16        0        5
        'lp solutions':    176       16        0      172
                'pump':    111       96

Improving bounds shared    Num
                'max_lp':   64
          'pseudo_costs':   41
         'reduced_costs':   83

Clauses shared    Num
   'default_lp':    1

CpSolverResponse summary:
status: FEASIBLE
objective: 107100
best_bound: 7900
integers: 613
booleans: 532
conflicts: 81090
branches: 185359
propagations: 4919042
integer_propagations: 8848063
restarts: 1801
lp_iterations: 74382
walltime: 204.612
usertime: 204.612
deterministic_time: 109.097
gap_integral: 1256.47
solution_fingerprint: 0x8bbf9ed16336bff0

FEASIBLE: A feasible solution has been found. But the search was stopped before we could prove optimality.


Processing time SBC: 204.77 seconds
Total Chemistry: 2
Squad Rating: 86
Total Cost: 107100

Regista6 · 2023-10-25T07:05:18Z

Hi,
Thanks so much for using the repo 🙏.
Just for completeness, for your requirement, set formation, SQUAD_RATING = 86 # Squad Rating: Min XX and
LEAGUE = [["Premier League"]] and NUM_LEAGUE = [1]. Then comment out everything (L530-566) in optimize.py except create_squad_rating_constraint_3 and create_league_constraint.
Also, change Chemistry = 0 as there is no requirement on chemistry.

Can you send your club.csv?

status: FEASIBLE objective: 107100 best_bound: 7900
This is pretty bad.
Can you try uncommenting df = df[(df["Rating"] >= input.SQUAD_RATING - 3) & (df["Rating"] <= input.SQUAD_RATING + 3)] in main.py?

Edit: I think you have mistakenly set Processing time create_squad_rating_constraint_2: 0.08 seconds!

carterlasalle · 2023-10-25T22:12:29Z

@Regista6
Thanks so much for the writeback.

I am trying it on Trailblazers Challenge 5:

input.py: https://gist.github.com/RocketNinja15/2a048dcd18ee40484099b343db825cd8

optimize.py: https://gist.github.com/RocketNinja15/fcf3d3d12d8d89b9612d7a68178fedde

Requirements:
Min. Leagues in Squad: 4
Min. 3 Players from the same Nation Min. 2 Players from the same Club
Min. 9 Players: Rare
Min. Team Rating: 80
Min 31 Squad Total Chemistry Points
Number of Players in the Squad: 11
4-3-3[5]

Current output:

# This will create the directory 'FIFA-23-Automated-SBC-Solving'.
!git clone https://github.com/Regista6/EA-FC-24-Automated-SBC-Solving

Cloning into 'EA-FC-24-Automated-SBC-Solving'...
remote: Enumerating objects: 190, done.
remote: Counting objects: 100% (169/169), done.
remote: Compressing objects: 100% (119/119), done.
remote: Total 190 (delta 105), reused 95 (delta 48), pack-reused 21
Receiving objects: 100% (190/190), 2.50 MiB | 18.52 MiB/s, done.
Resolving deltas: 100% (109/109), done.

%cd /content/EA-FC-24-Automated-SBC-Solving

/content/EA-FC-24-Automated-SBC-Solving

!pip3 install -r requirements.txt

Requirement already satisfied: pandas>=1.5.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (1.5.3)
Requirement already satisfied: openpyxl>=3.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (3.1.2)
Collecting ortools>=9.7 (from -r requirements.txt (line 3))
  Downloading ortools-9.7.2996-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (21.1 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 21.1/21.1 MB 52.1 MB/s eta 0:00:00
Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.5.2->-r requirements.txt (line 1)) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.5.2->-r requirements.txt (line 1)) (2023.3.post1)
Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.5.2->-r requirements.txt (line 1)) (1.23.5)
Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.10/dist-packages (from openpyxl>=3.1->-r requirements.txt (line 2)) (1.1.0)
Requirement already satisfied: absl-py>=0.13 in /usr/local/lib/python3.10/dist-packages (from ortools>=9.7->-r requirements.txt (line 3)) (1.4.0)
Collecting protobuf>=4.23.3 (from ortools>=9.7->-r requirements.txt (line 3))
  Downloading protobuf-4.24.4-cp37-abi3-manylinux2014_x86_64.whl (311 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 311.6/311.6 kB 32.8 MB/s eta 0:00:00
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=1.5.2->-r requirements.txt (line 1)) (1.16.0)
Installing collected packages: protobuf, ortools
  Attempting uninstall: protobuf
    Found existing installation: protobuf 3.20.3
    Uninstalling protobuf-3.20.3:
      Successfully uninstalled protobuf-3.20.3
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.24.4 which is incompatible.
Successfully installed ortools-9.7.2996 protobuf-4.24.4

