KeyError：0 尝试运行 LPP 时-解网

问：

我尝试使用 python 构建和解决线性规划问题，但是在运行时收到以下错误：

KeyError: 0

以下是我正在使用的代码：

bfast = 1
lunchanddinner = 2
mealstypes = list(set(meals_dataset.Type))
constraints = {'breakfast':bfast,'lunch_dinner':lunchanddinner}
meals = meals_dataset.Meal.tolist()
calories = dict( zip( meals, np.array(meals_dataset.Calories.tolist())))
x = pulp.LpVariable.dicts( "x", indexs = meals, lowBound=0, upBound=1, cat='Integer', indexStart=[])
proteins = meals_dataset.Protein.tolist()
def model(minprotein):
  prob = pulp.LpProblem("Meal Plan", LpMinimize)
  prob += pulp.lpSum( [ x[i]*calories[i] for i in meals])
  for i in range(len(mealstypes)):
    type_data = meals_dataset[meals_dataset.Type==mealstypes[i]]
    boundary_condition = np.arange(type_data.index.min(),type_data.index.max())
    const = constraints[mealstypes[i]]
    prob += pulp.lpSum( [ x[j] for j in boundary_condition ] )==const
  prob += pulp.lpSum( [ x[i]*proteins[i] for i in range(len(x))])<= minprotein
  return prob

def find_best_plan(minprotein):
  prob = model(minprotein)
  prob.solve()
  variables = []
  values = []
  for v in prob.variables():
    variable = v.name
    value = v.varValue
    variables.append(variable)
    values.append(value)
  values = np.array(values).astype(int)
  meal_list = pd.DataFrame(np.array([variables,values]).T,columns = ['Variable','Optimal Value'])
  meal_list['Optimal Value'] = meal_list['Optimal Value'].astype(int)
  squad = meal_list[meal_list['Optimal Value']!=0]
  squad_meals = meals_dataset.Meal.loc[np.array(squad.Variable.str.split('_').tolist())[:,1].astype(int)]
  squad_type = meals_dataset.Type.loc[np.array(squad.Variable.str.split('_').tolist())[:,1].astype(int)]
  return pd.DataFrame([squad_meals,squad_type]).T

然后，我运行以下命令

find_best_plan_80 = find_best_plan(80)

这会导致错误

KeyError                                  Traceback (most recent call last)
<ipython-input-38-43f957c4ddba> in <cell line: 1>()
----> 1 find_best_plan_80 = find_best_plan(80)

2 frames
<ipython-input-36-07827a9fc028> in <listcomp>(.0)
     10     boundary_condition = np.arange(type_data.index.min(),type_data.index.max())
     11     const = constraints[mealstypes[i]]
---> 12     prob += pulp.lpSum( [ x[j] for j in boundary_condition ] )==const
     13   prob += pulp.lpSum( [ x[i]*proteins[i] for i in range(len(x))])<= minprotein
     14   return prob

KeyError: 0

我被困在这里，无法弄清楚我的问题是什么。作为参考，这是我使用的数据库：

                        Meal  Calories   Protein             Type
0   weetabix_milk_applejuice   298.125   6.47025        breakfast
1           eggs_bacon_toast   635.500  32.41750        breakfast
2     proteinyog_raspberries   238.600  43.71750        breakfast
3             pancakes_syrup   418.500   9.61200        breakfast
4       turkeybacon_omelette   374.000  26.75750        breakfast
5      chicken_rice_broccoli   519.200  51.88300  lunch or dinner
6             prawn_stir_fry   559.200  37.99650  lunch or dinner
7      steak_fries_asparagus   867.100  67.16000  lunch or dinner
8       chicken_caesar_salad   242.900  35.78700  lunch or dinner
9            chicken_fajitas   804.560  58.12480  lunch or dinner
10     beefpie_mash_broccoli   736.200  23.33600  lunch or dinner
11                   tuna_jp   425.600  32.28300  lunch or dinner
12                beef_pasta   652.250  55.38000  lunch or dinner
13                       blt   513.000  15.46350  lunch or dinner
14          chicken_sandwich   397.200  29.76750  lunch or dinner

任何帮助将不胜感激。

Python 线性编程纸浆运筹学

答：

1赞 AirSquid 6/27/2023 #1

您在索引变量时犯了一些基本错误。如果你看一下这个错误，它告诉你它不存在（关键错误）......x[0]

您正在定义 x 以 {tuna_jp， blt， ...} 等 Meals 集进行索引，但随后您开始使用 from 和的整数进行索引。这些都行不通。选择名称或整数。boundary_conditionrange(len(x))

我强烈建议在开始时减轻大量混乱，即放弃并只使用基本的 python 字典。它更干净，您将能够专注于建模。然后，如果问题变得更大，或者您有一个大型数据文件，那么也许.pandasnumpypandas

1赞 Reinderien 6/27/2023 #2

Pandas 实际上是一种非常合理的 Pulp 做事方式——恕我直言，它比裸露的 Pulp 更好——但它需要正确完成，而且它没有出现在原始代码中。

from io import StringIO

import pandas as pd
import pulp

dstr = '''          Meal,Calories, Protein,           Type
weetabix_milk_applejuice, 298.125, 6.47025,      breakfast
        eggs_bacon_toast, 635.500,32.41750,      breakfast
  proteinyog_raspberries, 238.600,43.71750,      breakfast
          pancakes_syrup, 418.500, 9.61200,      breakfast
    turkeybacon_omelette, 374.000,26.75750,      breakfast
   chicken_rice_broccoli, 519.200,51.88300,lunch or dinner
          prawn_stir_fry, 559.200,37.99650,lunch or dinner
   steak_fries_asparagus, 867.100,67.16000,lunch or dinner
    chicken_caesar_salad, 242.900,35.78700,lunch or dinner
         chicken_fajitas, 804.560,58.12480,lunch or dinner
   beefpie_mash_broccoli, 736.200,23.33600,lunch or dinner
                 tuna_jp, 425.600,32.28300,lunch or dinner
              beef_pasta, 652.250,55.38000,lunch or dinner
                     blt, 513.000,15.46350,lunch or dinner
        chicken_sandwich, 397.200,29.76750,lunch or dinner
'''

with StringIO(dstr) as f:
    df = pd.read_csv(f, skipinitialspace=True, index_col='Meal')


def make_select(row: pd.Series) -> pulp.LpVariable:
    return pulp.LpVariable(name='assign_' + row.name, cat=pulp.LpBinary)


df['Select'] = df.apply(make_select, axis=1)
prob = pulp.LpProblem(name='meal_plan', sense=pulp.LpMinimize)
prob.objective = df.Calories.dot(df.Select)

prob.addConstraint(
    name='min_protein',
    constraint=df.Protein.dot(df.Select) >= 80,
)
prob.addConstraint(
    name='breakfasts',
    constraint=df[df.Type == 'breakfast'].Select.sum() == 1,
)
prob.addConstraint(
    name='dinners',
    constraint=df[df.Type == 'lunch or dinner'].Select.sum() == 2,
)
print(prob)

prob.solve()
assert prob.status == pulp.LpStatusOptimal
df['Select'] = df.Select.apply(pulp.LpVariable.value)
print(df)

