I am trying to maximize revenue using costs for several products.
Here is my code:
In this piece of code, I calculate revenue for each product and then sum it.
list = [65,78,90] #list of products
def calcRev(x,list):
sum = 0
x = pd.DataFrame(x)
for i in list:
count = 0
df_temp = df[df.app_id == i].reset_index()
df_temp['cost'] = x[count]
df_temp['inst'] = df_temp['cost']/df_temp['cpi']
df_temp['rev'] = (df_temp['LTV_accum']* df_temp['inst'])-df_temp['cost']
count = count+1
sum = sum+df_temp.rev.sum()
return sum
objective function:
def obj(x,list):
return -calcRev(x,list)
constraints:
def constrglobal(x,list):
return 10000-calcRev(x,list)
cons = ({'type':'ineq','fun':constrglobal,'args':(list,)})
then I initialize costs as initial guess x. Since I have two products as input x has two dims. x = np.full((15,), 0)
then I apply minimize function:
sol = minimize(obj,x,args=(list,),method='SLSQP',constraints=cons,options={'disp':True})
But as solution I have data distributed only for first product:
sol.x
array([1467.64152006, 1442.41330009, 1417.18518617, 1391.95714139,
1366.1311581 , 0. , 0. , 0. ,
0. , 0. ])
Can you please suggest what is wrong and how I can have sol.x distributed among all products?