In [1]:

    

import pandas as pd


    

In [2]:

    

import pulp as opt


    

In [15]:

    

df = pd.read_csv('data_movies_week_9.csv', header = None)


    

In [16]:

    

df


    

    
Out[16]:






  

    

      

      
0
      
1
      
2
      
3
    
  
  

    

      
0
      
1
      
Star Trek
      
6.100000e+07
      
720
    
    

      
1
      
2
      
Ice Age
      
3.300000e+07
      
459
    
    

      
2
      
3
      
Secret Life
      
2.550000e+07
      
334
    
    

      
3
      
4
      
Ghostbusters
      
2.075000e+07
      
285
    
    

      
4
      
5
      
Lights Out
      
1.700000e+07
      
223
    
    

      
5
      
6
      
Hillary's America
      
7.801226e+06
      
100
    
    

      
6
      
7
      
The Legend Of
      
6.300000e+06
      
78
    
    

      
7
      
8
      
Finding Dory
      
6.200000e+06
      
73
    
    

      
8
      
9
      
Mike and Dave
      
3.450000e+06
      
51
    
    

      
9
      
10
      
The Infiltrator
      
3.276515e+06
      
42
    
    

      
10
      
11
      
Central Intellience
      
3.225000e+06
      
41
    
    

      
11
      
12
      
The Purge
      
3.100000e+06
      
36
    
    

      
12
      
13
      
The BFG
      
1.794282e+06
      
23
    
    

      
13
      
14
      
The Shallows
      
1.716270e+06
      
22
    
    

      
14
      
15
      
AbsolutelyFa
      
1.404221e+06
      
18
    
    

      
15
      
16
      
Empty
      
-2.000000e+06
      
0
    
  

In [17]:

    

x1 = opt.LpVariable('x1',0,8, cat = 'Integer')
x2 = opt.LpVariable('x2',0,8, cat = 'Integer')
x3 = opt.LpVariable('x3',0,8, cat = 'Integer')
x4 = opt.LpVariable('x4',0,8, cat = 'Integer')
x5 = opt.LpVariable('x5',0,8, cat = 'Integer')
x6 = opt.LpVariable('x6',0,8, cat = 'Integer')
x7 = opt.LpVariable('x7',0,8, cat = 'Integer')
x8 = opt.LpVariable('x8',0,8, cat = 'Integer')
x9 = opt.LpVariable('x9',0,8, cat = 'Integer')
x10 = opt.LpVariable('x10',0,8, cat = 'Integer')
x11 = opt.LpVariable('x11',0,8, cat = 'Integer')
x12 = opt.LpVariable('x12',0,8, cat = 'Integer')
x13 = opt.LpVariable('x13',0,8, cat = 'Integer')
x14 = opt.LpVariable('x14',0,8, cat = 'Integer')
x15 = opt.LpVariable('x15',0,8, cat = 'Integer')
x16 = opt.LpVariable('x16',0,8, cat = 'Integer')


    

In [18]:

    

prob = opt.LpProblem('Optimize_FML_Success',opt.LpMaximize)


    

In [19]:

    

prob += df.ix[0,2]*x1 + df.ix[1,2]*x2 + df.ix[2,2]*x3 + df.ix[3,2]*x4 + df.ix[4,2]*x5 + df.ix[5,2]*x6 + df.ix[6,2]*x7 + df.ix[7,2]*x8 + df.ix[8,2]*x9 + df.ix[9,2]*x10 + df.ix[10,2]*x11 + df.ix[11,2]*x12 + df.ix[12,2]*x13 + df.ix[13,2]*x14 + df.ix[14,2]*x15 + df.ix[15,2]*x16


    

In [20]:

    

prob


    

    
Out[20]:





