PuLP优化求解仅返回唯一值：梦幻足球阵容球员排重约束实现

你只需要按球员姓名对数据集分组，为每个球员添加「所有对应实例的选中变量之和≤1」的约束即可，修改方案如下：

完整修改后代码 #

import pandas as pd
import pulp

print('--- (1/4) 定义问题 ---')

# 读取csv
raw_data = pd.read_csv('./csv/fantasypros.csv')

# 对位置做独热编码
encoded = pd.get_dummies(raw_data['Pos. Parent'])
raw_data = raw_data.join(encoded)

raw_data["salary"] = raw_data["Point Cost"].astype(float)

model = pulp.LpProblem("NFTdraft", pulp.LpMaximize)

total_points = {}
cost = {}
qb = {}
rb = {}
wr = {}
te = {}
k = {}
dst = {}
dk = {}
num_players = {}
# 新增：存储每个球员对应的所有决策变量
player_var_map = {}

# 遍历球员生成决策变量
for i, player in raw_data.iterrows():
    var_name = 'x' + str(i)
    decision_var = pulp.LpVariable(var_name, cat='Binary')
    
    total_points[decision_var] = player["FPTS"]
    cost[decision_var] = player["salary"]
    
    qb[decision_var] = player["QB"]
    rb[decision_var] = player["RB"]
    wr[decision_var] = player["WR"]
    te[decision_var] = player["TE"]
    k[decision_var] = player["K"]
    dst[decision_var] = player["DST"]
    dk[decision_var] = player["DK"]
    num_players[decision_var] = 1.0
    
    # 新增：把当前决策变量加入对应球员的变量列表
    player_name = player["Player"]
    if player_name not in player_var_map:
        player_var_map[player_name] = []
    player_var_map[player_name].append(decision_var)

# 定义目标函数：最大化总得分
objective_function = pulp.LpAffineExpression(total_points)
model += objective_function

# 薪资帽约束
total_cost = pulp.LpAffineExpression(cost)
model += (total_cost <= 135)

print('--- (2/4) 定义约束 ---')
# 新增：同一球员最多选中1个实例的约束
for player_name, vars in player_var_map.items():
    model += (pulp.lpSum(vars) <= 1, f"single_{player_name}_constraint")

# 位置约束
QB_constraint = pulp.LpAffineExpression(qb)
RB_constraint = pulp.LpAffineExpression(rb)
WR_constraint = pulp.LpAffineExpression(wr)
TE_constraint = pulp.LpAffineExpression(te)
K_constraint = pulp.LpAffineExpression(k)
DST_constraint = pulp.LpAffineExpression(dst)
DK_constraint = pulp.LpAffineExpression(dk)
total_players = pulp.LpAffineExpression(num_players)

model += (QB_constraint >= 1)
model += (QB_constraint <= 2)
model += (RB_constraint <= 8)
model += (WR_constraint <= 8)
model += (TE_constraint <= 8)
model += (K_constraint <= 1)
model += (DST_constraint <= 1)
model += (DK_constraint <= 2)
model += (total_players == 10)

print('--- (3/4) 求解问题 ---')
model.solve()

print('--- (4/4) 格式化结果 ---')
raw_data["is_drafted"] = 0.0

for var in model.variables():
    raw_data.loc[int(var.name[1:]), 'is_drafted'] = var.varValue

my_team = raw_data[raw_data["is_drafted"] == 1.0]
my_team = my_team[["Asset Name", "Player", "Pos. Parent", "Rarity", "Point Cost", "FPTS"]]

print(my_team)
print("总薪资消耗：{}".format(my_team["Point Cost"].sum()))
print("预期总得分：{}".format(my_team["FPTS"].sum().round(1)))
print('--- 执行完成 ---')

核心修改说明 #

• 新增player_var_map字典，按球员姓名存储所有同名人的决策变量
• 遍历所有球员分组，添加约束：每组内的决策变量求和不超过1，确保同一个球员最多只能有一个实例被选入阵容
• 原有逻辑全部保留，不会影响位置、薪资、阵容人数等原有约束的生效

内容的提问来源于stack exchange，提问作者python_noob_5