AttributeError: Can't pickle local object 'create_lossOdeint.<locals>.lossOdeint'

[gasilva]

I got this error when trying to use PDE

Traceback (most recent call last):
  File "/home/ats4i/Desktop/corona/dataAndModelsCovid19/dataFit_SEAIRD_v2YaboxEvolutionaryParams.py", line 1036, in <module>
    main(countriesExt)
  File "/home/ats4i/Desktop/corona/dataAndModelsCovid19/dataFit_SEAIRD_v2YaboxEvolutionaryParams.py", line 660, in main
    results = ray.get(results)
  File "/home/ats4i/anaconda3/lib/python3.7/site-packages/ray/worker.py", line 2349, in get
    raise value
ray.exceptions.RayTaskError: ray_Learner:train() (pid=117047, host=jedha)
  File "/home/ats4i/Desktop/corona/dataAndModelsCovid19/dataFit_SEAIRD_v2YaboxEvolutionaryParams.py", line 337, in train
    for step in de.geniterator():
  File "/home/ats4i/anaconda3/lib/python3.7/site-packages/yabox/algorithms/de.py", line 172, in geniterator
    for step in it:
  File "/home/ats4i/anaconda3/lib/python3.7/site-packages/yabox/algorithms/de.py", line 203, in iterator
    it = PDEIterator(self)
  File "/home/ats4i/anaconda3/lib/python3.7/site-packages/yabox/algorithms/de.py", line 70, in __init__
    super().__init__(de)
  File "/home/ats4i/anaconda3/lib/python3.7/site-packages/yabox/algorithms/de.py", line 9, in __init__
    self.fitness = de.evaluate(self.population)
  File "/home/ats4i/anaconda3/lib/python3.7/site-packages/yabox/algorithms/de.py", line 161, in evaluate
    return self.evaluate_denormalized(PD)
  File "/home/ats4i/anaconda3/lib/python3.7/site-packages/yabox/algorithms/de.py", line 213, in evaluate_denormalized
    return list(self.pool.map(self.fobj, PD, chunksize=self.chunksize))
  File "/home/ats4i/anaconda3/lib/python3.7/multiprocessing/pool.py", line 268, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
  File "/home/ats4i/anaconda3/lib/python3.7/multiprocessing/pool.py", line 657, in get
    raise self._value
  File "/home/ats4i/anaconda3/lib/python3.7/multiprocessing/pool.py", line 431, in _handle_tasks
    put(task)
  File "/home/ats4i/anaconda3/lib/python3.7/multiprocessing/connection.py", line 206, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/home/ats4i/anaconda3/lib/python3.7/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)

the function to be minimized

def create_lossOdeint(data, recovered, \
            death, s_0, e_0, a_0, i_0, r_0, d_0, startNCases, \
                 weigthCases, weigthRecov):
    def lossOdeint(point):
        size = len(data)
        beta, beta2, sigma, sigma2, sigma3, gamma, b, mu = point
        def SEAIRD(y,t):
            S = y[0]
            E = y[1]
            A = y[2]
            I = y[3]
            R = y[4]
            p=0.2
            # beta2=beta
            y0=-(beta2*A+beta*I)*S+mu*S #S
            y1=(beta2*A+beta*I)*S-sigma*E-mu*E #E
            y2=sigma*E*(1-p)-gamma*A-mu*A #A
            y3=sigma*E*p-gamma*I-sigma2*I-sigma3*I-mu*I #I
            y4=b*I+gamma*A+sigma2*I-mu*R #R
            y5=(-(y0+y1+y2+y3+y4)) #D
            return [y0,y1,y2,y3,y4,y5]

        y0=[s_0,e_0,a_0,i_0,r_0,d_0]
        tspan=np.arange(0, size, 1)
        res=odeint(SEAIRD,y0,tspan) 
        #,hmax=0.01)

        tot=0
        l1=0
        l2=0
        l3=0
        for i in range(0,len(data.values)):
            if data.values[i]>startNCases:
                l1 = l1+(res[i,3] - data.values[i])**2
                l2 = l2+(res[i,5] - death.values[i])**2
                l3 = l3+(res[i,4] - recovered.values[i])**2
                tot+=1
        l1=np.sqrt(l1/max(1,tot))
        l2=np.sqrt(l2/max(1,tot))
        l3=np.sqrt(l3/max(1,tot))

        #weight for cases
        u = weigthCases
        #weight for recovered
        w = weigthRecov 
        #weight for deaths
        v = max(0,1. - u - w)
        return u*l1 + v*l2 + w*l3 
    return lossOdeint

the call to minimizer

        bounds=[(1e-12, .2),(1e-12, .2),(1/60 ,0.4),(1/60, .4),
        (1/60, .4),(1e-12, .4),(1e-12, .4),(1e-12, .4)]

        maxiterations=1000
        f=create_lossOdeint(self.data, self.recovered, \
            self.death, self.s_0, self.e_0, self.a_0, self.i_0, self.r_0, self.d_0, self.startNCases, \
                 self.weigthCases, self.weigthRecov)
        de = PDE(f, bounds, maxiters=maxiterations)
        i=0
        with tqdm(total=maxiterations*500) as pbar:
            for step in de.geniterator():
                idx = step.best_idx
                norm_vector = step.population[idx]
                best_params = de.denormalize([norm_vector])
                pbar.update(i)
                i+=1
        p=best_params[0]