Optimize_FML_Success:
MAXIMIZE
61000000.0*x1 + 3276514.912*x10 + 3225000.0*x11 + 3100000.0*x12 + 1794281.975*x13 + 1716269.716*x14 + 1404220.676*x15 + -2000000.0*x16 + 33000000.0*x2 + 25500000.0*x3 + 20750000.0*x4 + 17000000.0*x5 + 7801225.98*x6 + 6300000.0*x7 + 6200000.0*x8 + 3450000.0*x9 + 0.0
VARIABLES
0 <= x1 <= 8 Integer
0 <= x10 <= 8 Integer
0 <= x11 <= 8 Integer
0 <= x12 <= 8 Integer
0 <= x13 <= 8 Integer
0 <= x14 <= 8 Integer
0 <= x15 <= 8 Integer
0 <= x16 <= 8 Integer
0 <= x2 <= 8 Integer
0 <= x3 <= 8 Integer
0 <= x4 <= 8 Integer
0 <= x5 <= 8 Integer
0 <= x6 <= 8 Integer
0 <= x7 <= 8 Integer
0 <= x8 <= 8 Integer
0 <= x9 <= 8 Integer

In [21]:

    

prob += x1+x2+x3+x4+x5+x6+x7+x8+x9+x10+x11+x12+x13+x14+x15+x16 <= 8


    

In [22]:

    

prob += x1+x2+x3+x4+x5+x6+x7+x8+x9+x10+x11+x12+x13+x14+x15+x16 >= 8


    

In [23]:

    

prob += df.ix[0,3]*x1 + df.ix[1,3]*x2 + df.ix[2,3]*x3 + df.ix[3,3]*x4 + df.ix[4,3]*x5 + df.ix[5,3]*x6 + df.ix[6,3]*x7 + df.ix[7,3]*x8 + df.ix[8,3]*x9 + df.ix[9,3]*x10 + df.ix[10,3]*x11 + df.ix[11,3]*x12 + df.ix[12,3]*x13 + df.ix[13,3]*x14 + df.ix[14,3]*x15 + df.ix[15,3]*x16 <= 1000


    

In [24]:

    

opt.pulpTestAll()


    

    




	 Testing zero subtraction
	 Testing inconsistant lp solution
	 Testing continuous LP solution
	 Testing maximize continuous LP solution
	 Testing unbounded continuous LP solution
	 Testing Long Names
	 Testing repeated Names
	 Testing zero constraint
	 Testing zero objective
	 Testing LpVariable (not LpAffineExpression) objective
	 Testing Long lines in LP
	 Testing LpAffineExpression divide
	 Testing MIP solution
	 Testing MIP solution with floats in objective
	 Testing MIP relaxation
	 Testing feasibility problem (no objective)
	 Testing an infeasible problem
	 Testing an integer infeasible problem
	 Testing column based modelling
	 Testing dual variables and slacks reporting
	 Testing fractional constraints
	 Testing elastic constraints (no change)
	 Testing elastic constraints (freebound)
	 Testing elastic constraints (penalty unchanged)
	 Testing elastic constraints (penalty unbounded)
* Solver <class 'pulp.solvers.PULP_CBC_CMD'> passed.
Solver <class 'pulp.solvers.CPLEX_DLL'> unavailable
Solver <class 'pulp.solvers.CPLEX_CMD'> unavailable
Solver <class 'pulp.solvers.CPLEX_PY'> unavailable
Solver <class 'pulp.solvers.COIN_CMD'> unavailable
Solver <class 'pulp.solvers.COINMP_DLL'> unavailable
Solver <class 'pulp.solvers.GLPK_CMD'> unavailable
Solver <class 'pulp.solvers.XPRESS'> unavailable
Solver <class 'pulp.solvers.GUROBI'> unavailable
Solver <class 'pulp.solvers.GUROBI_CMD'> unavailable
Solver <class 'pulp.solvers.PYGLPK'> unavailable
Solver <class 'pulp.solvers.YAPOSIB'> unavailable

In [25]:

    

status = prob.solve(opt.PULP_CBC_CMD(msg=0))


    

In [26]:

    

opt.LpStatus[status]


    

    
Out[26]:





'Optimal'

In [27]:

    

for v in prob.variables():
    print (v.name, "=" , v.varValue)


    

    




x1 = 1.0
x10 = 0.0
x11 = 1.0
x12 = 4.0
x13 = 0.0
x14 = 1.0
x15 = 0.0
x16 = 0.0
x2 = 0.0
x3 = 0.0
x4 = 0.0
x5 = 0.0
x6 = 0.0
x7 = 0.0
x8 = 1.0
x9 = 0.0

In [28]:

    

opt.value(prob.objective)


    

    
Out[28]:





84541269.716

In [ ]: