Eigenvalue
Reputation: 1133
getting empty model pyomo
So for the life of me, I can't get my pyomo program to run.
I have my python file :
from pyomo.environ import *
#pyomo solve --solver=glpk diet.py diet.dat
model = AbstractModel()
# Foodss
model.m = Param(within=NonNegativeIntegers)
model.n = Param(within=NonNegativeIntegers)
model.warehouses = RangeSet(1, model.m)
model.stores = RangeSet(1, model.n)
model.cost = Param(model.warehouses,model.stores)
model.a = Param(model.warehouses)
model.b = Param(model.warehouses)
model.d = Param(model.stores)
model.amounts = Var(model.warehouses, model.stores, within = NonNegativeIntegers)
model.pprint()
# Minimize the cost of food that is consumed
def cost_rule(model):
return sum(
model.cost[n,i] * model.amounts[n,i]
for n in model.warehouses
for i in model.stores
)
model.cost = Objective(rule=cost_rule)
def minDemandRule(store, model):
return sum(model.a[i]*model.amounts[i, store] for i in model.warehouses) >= model.d[store]
model.demandConstraint = Constraint(model.stores, rule=minDemandRule)
# Limit the volume of food consumed
def maxSupplyRule(warehouse,model):
return sum(model.amounts[warehouses,j] for j in model.stores) <= self.b[warehouse]
model.supplyConstraint = Constraint(model.warehouses, rule=maxSupplyRule)
plus .dat file :
set warehouses := warehouseone warehousetwo warehousethree warehousefour;
set stores := storeone storetwo storethree storefour storefive storesix;
param cost:
storeone storetwo storethree storefour storefive storesix :=
warehouseone 23 12 34 25 27 16
warehousetwo 29 24 43 35 28 19
warehousethree 43 31 52 36 30 21
warehousefour 54 36 54 46 34 27;
param m := 4 ;
param n := 6 ;
param: a:=
warehouseone 15
warehousetwo 25
warehousethree 40
warehousefour 70;
param: b :=
warehouseone 10
warehousetwo 5
warehousethree 7
warehousefour 4;
param: d :=
storeone 45
storetwo 120
storethree 165
storefour 214
storefive 64
storesix 93;
From my understanding of how this works, you sort of map things from one to another. This seems okay to me but when I run it.
pyomo solve --solver=glpk transport.py data.dat
[ 0.00] Setting up Pyomo environment
[ 0.00] Applying Pyomo preprocessing actions
2 Set Declarations
amounts_index : Dim=0, Dimen=2, Size=0, Domain=None, Ordered=True, Bounds=None
Virtual
cost_index : Dim=0, Dimen=2, Size=0, Domain=None, Ordered=True, Bounds=None
Virtual
2 RangeSet Declarations
stores : Dim=0, Dimen=1, Size=0, Domain=None, Ordered=True, Bounds=None
Not constructed
warehouses : Dim=0, Dimen=1, Size=0, Domain=None, Ordered=True, Bounds=None
Not constructed
6 Param Declarations
a : Size=0, Index=warehouses, Domain=Any, Default=None, Mutable=False
Not constructed
b : Size=0, Index=warehouses, Domain=Any, Default=None, Mutable=False
Not constructed
cost : Size=0, Index=cost_index, Domain=Any, Default=None, Mutable=False
Not constructed
d : Size=0, Index=stores, Domain=Any, Default=None, Mutable=False
Not constructed
m : Size=1, Index=None, Domain=NonNegativeIntegers, Default=None, Mutable=False
Not constructed
n : Size=1, Index=None, Domain=NonNegativeIntegers, Default=None, Mutable=False
Not constructed
1 Var Declarations
amounts : Size=0, Index=amounts_index
Not constructed
11 Declarations: m n warehouses stores cost_index cost a b d amounts_index amounts
WARNING: Implicitly replacing the Component attribute cost (type=<class 'pyomo.core.base.param.IndexedParam'>) on block unknown with a new Component (type=<class 'pyomo.core.base.objective.SimpleObjective'>).
This is usually indicative of a modelling error.
To avoid this warning, use block.del_component() and block.add_component().
