Creating a constraint that changes the optimization based off what the model is solving.

[michaelrickarena]

It is currently my first time using the PuLP library in python. The purpose of diving into this library was to make a fantasy football solver in python. I have successfully made the solver but cant figure out how to add a few constraints I need.

I have an excel sheet of 400 players and how I project them to play, and I want to find the optimal combination of 9 players given specific constraints. The excel sheet holds, player name, player projection, team player is on, opponent player is facing, and position. Below is what the head of the panda data frame looks like.

              Name  Projection Position Team  Salary Opponent
0             Jets    3.528576      DST  NYJ    2000      IND
1           Texans    7.936528      DST  HOU    2100      PIT
2         Panthers    4.219883      DST  CAR    2200      LAC
3          Raiders    0.904948      DST  LVR    2300       NE

The constraints i have done successfully: limit a max of 9 players are selected, only 1 person of position QB, 3-4 of position WR, 1-2 of position TE, 1 position DST, and 2-3 position RB.

raw_data = pd.read_csv(file_name,engine="python",index_col=False, header=0, delimiter=",", quoting = 3)

#create new columns that has binary numbers if player == a specific position
raw_data["RB"] = (raw_data["Position"] == 'RB').astype(float)
raw_data["WR"] = (raw_data["Position"] == 'WR').astype(float)
raw_data["QB"] = (raw_data["Position"] == 'QB').astype(float)
raw_data["TE"] = (raw_data["Position"] == 'TE').astype(float)
raw_data["DST"] = (raw_data["Position"] == 'DST').astype(float)
raw_data["Salary"] = raw_data["Salary"].astype(float)

total_points = {}
cost = {}
QBs = {}
RBs = {}
WRs = {}
TEs = {}
DST = {}
number_of_players = {}


for i, player in raw_data.iterrows():
    var_name = 'x' + str(i) # Create variable name
    decision_var = pulp.LpVariable(var_name, cat='Binary') # Initialize Variables

    total_points[decision_var] = player["Projection"] # Create Projection Dictionary
    cost[decision_var] = player["Salary"] # Create Cost Dictionary
    
    # Create Dictionary for Player Types
    QBs[decision_var] = player["QB"]
    RBs[decision_var] = player["RB"]
    WRs[decision_var] = player["WR"]
    TEs[decision_var] = player["TE"]
    DST[decision_var] = player["DST"]
    number_of_players[decision_var] = 1.0

QB_constraint = pulp.LpAffineExpression(QBs)
RB_constraint = pulp.LpAffineExpression(RBs)
WR_constraint = pulp.LpAffineExpression(WRs)
TE_constraint = pulp.LpAffineExpression(TEs)
DST_constraint = pulp.LpAffineExpression(DST)
total_players = pulp.LpAffineExpression(number_of_players)

model += (QB_constraint == 1)
model += (RB_constraint <= 3)
model += (RB_constraint >= 2)
model += (WR_constraint <= 4)
model += (WR_constraint >= 3)
model += (TE_constraint <= 2)
model += (TE_constraint >= 1)
model += (DST_constraint == 1)
model += (total_players == 9)

The constraints I am trying to add and cant figure out how: have 2 players of the 9 selected be on the same team as the QB, the opponent of the DST cant be anyone of the 9's team, have 1 players opponent be the QB's team. Any idea how I would do this? This data is in my excel file but I'm not sure how to add these constraints to the model?

Ive been looking through the cases in the documentation and i cant find any examples where the optimal output is changed based off what the model picks. Example: if picks a quarterback, it affects the rest of the 8 players being selected.

Appreciate any help anyone can provide me.

[pchtsp]

Hello! You seemed to have cross posted in stack overflow ? https://stackoverflow.com/questions/64094589/python-pulp-optimization-problem-for-fantasy-football-how-to-add-certain-condit

In that case you can paste the solution if it indeed helped you. Next time, please add the link to the question in both sides, so not to repeat efforts.

To my surprise, there are a lot of questions and answers about fantasy football, pandas and pulp in stack overflow, most similar one to the other... (maybe a course, a test or an interview screening?) so you can also look at other questions...

[michaelrickarena]

Hi,

Yes I did cross post sorry I didn't realize that wasn't allowed. It's not for school just my own personal project. I have found other questions on fantasy football too but nothing regarding the constraints i was having issues with.

Sadly the post on stack overflow didn't really work as when i implemented his code, I got many errors and the solver still is not working.

attached in the pastebin is his solution that I tried to implement. But for this code none of the constraints are working now. I'm not sure why it's as broken as it is. Any help would be really appreciated.

https://pastebin.com/fSEVL0Vs

Issues ive been having with the solution suggested on stack overflow:

1. prob += pulp.lpSum([player_vars[i] for i in player_ids if raw_data['Position'] == 'RB']) >= 2 didn't work unless I put a .any() or .all() at the end of the ['Position'], im not sure if this is a reason why none of the constraints are working

2. None of the constraints are working, I'm not sure what's going on to be honest. Its outputting solutions with 3 people, 10 people, and no positional constraints. any help would be really appreciated.

[stumitchell]

you are currently getting help on stack overflow with you issue and Jacob seems to be very knowledgeable so I'd keep asking there.

[pchtsp]

Feel free to cross - post, it's okay. Just remember to add the link to the other site. Good luck!

…

On Tue, Sep 29, 2020, 21:39 Stuart Mitchell ***@***.***> wrote: you are currently getting help on stack overflow with you issue and Jacob seems to be very knowledgeable so I'd keep asking there. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#342 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABJUZ43YN6CLBKBS4VH6LT3SIIZW7ANCNFSM4R35CZRQ> .