SciPy opitimize 'ValueError: setting an array element with a sequence.' ARIMA models - scipy

Question

I know that the issue 'ValueError: setting an array element with a sequence.' is normally because the function being optimized is a vector and not a scalar, anyhow my ARMA model below still gets this issue. Here is the code
def loghood(parm,endog,exog,p,q):
arparams,maparams,exogparams,bias = parm
armapredicts=np.zeros(endog.shape[0])
bias=0
res=abs(endog - np.mean(endog))
if p==0:
for i in range(1,endog.shape[0]-p):
armapredicts[i] = np.array([[res[i-f+q]] for f in range(0,q)]).dot(maparams.T) + exog.iloc[i,:].dot(exogparams.T) + bias
if q==0:
for i in range(1,endog.shape[0]-q):
armapredicts[i] = np.array([ [endog[i-f+p]] for f in range(0,p)]).T.dot(arparams) + exog.iloc[i,:].T.dot(exogparams) + bias
else:
for i in range(1,endog.shape[0]-2):
armapredicts[i] = np.array([ [endog[i-f+p]] for f in range(0,p)]).T.dot(arparams.reshape(-1,1)) + np.array([[res[i-f+q]] for f in range(0,q)]).T.dot(maparams.reshape(-1,1)) + exog.iloc[i,:].T.dot(exogparams.reshape(-1,1)) + bias
print(np.array([ [endog[i-f+p]] for f in range(0,p)]).T.shape )
print(maparams.reshape(-1,1).shape)
liklihood=1/((2*np.pi*armapredicts)**(1/2))*np.exp(-res**2/(2*armapredicts**2))
print(liklihood.shape)
log_hood=np.sum(np.log(liklihood.values))
print(log_hood)
return log_hood
x0=[np.ones(2),np.ones(2),np.ones(returnsant.shape[1]-1),1]
x0=np.array(x0,dtype=object).flatten()
res = spop.minimize(loghood,x0 ,args=(returnsant['Hedge Fund'],returnsant.drop('Hedge Fund',axis= 1),2,2), method='Nelder-mead')
print(res)
I know that likelihood is a 1-d vector and the log_hood is certainly scalar after np.sum, so how is this error occurring?? Thank you for your time.
EDIT:forgot to include the full error message
TypeError Traceback (most recent call last)
TypeError: only size-1 arrays can be converted to Python scalars
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
/var/folders/7f/gmpqnwqx0lb4nrsz2vqhvgn40000gp/T/ipykernel_30671/32993526.py in <module>
1 x0=[np.ones(2),np.ones(2),np.ones(returnsant.shape[1]-1),1]
2 #x0=np.array(x0,dtype=object).flatten()
----> 3 res = spop.minimize(loghood,x0 ,args=(returnsant['Hedge Fund'],returnsant.drop('Hedge Fund',axis= 1),2,2), method='Nelder-mead')
4 print(res)
~/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
609
610 if meth == 'nelder-mead':
--> 611 return _minimize_neldermead(fun, x0, args, callback, bounds=bounds,
612 **options)
613 elif meth == 'powell':
~/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/optimize.py in _minimize_neldermead(func, x0, args, callback, maxiter, maxfev, disp, return_all, initial_simplex, xatol, fatol, adaptive, bounds, **unknown_options)
687 zdelt = 0.00025
688
--> 689 x0 = asfarray(x0).flatten()
690
691 if bounds is not None:
~/opt/anaconda3/lib/python3.9/site-packages/numpy/core/overrides.py in asfarray(*args, **kwargs)
~/opt/anaconda3/lib/python3.9/site-packages/numpy/lib/type_check.py in asfarray(a, dtype)
112 if not _nx.issubdtype(dtype, _nx.inexact):
113 dtype = _nx.float_
--> 114 return asarray(a, dtype=dtype)
115
116
ValueError: setting an array element with a sequence.
FINAL EDIT:
I resolved the issue by simply creating a big array x0 of all my parameters
x0=np.zeros(5+returnsant.shape[1]-1)
res = spop.minimize(loghood,x0,args=(returnsant['Hedge Fund'],returnsant.drop('Hedge Fund',axis= 1),0,2), method='Nelder-mead')
print(res)
now I declare my subparameters in loghood
def loghood(parms,endog,exog,p,q):
arparams,maparams,exogparams,bias = parms[0:p],parms[p:p+q],parms[p+q:p+q+exog.shape[1]],parms[p+q+exog.shape[1]:-1]
armapredicts=np.zeros(endog.shape[0])
bias=0
res=abs(endog - np.mean(endog))
if p==0:
for i in range(1,endog.shape[0]-q):
armapredicts[i] = np.array([[res[i-f+q]] for f in range(0,q)]).T.dot(maparams) + exog.iloc[i,:].T.dot(exogparams) + bias
if q==0:
for i in range(1,endog.shape[0]-p):
armapredicts[i] = np.array([ [endog[i-f+p]] for f in range(0,p)]).T.dot(arparams) + exog.iloc[i,:].T.dot(exogparams) + bias
else:
for i in range(1,endog.shape[0]-2):
armapredicts[i] = np.array([ [endog[i-f+p]] for f in range(0,p)]).T.dot(arparams.reshape(-1,1)) + np.array([[res[i-f+q]] for f in range(0,q)]).T.dot(maparams.reshape(-1,1)) + exog.iloc[i,:].T.dot(exogparams.reshape(-1,1)) + bias
liklihood=1/((2*np.pi*armapredicts)**(1/2))*np.exp(-res**2/(2*armapredicts**2))
log_hood=np.sum(-np.log(liklihood.squeeze()))
return log_hood