Unbound Local Error
Answered
Hello, I am trying to code an algorithm using defined functions in python and then add a lazy constraint, I took the work done in tsp.py as an example and guide. However, I get the following error:
UnboundLocalError: local variable 'vis' referenced before assignment
Exception ignored in: 'gurobipy.gurobipy.callbackstub' Traceback (most recent call last): File "src\gurobipy\callback.pxi", line 180, in gurobipy.gurobipy.CallbackClass.callback File "<ipython-input-5-e0036d7dd796>", line 31, in cut File "<ipython-input-5-e0036d7dd796>", line 19, in inner UnboundLocalError: local variable 'vis' referenced before assignment
This is the code I wrote:
met = ['A','B','C','D','E']
reac = ['v1','v2','v3','v4','v5','v6','v7']
reactions = [i for i in range(len(reac))]
metabolites = [ i for i in range(len(met))]
LB =[0,-10,0,0,-10,-10,-10]
UB =[10,10,10,10,10,10,10]
S = {(0,0):-1,(0,1):0,(0,2):1,(0,3):0,(0,4):0,(0,5):0,(0,6):0,
(1,0):1,(1,1):-1,(1,2):0,(1,3):-1,(1,4):0,(1,5):0,(1,6):0,
(2,0):0,(2,1):1,(2,2):0,(2,3):0,(2,4):1,(2,5):0,(2,6):0,
(3,0):-1,(3,1):0,(3,2):0,(3,3):0,(3,4):0,(3,5):1,(3,6):0,
(4,0):1,(4,1):0,(4,2):0,(4,3):0,(4,4):0,(4,5):0,(4,6):1}
v4 = reac.index('v4')
v1 = reac.index('v1')
def inner(yoj): # from this model I want the list of solutions
i = gp.Model()
vi = i.addVars(range(len(reac)),lb=-GRB.INFINITY, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name='vi')
i.setObjective(2000*vi[v4], GRB.MAXIMIZE)
i.addConstrs((gp.quicksum(S[i,j] * vi[j] for j in reactions) == 0 for i in metabolites), name='IFFBA')
i.addConstrs((LB[j]*yoj[j] <= vi[j] for j in reactions), name='iLB')
i.addConstrs((vi[j] <= UB[j]*yoj[j] for j in reactions), name='iUB')
if i.status == GRB.OPTIMAL:
soi = i.getAttr('X',i.getVars())
vis = soi.copy()
return vis
def cut(model,where): #this will add the lazy constraint
if (where == GRB.Callback.MIPSOL):
voj = model.cbGetSolution(model._vars) #solutions
yoj = model.cbGetSolution(model._varsy)# retrieve the yj's solution from m.model
keys = model._vars.keys() #keys
vjs = model._vars #variables master's vj
yjs = model._varsy #variables master's yj
eps = .00001
vij = inner(yoj) #calls the first function to get the solution list
if abs(vij[v4]-voj[v4]) >= eps:
model.cbLazy(vij[v4] <= vjs[v4] + 2000*(gp.quicksum((1-yjs[j])*abs(vij[j]) for j in keys)))
else:
print('Outer Growth Rate','-->',voj[v4])
print('Inner Growth Rate','-->',vij[v4])
m = gp.Model()
v = m.addVars(range(len(reac)), lb=-GRB.INFINITY, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name= 'v')
y = m.addVars(range(len(reac)), vtype=GRB.BINARY, name= 'y')
m.setObjective(1*v[v1], GRB.MAXIMIZE)
m.addConstrs((gp.quicksum(S[i,j] * v[j] for j in reactions) == 0 for i in metabolites), name='OFFBA')
m.addConstrs((LB[j]*y[j] <= v[j] for j in reactions), name='oLBy')
m.addConstrs((v[j] <= UB[j]*y[j] for j in reactions), name='oUBy')
m.addConstr(sum(1-y[j] for j in reactions) == k, name='Knapsack')
m._vars = v
m._varsy = y
m.Params.lazyConstraints = 1
m.optimize(cut)
if m.status == GRB.OPTIMAL:
sol = m.getAttr('X',m.getVars())
sol1= sol.copy()
The idea is to solve a master problem and get \(v_j\) and \(y_j\), then use the \(y_j\) in the inner problem defined in the function (inner) then get the \(vi_j\) to evaluate a value against a value from the master problem (criteria) to add the lazy constraint. That's why I defined a function called inner takes the \(ys\) list as an argument and returns values called \(vi\).
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I tried to declare vis as global but still can't get it to work, any ideas?
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It looks like you are missing a call to i.optimize(). As a result, the model's status is not \( \texttt{GRB.OPTIMAL} \), so \( \texttt{vis} \) is never assigned a value.
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Such a rookie mistake! I can't believe I missed this, thanks for your reply!
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