[ 0.01] Creating model
ERROR: Constructing component 'a' from data={'warehousefour': 70, 'warehouseone': 15, 'warehousethree': 40, 'warehousetwo': 25} failed:
RuntimeError: Failed to set value for param=a, index=warehousefour, value=70.
source error message="Error setting parameter value: Index 'warehousefour' is not valid for array Param 'a'"
[ 0.02] Pyomo Finished
ERROR: Unexpected exception while running model:
Failed to set value for param=a, index=warehousefour, value=70.
source error message="Error setting parameter value: Index 'warehousefour' is not valid for array Param 'a'"
I feel like the dat file isn't being read properly, but when I look at other examples, this is how it is done, so I'm a bit perplexed.
Upvotes: 1
Views: 866
Answers (1)
Timofey Chernousov
Reputation: 1294
there was several issues in you code from typos to wrong pyomo usage. Below is fixed version. If it is work for you and you have more specific questions, please post new question for it.
File diet.py:
from pyomo.environ import *
#pyomo solve --solver=glpk diet.py diet.dat
model = AbstractModel()
# Foodss
model.warehouses = Set()
model.stores = Set()
model.a = Param(model.warehouses)
model.b = Param(model.warehouses)
model.d = Param(model.stores)
model.cost = Param(model.warehouses, model.stores)
model.amounts = Var(model.warehouses, model.stores, within = NonNegativeIntegers)
model.pprint()
# Minimize the cost of food that is consumed
def cost_rule(model):
return sum(
model.cost[n,i] * model.amounts[n,i]
for n in model.warehouses
for i in model.stores
)
model.costObjective = Objective(rule=cost_rule)
def minDemandRule(model, store):
return sum(model.a[i]*model.amounts[i, store] for i in model.warehouses) >= model.d[store]
model.demandConstraint = Constraint(model.stores, rule=minDemandRule)
# Limit the volume of food consumed
def maxSupplyRule(model, warehouse):
return sum(model.amounts[warehouse,j] for j in model.stores) <= model.b[warehouse]
model.supplyConstraint = Constraint(model.warehouses, rule=maxSupplyRule)
File diet.dat:
param: warehouses:
a b :=
warehouseone 15 10
warehousetwo 25 5
warehousethree 40 7
warehousefour 70 4;
param: stores:
d :=
storeone 45
storetwo 120
storethree 165
storefour 214
storefive 64
storesix 93;
param cost:
storeone storetwo storethree storefour storefive storesix :=
warehouseone 23 12 34 25 27 16
warehousetwo 29 24 43 35 28 19
warehousethree 43 31 52 36 30 21
warehousefour 54 36 54 46 34 27;
Example run (note I am using CLP solver here, but is not principal):
$ pyomo solve --solver=clp test.py test.dat
[ 0.00] Setting up Pyomo environment
[ 0.00] Applying Pyomo preprocessing actions
4 Set Declarations
amounts_index : Dim=0, Dimen=2, Size=0, Domain=None, Ordered=False, Bounds=None
Virtual
cost_index : Dim=0, Dimen=2, Size=0, Domain=None, Ordered=False, Bounds=None
Virtual
stores : Dim=0, Dimen=1, Size=0, Domain=None, Ordered=False, Bounds=None
Not constructed
warehouses : Dim=0, Dimen=1, Size=0, Domain=None, Ordered=False, Bounds=None
Not constructed
4 Param Declarations
a : Size=0, Index=warehouses, Domain=Any, Default=None, Mutable=False
Not constructed
b : Size=0, Index=warehouses, Domain=Any, Default=None, Mutable=False
Not constructed
cost : Size=0, Index=cost_index, Domain=Any, Default=None, Mutable=False
Not constructed
d : Size=0, Index=stores, Domain=Any, Default=None, Mutable=False
Not constructed
1 Var Declarations
amounts : Size=0, Index=amounts_index
Not constructed
9 Declarations: warehouses stores a b d cost_index cost amounts_index amounts
[ 0.00] Creating model
[ 0.02] Applying solver
[ 0.03] Processing results
Number of solutions: 1
Solution Information
Gap: None
Status: optimal
Function Value: 532.113571429
Solver results file: results.json
[ 0.04] Applying Pyomo postprocessing actions
[ 0.04] Pyomo Finished
$
Upvotes: 1
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