def findSolutionValues(self, lp):
prob = lp.solverModel
if self.mip and self.hasMIPConstraints(lp.solverModel):
solutionStatus = glpk.glp_mip_status(prob)
else:
solutionStatus = glpk.glp_get_status(prob)
glpkLpStatus = {
glpk.GLP_OPT: LpStatusOptimal,
glpk.GLP_UNDEF: LpStatusUndefined,
glpk.GLP_FEAS: LpStatusNotSolved,
glpk.GLP_INFEAS: LpStatusInfeasible,
glpk.GLP_NOFEAS: LpStatusInfeasible,
glpk.GLP_UNBND: LpStatusUnbounded
}
#populate pulp solution values
for var in lp.variables():
if self.mip and self.hasMIPConstraints(lp.solverModel):
var.varValue = glpk.glp_mip_col_val(prob, var.glpk_index)
else:
var.varValue = glpk.glp_get_col_prim(prob, var.glpk_index)
var.dj = glpk.glp_get_col_dual(prob, var.glpk_index)
#put pi and slack variables against the constraints
for constr in lp.constraints.values():
if self.mip and self.hasMIPConstraints(lp.solverModel):
row_val = glpk.glp_mip_row_val(prob, constr.glpk_index)
else:
row_val = glpk.glp_get_row_prim(prob, constr.glpk_index)
constr.slack = -constr.constant - row_val
constr.pi = glpk.glp_get_row_dual(prob, constr.glpk_index)
lp.resolveOK = True
for var in lp.variables():
var.isModified = False
lp.status = glpkLpStatus.get(solutionStatus, LpStatusUndefined)
return lp.status
def evaluate_problem(self, data, problem):
responses = data["response"]
metrics = data["metric"]
counter = 0
obj_val = glpki.glp_mip_obj_val(problem)
for r in responses:
if (1.0 == (glpki.glp_mip_col_val(problem, r.column))):
counter = counter + 1
return (counter, obj_val)