Optional input data
For the problem formulation
import pyomo.environ as pe
model = pe.AbstractModel()
model.I = pe.Set()
model.p = model.Param(model.I)
model.create_instance("input.dat")
and the input.dat
set I := 1 2 3 ;
param p :=
1 0.1
2 0.2
3 0.3
;
param q :=
1 1.1
2 2.2
3 3.3
;
The following error is shown
AttributeError: 'AbstractModel' object has no attribute 'q'
How to silence create_instance in this case? The model is fully specified. The "excess" data (parameter q in this case) is needed for another model and the models share this input.dat. I could go with a try/except for the AttributeError and just carry on I guess, but then I would need to guard each create_instance call. I looked for a "skip_undefined" kwarg or similar in the documentation. Is there another preferred way to handle this situation?
pyomo
asked Nov 21 '18 at 17:57
phaebzphaebz
90110
add a comment |
For the problem formulation
import pyomo.environ as pe
model = pe.AbstractModel()
model.I = pe.Set()
model.p = model.Param(model.I)
model.create_instance("input.dat")
and the input.dat
set I := 1 2 3 ;
param p :=
1 0.1
2 0.2
3 0.3
;
param q :=
1 1.1
2 2.2
3 3.3
;
The following error is shown
AttributeError: 'AbstractModel' object has no attribute 'q'
How to silence create_instance in this case? The model is fully specified. The "excess" data (parameter q in this case) is needed for another model and the models share this input.dat. I could go with a try/except for the AttributeError and just carry on I guess, but then I would need to guard each create_instance call. I looked for a "skip_undefined" kwarg or similar in the documentation. Is there another preferred way to handle this situation?
pyomo
asked Nov 21 '18 at 17:57
phaebzphaebz
90110
add a comment |
For the problem formulation
import pyomo.environ as pe
model = pe.AbstractModel()
model.I = pe.Set()
model.p = model.Param(model.I)
model.create_instance("input.dat")
and the input.dat
set I := 1 2 3 ;
param p :=
1 0.1
2 0.2
3 0.3
;
param q :=
1 1.1
2 2.2
3 3.3
;
The following error is shown
AttributeError: 'AbstractModel' object has no attribute 'q'
How to silence create_instance in this case? The model is fully specified. The "excess" data (parameter q in this case) is needed for another model and the models share this input.dat. I could go with a try/except for the AttributeError and just carry on I guess, but then I would need to guard each create_instance call. I looked for a "skip_undefined" kwarg or similar in the documentation. Is there another preferred way to handle this situation?
pyomo
asked Nov 21 '18 at 17:57
phaebzphaebz
90110
For the problem formulation
import pyomo.environ as pe
model = pe.AbstractModel()
model.I = pe.Set()
model.p = model.Param(model.I)
model.create_instance("input.dat")
and the input.dat
set I := 1 2 3 ;
param p :=
1 0.1
2 0.2
3 0.3
;
param q :=
1 1.1
2 2.2
3 3.3
;
The following error is shown
AttributeError: 'AbstractModel' object has no attribute 'q'
How to silence create_instance in this case? The model is fully specified. The "excess" data (parameter q in this case) is needed for another model and the models share this input.dat. I could go with a try/except for the AttributeError and just carry on I guess, but then I would need to guard each create_instance call. I looked for a "skip_undefined" kwarg or similar in the documentation. Is there another preferred way to handle this situation?
pyomo
pyomo
asked Nov 21 '18 at 17:57
phaebzphaebz
90110
asked Nov 21 '18 at 17:57
phaebzphaebz
90110
asked Nov 21 '18 at 17:57
phaebzphaebz
90110
asked Nov 21 '18 at 17:57
phaebzphaebz
90110
asked Nov 21 '18 at 17:57
phaebzphaebz
90110
90110
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
According to the documentation, if you load your data using the method load from the class DataPortal, the parameters not used by the model are omitted.
Therefore you may try:
from pyomo.environ import *
data = DataPortal()
model = AbstractModel()
data.load(filename='./input.dat')
model.I = Set()
model.p = model.Param(model.I)
instance = model.create_instance(data)
answered Nov 21 '18 at 18:34
leoburgyleoburgy
1107
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
According to the documentation, if you load your data using the method load from the class DataPortal, the parameters not used by the model are omitted.
Therefore you may try:
from pyomo.environ import *
data = DataPortal()
model = AbstractModel()
data.load(filename='./input.dat')
model.I = Set()
model.p = model.Param(model.I)
instance = model.create_instance(data)
answered Nov 21 '18 at 18:34
leoburgyleoburgy
1107
add a comment |
According to the documentation, if you load your data using the method load from the class DataPortal, the parameters not used by the model are omitted.
Therefore you may try:
from pyomo.environ import *
data = DataPortal()
model = AbstractModel()
data.load(filename='./input.dat')
model.I = Set()
model.p = model.Param(model.I)
instance = model.create_instance(data)
answered Nov 21 '18 at 18:34
leoburgyleoburgy
1107
add a comment |
According to the documentation, if you load your data using the method load from the class DataPortal, the parameters not used by the model are omitted.
Therefore you may try:
from pyomo.environ import *
data = DataPortal()
model = AbstractModel()
data.load(filename='./input.dat')
model.I = Set()
model.p = model.Param(model.I)
instance = model.create_instance(data)
answered Nov 21 '18 at 18:34
leoburgyleoburgy
1107
According to the documentation, if you load your data using the method load from the class DataPortal, the parameters not used by the model are omitted.
Therefore you may try:
from pyomo.environ import *
data = DataPortal()
model = AbstractModel()
data.load(filename='./input.dat')
model.I = Set()
model.p = model.Param(model.I)
instance = model.create_instance(data)
answered Nov 21 '18 at 18:34
leoburgyleoburgy
1107
answered Nov 21 '18 at 18:34
leoburgyleoburgy
1107
answered Nov 21 '18 at 18:34
leoburgyleoburgy
1107
answered Nov 21 '18 at 18:34
leoburgyleoburgy
1107
1107
add a comment |
add a comment |
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