Processing time create_var: 0.03 seconds
Processing time create_basic_constraints: 0.01 seconds
Processing time create_min_club_constraint: 0.01 seconds
Processing time create_min_league_constraint: 0.01 seconds
Processing time create_min_country_constraint: 0.01 seconds
Processing time create_rarity_1_constraint: 0.0 seconds
Processing time create_squad_rating_constraint_3: 0.04 seconds
Processing time create_chemistry_constraint: 0.15 seconds
Processing time set_objective: 0.0 seconds
Solve Started

Starting CP-SAT solver v9.7.2996
Parameters: max_time_in_seconds: 600 log_search_progress: true num_search_workers: 8

Initial optimization model '': (model_fingerprint: 0xeb5cb57c7f66506f)
#Variables: 4'139 (#bools: 264 in objective)
  - 2'655 Booleans in [0,1]
  - 701 in [0,3]
  - 27 in [0,11]
  - 27 in [0,1500]
  - 729 in [0,15000]
#kBoolOr: 3 (#literals: 173)
#kExactlyOne: 173 (#literals: 692)
#kIntProd: 1'917
#kLinMax: 27 (#expressions: 54)
#kLinear0: 1
#kLinear1: 1'752 (#enforced: 1'525)
#kLinear2: 323 (#enforced: 254)
#kLinear3: 462 (#enforced: 432)
#kLinearN: 482 (#enforced: 443) (#terms: 4'051)

Starting presolve at 0.00s
INFEASIBLE: ''

Presolve summary:
  - 0 affine relations were detected.
Problem closed by presolve.
CpSolverResponse summary:
status: INFEASIBLE
objective: NA
best_bound: NA
integers: 0
booleans: 0
conflicts: 0
branches: 0
propagations: 0
integer_propagations: 0
restarts: 0
lp_iterations: 0
walltime: 0.00620084
usertime: 0.00620111
deterministic_time: 0
gap_integral: 0

INFEASIBLE: The problem has been proven infeasible.


Processing time SBC: 0.27 seconds

Here is my club file:
DonaldTrumpet.csv

Regista6 · 2023-10-26T04:51:10Z

# input.py
FORMATION = "4-3-3[5]"

NUM_PLAYERS = 11

FIX_PLAYERS  = 0 # FIX_PLAYERS = 1 => players will be picked based on the formation and 0 otherwise.

# Set only one of the below to True and the other to False. Both can't be False.
USE_PREFERRED_POSITION = False
USE_ALTERNATE_POSITIONS = True

# Set only one of the below to True and the others to False if duplicates are to be prioritized.
USE_ALL_DUPLICATES = False
USE_AT_LEAST_HALF_DUPLICATES = False
USE_AT_LEAST_ONE_DUPLICATE = False

NUM_UNIQUE_LEAGUE = [4, "Min"] # Leagues: Max / Min / Exactly X  (Min. Leagues in Squad: 4)

MIN_NUM_COUNTRY = 3  # Same Nation Count: Min X (Min. 3 Players from the same Nation)

MIN_NUM_CLUB = 2  # Same Club Count: Min X (Min. 2 Players from the same Club)

RARITY_2 = ["Rare"]  # (Min. 9 Players: Rare)
NUM_RARITY_2 = [9]   # Total players from i^th Rarity >= NUM_RARITY_2[i]

