python - Or-Tools CP-SAT 求解器导出/导入 : how to access vars after loading a model?

Question

使用 OR-Tools CP-CAT 求解器的 Python 接口(interface) (reference) ，我希望能够保存 cp_model，稍后或从不同的进程加载它，并继续与之交互。
我能够将模型序列化为 Protubuf，然后加载并解决它:

from google.protobuf import text_format
from ortools.sat.python import cp_model

def create_model():
    model = cp_model.CpModel()
    a = model.NewIntVar(0, 10, "var_a")
    b = model.NewIntVar(0, 10, "var_b")

    model.Maximize(a + b)
    return model
    
def clone_model(model):
    new_model = cp_model.CpModel()
    text_format.Parse(str(model), new_model.Proto())
    
    return new_model

def solve_model(model):
    solver = cp_model.CpSolver()
    status = solver.Solve(new_model)

    print(solver.StatusName(status))
    print(solver.ObjectiveValue())

# Works fine
model = create_model()
new_model = clone_model(model)
solve_model(new_model)

(source)
但是，我想在加载模型后继续与模型交互。例如，我希望能够执行以下操作:

model = create_model()
new_model = clone_model(model)

c = new_model.NewIntVar(0, 5, "var_c")    
new_model.Add(a < c)

问题是最后一行不起作用，因为 a没有定义;而且我找不到任何方法来访问现有模型的变量。
我正在寻找类似的东西: a = new_model.getExistingVariable("var_a")这将允许我在加载模型后继续与模型中预先存在的变量进行交互。

Answer 1

根据@Stradivari 的评论，一种似乎有效的方法是简单地 pickle模型及其变量。
例如:

from ortools.sat.python import cp_model
import pickle

class ClonableModel:
    def __init__(self):
        self.model = cp_model.CpModel()
        self.vars = {}
        
    def create_model(self):
        self.vars['a'] = self.model.NewIntVar(0, 10, "var_a")
        self.vars['b'] = self.model.NewIntVar(0, 10, "var_b")

        self.model.Maximize(self.vars['a'] + self.vars['b'])
        
    # Also possible to serialize via a file / over network 
    def clone(self):
        return pickle.loads(pickle.dumps(self))
    
    def solve(self):
        solver = cp_model.CpSolver()
        status = solver.Solve(self.model)

        return '%s: %i' % (solver.StatusName(status), solver.ObjectiveValue())

现在，以下工作按预期工作:

model = ClonableModel()
model.create_model()

new_model = model.clone()
new_model.model.NewIntVar(0,5,"c")
new_model.model.Add(new_model.vars['a'] < c)

print('Original model: %s' % model.solve())
print('Cloned model: %s' % new_model.solve())

# Original model: OPTIMAL: 20
# Cloned model: OPTIMAL: 14