Check if pyomo model and generated LP file format is valid and catch error/exception - linear-programming

Question

I have a pyomo ConcreteModel() which I solve repeatedly within another stochastic optimization process whereas one or more parameters are changed on the model.
The basic process can be described as follows:
# model is created as a pyomo.ConcreteModel()
for i in range(0, 10):
# change some parameter on the model
opt = SolverFactory('gurobi', solver_io='lp')
# how can I check here if the changed model/lp-file is valid?
results = opt.solve(model)
Now I get an error for some cases where the model and LP file (see gist) seems to contain NaN values:
ERROR: Solver (gurobi) returned non-zero return code (1)
ERROR: Solver log: Academic license - for non-commercial use only Error
reading LP format file /tmp/tmp8agg07az.pyomo.lp at line 1453 Unrecognized
constraint RHS or sense Neighboring tokens: " <= nan c_u_x1371_: +1 x434
<= nan "
Unable to read file Traceback (most recent call last):
File "<stdin>", line 5, in <module> File
"/home/cord/.anaconda3/lib/python3.6/site-
packages/pyomo/solvers/plugins/solvers/GUROBI_RUN.py", line 61, in
gurobi_run
model = read(model_file)
File "gurobi.pxi", line 2652, in gurobipy.read
(../../src/python/gurobipy.c:127968) File "gurobi.pxi", line 72, in
gurobipy.gurobi.read (../../src/python/gurobipy.c:125753)
gurobipy.GurobiError: Unable to read model Freed default Gurobi
environment
Of course, the first idea would be to prevent setting these NaN-values. But I don't know why they occur anyhow and want to figure out when the model breaks due to a wrong structure caused by NaNs.
I know that I can catch the solver status and termination criterion from the SolverFactory() object. But the error obviously occurs somewhere before the solving process due to the invalid changed values.
How can I can catch these kinds of errors for different solvers before solving i. e. check if the model/lp-file is valid before applying a solver? Is there some method e.g. check_model() which delivers True or False if the model is (not) valid or something similar?
Thanks in advance!

Answer 1

If you know that the error is taking place when the parameter values are being changed, then you could test to see whether the sum of all relevant parameter values is a valid number. After all, NaN + 3 = NaN.
Since you are getting NaN, I am going to guess that you are importing parameter values using Pandas from an Excel spreadsheet? There is a way to convert all the NaNs to a default number.
Code example for parameter check:
>>> from pyomo.environ import *
>>> m = ConcreteModel()
>>> m.p1 = Param(initialize=1)
>>> m.p2 = Param(initialize=2)
>>> for p in m.component_data_objects(ctype=Param):
... print(p.name)
...
p1
p2
>>> import numpy
>>> m.p3 = Param(initialize=numpy.nan)
>>> import math
>>> math.isnan(value(sum(m.component_data_objects(ctype=Param))))
True
Indexed, Mutable Parameters:
>>> from pyomo.environ import *
>>> m = ConcreteModel()
>>> m.i = RangeSet(2)
>>> m.p = Param(m.i, initialize={1: 1, 2:2}, mutable=True)
>>> import math
>>> import numpy
>>> math.isnan(value(sum(m.component_data_objects(ctype=Param))))
False
>>> m.p[1] = numpy.nan
>>> math.isnan(value(sum(m.component_data_objects(ctype=Param))))
True