Python - Pyomo - R - Reticulate - [WinError 6] 句柄无效

如何解决Python - Pyomo - R - Reticulate - [WinError 6] 句柄无效

“reticulate”包提供了从 R 调用 Python 的可能性。我尝试使用基于求解器“glpk”的 reticulate 和 pyomo 通过 R 运行以下代码。如果我独立于 R 执行代码，则 Python 代码正在工作。在 R 中使用包“reticulate”执行代码不起作用。我也在 stackoverflow (pyomo + reticulate error 6 the handle is invalid) 中发现了一个类似的问题，但无论是 setwd、以权限运行还是 setting.DEFINE_SIGNAL_HANDLERS_DEFAULT = False 都没有解决“错误 6 句柄无效”的问题。 预先感谢您的帮助！

溴， 亚历克斯

基于 Python 的示例来自：https://nbviewer.jupyter.org/github/Pyomo/PyomoGallery/blob/master/transport/transport.ipynb

Python 代码

from pyomo.environ import *
import pyutilib.subprocess.GlobalData
pyutilib.subprocess.GlobalData.DEFINE_SIGNAL_HANDLERS_DEFAULT = False
 
model = ConcreteModel()
 
model.i = Set(initialize=['seattle','san-diego'],doc='Canning plans')
model.j = Set(initialize=['new-york','chicago','topeka'],doc='Markets')
 
model.a = Param(model.i,initialize={'seattle':350,'san-diego':600},doc='Capacity of plant i in cases')
model.b = Param(model.j,initialize={'new-york':325,'chicago':300,'topeka':275},doc='Demand at market j in cases')

dtab = {
    ('seattle','new-york') : 2.5,('seattle','chicago')  : 1.7,'topeka')   : 1.8,('san-diego','chicago')  : 1.8,'topeka')   : 1.4,}
model.d = Param(model.i,model.j,initialize=dtab,doc='Distance in thousands of miles')

model.f = Param(initialize=90,doc='Freight in dollars per case per thousand miles')

def c_init(model,i,j):
  return model.f * model.d[i,j] / 1000
model.c = Param(model.i,initialize=c_init,doc='Transport cost in thousands of dollar per case')
 
model.x = Var(model.i,bounds=(0.0,None),doc='Shipment quantities in case')
 
def supply_rule(model,i):
  return sum(model.x[i,j] for j in model.j) <= model.a[i]
model.supply = Constraint(model.i,rule=supply_rule,doc='Observe supply limit at plant i')

def demand_rule(model,j):
  return sum(model.x[i,j] for i in model.i) >= model.b[j]  
model.demand = Constraint(model.j,rule=demand_rule,doc='Satisfy demand at market j')
 
def objective_rule(model):
  return sum(model.c[i,j]*model.x[i,j] for i in model.i for j in model.j)
model.objective = Objective(rule=objective_rule,sense=minimize,doc='Define objective function')
 
def pyomo_postprocess(options=None,instance=None,results=None):
  model.x.display()
 
if __name__ == '__main__':
    # This emulates what the pyomo command-line tools does
    from pyomo.opt import SolverFactory
    import pyomo.environ
    opt = SolverFactory("glpk")
    results = opt.solve(model)
    #sends results to stdout
    results.write()
    print("\nDisplaying Solution\n" + '-'*60)
    pyomo_postprocess(None,model,results)

R 中的代码：

library("reticulate")
use_python("C:/Anaconda",required = TRUE)

setwd("C:/Users/mea39219/Documents/R/Test")

a <- py_run_file("transportproblem.py",local=T)
a$results

R 输出：

> library("reticulate")
> use_python("C:/Anaconda",required = TRUE)
> reticulate::py_config()
python:         C:/Anaconda/python.exe
libpython:      C:/Anaconda/python38.dll
pythonhome:     C:/Anaconda
version:        3.8.8 (default,Apr 13 2021,15:08:03) [MSC v.1916 64 bit (AMD64)]
Architecture:   64bit
numpy:          C:/Anaconda/Lib/site-packages/numpy
numpy_version:  1.20.1

NOTE: Python version was forced by use_python function
> 
> setwd("C:/Users/mea39219/Documents/R/Test")
> 
> a <- py_run_file("transportproblem.py",local=T)
Error in py_run_file_impl(file,local,convert) : 
  RuntimeError: Attempting to use an unavailable solver.

The SolverFactory was unable to create the solver "glpk"
and returned an UnknownSolver object.  This error is raised at the point
where the UnknownSolver object was used as if it were valid (by calling
method "solve").

The original solver was created with the following parameters:
    type: glpk
    _args: ()
    options: {}
WARNING: Failed to create solver with name 'glpk': Could not execute the
    command: 'C:\Anaconda\Library\bin\glpsol.exe --version'
        Error message: [WinError 6] Das Handle ist ungültig
> a$results
Error: object 'a' not found

版本：glpk (5.0)、pyomo (5.7.2)、网纹 (1.12)