SQUAD_RATING = 80 # Squad Rating: Min XX

CHEMISTRY = 31  # Squad Total Chemistry Points: Min X

# optimize.py (These constraints should be enabled) (L530-566)

model = create_min_club_constraint(df, model, player, map_idx, players_grouped, num_cnts)
model = create_unique_league_constraint(df, model, player, league, map_idx, players_grouped, num_cnts)
model = create_min_country_constraint(df, model, player, map_idx, players_grouped, num_cnts)
model = create_rarity_2_constraint(df, model, player, map_idx, players_grouped, num_cnts)
model = create_squad_rating_constraint_3(df, model, player, map_idx, players_grouped, num_cnts)

# This is what I get with default main.py and your club! 
FEASIBLE: A feasible solution has been found. But the search was stopped before we could prove optimality.
Processing time SBC: 139.87 seconds
Total Chemistry: 31
Squad Rating: 80
Total Cost: 7750
# Also best_bound = 6950 (the optimal_cost lies between current_value and best_bound, current_value could also be proven optimal if the solver is given more time)

Name	Rating	Color	Rarity	Position	Is_Pos	Country	League	Club	Untradeable	Loans	IsDuplicate	IsInActive11	Cost	Chemistry
Borja Iglesias	83	Gold	Rare	CF	1	Spain	LALIGA EA SPORTS	Real Betis	TRUE	FALSE	FALSE	FALSE	900	3
Javi Galán	82	Gold	Rare	LB	1	Spain	LALIGA EA SPORTS	Atlético de Madrid	TRUE	FALSE	FALSE	FALSE	750	3
Andrich	82	Gold	Rare	CDM	1	Germany	Bundesliga	Leverkusen	TRUE	FALSE	FALSE	FALSE	750	2
Minge	81	Gold	Rare	CM	1	Germany	GPFBL	SC Freiburg	TRUE	FALSE	FALSE	FALSE	700	3
Sancet	81	Gold	Rare	CM	1	Spain	LALIGA EA SPORTS	Athletic Club	TRUE	FALSE	FALSE	FALSE	700	3
Reinildo	81	Gold	Rare	CB	1	Mozambique	LALIGA EA SPORTS	Atlético de Madrid	TRUE	FALSE	FALSE	FALSE	700	3
Karl	80	Gold	Rare	RB	1	Germany	GPFBL	SC Freiburg	TRUE	FALSE	FALSE	FALSE	1000	3
Anyomi	80	Gold	Rare	RW	1	Germany	GPFBL	Frankfurt	TRUE	FALSE	FALSE	FALSE	1000	3
Rubén Sobrino	74	Silver	Rare	LW	1	Spain	LALIGA EA SPORTS	Cádiz CF	TRUE	FALSE	FALSE	FALSE	700	3
Glauder	69	Silver	Common	CB	1	Spain	LALIGA HYPERMOTION	Albacete BP	TRUE	FALSE	FALSE	FALSE	250	2
von Schrader	68	Silver	Common	GK	1	Germany	GPFBL	TSG Hoffenheim	TRUE	FALSE	FALSE	FALSE	300	3

Regista6 self-assigned this Oct 25, 2023

Regista6 added the help wanted Extra attention is needed label Oct 25, 2023

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

UNKNOWN: Simple Input #7

UNKNOWN: Simple Input #7

carterlasalle commented Oct 25, 2023 •

edited by Regista6

Loading

carterlasalle commented Oct 25, 2023 •

edited by Regista6

Loading

Regista6 commented Oct 25, 2023 •

edited

Loading

carterlasalle commented Oct 25, 2023

Regista6 commented Oct 26, 2023 •

edited

Loading

UNKNOWN: Simple Input #7

UNKNOWN: Simple Input #7

Comments

carterlasalle commented Oct 25, 2023 • edited by Regista6 Loading

carterlasalle commented Oct 25, 2023 • edited by Regista6 Loading

Regista6 commented Oct 25, 2023 • edited Loading

carterlasalle commented Oct 25, 2023

Regista6 commented Oct 26, 2023 • edited Loading

carterlasalle commented Oct 25, 2023 •

edited by Regista6

Loading

carterlasalle commented Oct 25, 2023 •

edited by Regista6

Loading

Regista6 commented Oct 25, 2023 •

edited

Loading

Regista6 commented Oct 26, 2023 •

edited

Loading