meal_plan:
MINIMIZE
652.25*assign_beef_pasta + 736.2*assign_beefpie_mash_broccoli + 513.0*assign_blt + 242.9*assign_chicken_caesar_salad + 804.56*assign_chicken_fajitas + 519.2*assign_chicken_rice_broccoli + 397.2*assign_chicken_sandwich + 635.5*assign_eggs_bacon_toast + 418.5*assign_pancakes_syrup + 559.2*assign_prawn_stir_fry + 238.6*assign_proteinyog_raspberries + 867.1*assign_steak_fries_asparagus + 425.6*assign_tuna_jp + 374.0*assign_turkeybacon_omelette + 298.125*assign_weetabix_milk_applejuice + 0.0
SUBJECT TO
min_protein: 55.38 assign_beef_pasta + 23.336 assign_beefpie_mash_broccoli
 + 15.4635 assign_blt + 35.787 assign_chicken_caesar_salad
 + 58.1248 assign_chicken_fajitas + 51.883 assign_chicken_rice_broccoli
 + 29.7675 assign_chicken_sandwich + 32.4175 assign_eggs_bacon_toast
 + 9.612 assign_pancakes_syrup + 37.9965 assign_prawn_stir_fry
 + 43.7175 assign_proteinyog_raspberries + 67.16 assign_steak_fries_asparagus
 + 32.283 assign_tuna_jp + 26.7575 assign_turkeybacon_omelette
 + 6.47025 assign_weetabix_milk_applejuice >= 80
breakfasts: assign_eggs_bacon_toast + assign_pancakes_syrup
 + assign_proteinyog_raspberries + assign_turkeybacon_omelette
 + assign_weetabix_milk_applejuice = 1
dinners: assign_beef_pasta + assign_beefpie_mash_broccoli + assign_blt
 + assign_chicken_caesar_salad + assign_chicken_fajitas
 + assign_chicken_rice_broccoli + assign_chicken_sandwich
 + assign_prawn_stir_fry + assign_steak_fries_asparagus + assign_tuna_jp = 2
VARIABLES
0 <= assign_beef_pasta <= 1 Integer
0 <= assign_beefpie_mash_broccoli <= 1 Integer
0 <= assign_blt <= 1 Integer
0 <= assign_chicken_caesar_salad <= 1 Integer
0 <= assign_chicken_fajitas <= 1 Integer
0 <= assign_chicken_rice_broccoli <= 1 Integer
0 <= assign_chicken_sandwich <= 1 Integer
0 <= assign_eggs_bacon_toast <= 1 Integer
0 <= assign_pancakes_syrup <= 1 Integer
0 <= assign_prawn_stir_fry <= 1 Integer
0 <= assign_proteinyog_raspberries <= 1 Integer
0 <= assign_steak_fries_asparagus <= 1 Integer
0 <= assign_tuna_jp <= 1 Integer
0 <= assign_turkeybacon_omelette <= 1 Integer
0 <= assign_weetabix_milk_applejuice <= 1 Integer
Welcome to the CBC MILP Solver 
Version: 2.10.3 
Build Date: Dec 15 2019 

Result - Optimal solution found
Objective value:                878.70000000
Enumerated nodes:               0
Total iterations:               0
Time (CPU seconds):             0.01
Time (Wallclock seconds):       0.01
Option for printingOptions changed from normal to all
Total time (CPU seconds):       0.01   (Wallclock seconds):       0.01

                          Calories   Protein             Type  Select
Meal                                                                 
weetabix_milk_applejuice   298.125   6.47025        breakfast     0.0
eggs_bacon_toast           635.500  32.41750        breakfast     0.0
proteinyog_raspberries     238.600  43.71750        breakfast     1.0
pancakes_syrup             418.500   9.61200        breakfast     0.0
turkeybacon_omelette       374.000  26.75750        breakfast     0.0
chicken_rice_broccoli      519.200  51.88300  lunch or dinner     0.0
prawn_stir_fry             559.200  37.99650  lunch or dinner     0.0
steak_fries_asparagus      867.100  67.16000  lunch or dinner     0.0
chicken_caesar_salad       242.900  35.78700  lunch or dinner     1.0
chicken_fajitas            804.560  58.12480  lunch or dinner     0.0
beefpie_mash_broccoli      736.200  23.33600  lunch or dinner     0.0
tuna_jp                    425.600  32.28300  lunch or dinner     0.0
beef_pasta                 652.250  55.38000  lunch or dinner     0.0
blt                        513.000  15.46350  lunch or dinner     0.0
chicken_sandwich           397.200  29.76750  lunch or dinner     1.0

1赞 AirSquid 6/27/2023

我喜欢反驳意见，尤其是当有漂亮的代码备份时。我只为光滑的 StringIO => 数据帧实现投了赞成票。在那里学到了一些东西！;)

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KeyError：0 尝试运行 LPP 时

KeyError: 0 When attempting to run my LPP

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