Python 在Pyomo中得到目标的梯度和Hessian

我有一个Pyomo模型，我想得到目标的梯度和Hessian。一位亲戚问了同样的问题。当我尝试那里提出的解决方案时

from pyomo.core.base.symbolic import differentiate
from pyomo.core.base.expr import identify_variables

varList = list(identify_variables(zipfe.loglikelihood.expr))
firstDerivs = differentiate(zipfe.loglikelihood.expr, wrt_list=varList)

我得到以下错误：

Traceback (most recent call last):
  File "/home/pauperei/.conda/envs/py36/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-9-6f2637b1fe13>", line 1, in <module>
    firstDerivs = differentiate(zipfe.loglikelihood.expr, wrt_list=varList)
  File "/home/pauperei/.conda/envs/py36/lib/python3.6/site-packages/pyomo/core/base/symbolic.py", line 122, in differentiate
    tmp_expr, locals=dict((str(x), x) for x in sympy_vars) )
  File "/home/pauperei/.conda/envs/py36/lib/python3.6/site-packages/sympy/core/sympify.py", line 354, in sympify
    expr = parse_expr(a, local_dict=locals, transformations=transformations, evaluate=evaluate)
  File "/home/pauperei/.conda/envs/py36/lib/python3.6/site-packages/sympy/parsing/sympy_parser.py", line 894, in parse_expr
    return eval_expr(code, local_dict, global_dict)
  File "/home/pauperei/.conda/envs/py36/lib/python3.6/site-packages/sympy/parsing/sympy_parser.py", line 807, in eval_expr
    code, global_dict, local_dict)  # take local objects in preference
  File "<string>", line 1, in <module>
TypeError: 'Symbol' object does not support indexing

问题似乎是Symphy不喜欢像

alpha1[0]

这样的索引变量。这个问题有解决办法吗

编辑： 我正在使用pyomo5.2和python3.6。我将尽快尝试添加一个最小的工作示例

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在过去的几天里，这项工作已经在pyomogithub存储库中完成，希望很快会有解决方案。

要使用索引变量，请使用

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区分

应该可以很好地处理索引变量。你能用pyomo版本、python版本和一个简单的例子来更新你的问题吗？在将所有变量定义为IndexedBase之后，我仍然有同样的问题。我认为这是正确的解决方案，但是应该在Pyomo中的differention函数中调用IndexBase。正如我在对我的问题的编辑中所评论的，这似乎是未来Pyomo版本的一项任务。谢谢

zipfe.loglikelihood.pprint()
loglikelihood : Size=1, Index=None, Active=True
Key  : Active : Sense    : Expression
None :   True : minimize : log( 1 + exp( alpha1[0] + 2.0*alpha1[1] + alpha1[4] + 2.8986705607108596*( delta[0] + 2.0*delta[1] ) ) ) - ( 2.0*beta1[0] + beta1[3] + 2.8986705607108596*( gamma[0] + 2.0*gamma[1] ) ) + log( exp(  - log( 1 + exp( alpha1[0] + 2.0*alpha1[1] + alpha1[4] + 2.8986705607108596*( delta[0] + 2.0*delta[1] ) ) ) + 2.0*beta1[0] + beta1[3] + 2.8986705607108596*( gamma[0] + 2.0*gamma[1] ) ) + exp(  - log( 1 + exp( alpha1[0] + 5.0*alpha1[1] + 2.8986705607108596*( delta[0] + 2.0*delta[1] ) ) ) + 5.0*beta1[0] + 2.8986705607108596*( gamma[0] + 2.0*gamma[1] ) ) + exp(  - log( 1 + exp( alpha1[0] + 2.0*alpha1[1] + alpha1[7] + 2.8986705607108596*( delta[0] + 2.0*delta[1] ) ) ) + 2.0*beta1[0] + beta1[6] + 2.8986705607108596*( gamma[0] + 2.0*gamma[1] ) ) + exp(  - log( 1 + exp( alpha1[0] + alpha1[1] + alpha1[6] 

>>> alpha1 = IndexedBase('alpha1')
>>> alpha1[0]
alpha1[0]