python - ValueError: too many values to unpack (expected 3)?

Question

I have been having issues with the code I am trying to right with the model I am trying to code the following error has appeared and being a relative novice I am unsure of how to resolve it.

ValueError                                Traceback (most recent call last)
<ipython-input-2-5f21a0ce8185> in <module>()
     26         proposed[j] = proposed[j] + np.random.normal(0,propsigma[j])
     27         if (proposed[j]>0): # automatically reject moves if proposed parameter <=0
---> 28             alpha = np.exp(logistic_loglik(proposed,time,ExRatio,sig)-logistic_loglik(par_out[i-1,],time,ExRatio,sig))
     29             u = np.random.rand()
     30             if (u < alpha):

<ipython-input-2-5f21a0ce8185> in logistic_loglik(params, t, data, sig)
      3 # set up a function to return the log likelihood
      4 def logistic_loglik(params,t,data,sig):
----> 5     return sum(norm.logpdf(logistic(data, t, params),sig))
      6 
      7 # set standard deviations to be 10% of the population values

<ipython-input-1-c9480e66b7ef> in logistic(x, t, params)
      6 
      7 def logistic(x,t,params):
----> 8     S, R, A = x
      9     r, Nmax, delta_s, beta, gamma, delta_r, delta_a, Emax, H, MICs, MICr = params
     10     N = S + R

ValueError: too many values to unpack (expected 3) 

The model I am trying to code is an MCMC to fit some ODE's to some data I have added the code below for context.

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint

%matplotlib inline

def logistic(x,t,params):    
    S, R, A = x
    r, Nmax, delta_s, beta, gamma, delta_r, delta_a, Emax, H, MICs, MICr = params
    N = S + R
    E_s = 1 - (Emax * A**H)/(MICs**H + A**H)
    E_r = 1- (Emax * A**H)/(MICr**H + A**H)

    derivs = [r * (1 - N / Nmax ) * E_s * S - delta_s * S - ((beta * S * R)/N), 
     r * (1 - gamma) * (1 - N/Nmax) * E_r * R  - delta_r * R + ((beta * S * R)/N), - delta_a * A]


     return derivs

r = 0.5
Nmax = 10**7
delta_s = 0.025
beta = 10**-2
gamma = 0.5
delta_r = 0.025
delta_a = 0.003
Emax = 2
H = 2
MICs = 8
MICr = 2000

[r, Nmax, delta_s, beta, gamma, delta_r, delta_a, Emax, H, MICs, MICr] = params
S = 9 * 10**6
R = 10**5
A = 5.6
x0 = [S, R, A]

maxt = 2000
tstep = 1
t = np.arange(0,maxt,tstep)

def logistic_resid(params,t,data):
    return logistic(params,t)-data

logistic_out = odeint(logistic, x0, t, args=(params,))

time = np.array([0, 168, 336, 504, 672, 840, 1008, 1176, 1344, 1512, 1680, 1848, 2016, 2184, 2352, 2520, 2688, 2856])
ExRatio = np.array([2, 27, 43, 36, 39, 32, 27, 22, 13, 10, 14, 14, 4, 4, 7, 3, 3, 1])
ratio = 100* logistic_out[:,1]/(logistic_out[:,0]+logistic_out[:,1])
plt.plot(t,ratio)
plt.plot(time,ExRatio,'h')
xlabel('Position')
ylabel('Pollution')

New Cell

from scipy.stats import norm

# set up a function to return the log likelihood
def logistic_loglik(params,t,data,sig):
    return sum(norm.logpdf(logistic(data, t, params),sig))

# set standard deviations to be 10% of the population values
sig = ExRatio/10

# parameters for the MCMC
reps = 50000
npars = 3

# output matrix
par_out = np.ones(shape=(reps,npars))
# acceptance 
accept = np.zeros(shape=(reps,npars))
# proposal standard deviations. These have been pre-optimized.
propsigma = [0.05,20,5]

for i in range(1,reps):
    # make a copy of previous parameters
    par_out[i,] = par_out[i-1,]
    for j in range(npars):
        proposed = np.copy(par_out[i,:]) # we need to make a copy so that rejected moves don't affect the original matrix
        proposed[j] = proposed[j] + np.random.normal(0,propsigma[j])
        if (proposed[j]>0): # automatically reject moves if proposed parameter <=0 
            alpha = np.exp(logistic_loglik(proposed,time,ExRatio,sig)-logistic_loglik(par_out[i-1,],time,ExRatio,sig))
            u = np.random.rand()
            if (u < alpha):
                par_out[i,j] = proposed[j]
                accept[i,j] = 1

#print(sum(accept[range(101,reps),:])/(reps-100))


#plt.plot(par_out[:,0])
#plt.plot(par_out[range(101,reps),0])
#plt.plot(par_out[:,0],par_out[:,2])
plt.hist(par_out[range(101,reps),0],50)
print('
')
a=np.mean(par_out[range(101,reps),0])

I think its mistaking my parameters for something else but that might be wrong. I am using Jupyter notebook

Answer 1

You cannot use S, R, A = x, if x is empty or has not enough (too much) values to unpack.

For what I see, you are trying to define S, R and A values using the variable x. It is possible in that way only if x is the len of 3. If you want to assign certain x values to specific S, R or A use loop, or if you want to do this that way you can use:

S, R, *A = x,

this way the variable S and R will have the first and second element of x, and variable A the rest. You can put * before any variable to make it take the excessive values you store in x.