Failed initialization–Stan - Can't figure out - Please help

Modeling
1

Hi,
I’m trying to apply censore regression whenever the censor ==1, I use the following code:

# ----------------- CASE VI (testing )

modelWithCensor5= """
data {
   int<lower=0> Nobs_train;                           // Number of total data points
   real activation_train[Nobs_train];                // the predictor vector
   real y_train[Nobs_train];                        // the outcome  
   real censore[Nobs_train];
   int<lower=0,upper=1> run_estimation; // simulate (0) or estimate (1)

 }

parameters {
   real alpha;             
   real beta;    
   real<lower=0> eps;     
 }

model {
// prior

    alpha ~ normal(0, 10);
    beta ~ normal(0, 10);
    eps ~ normal(0, 10);

// likelihood, which we only evaluate conditionally
 
    for (i in 1:Nobs_train) {
     if (run_estimation ==1){
     
      if (censore[i] == 0 ) {
      target += normal_lpdf(y_train[i]|alpha + beta * activation_train[i] , eps);
       }
      else if (censore[i] == 1){ 
       target += normal_lccdf(y_train[i]|alpha + beta * activation_train[i], eps); 
      }
     }
    }
} 


generated quantities {
   vector[Nobs_train] y_tilde;
   for(i in 1:Nobs_train) {
    y_tilde[i] = normal_rng(alpha + beta * activation_train[i] , eps);
   }
 }

I run using the following data:

run_estimation = 1

data11 = {'Nobs_train':Nobs_train, 
         'N_tilde':N_tilde,
        'activation_train':activation_train,
        'y_train':y_train,
        'run_estimation':run_estimation,
         'censore':censore,
         'N_cens_train':N_cens_train}


fit11 = pystan.stan(model_code = modelWithCensor5, data=data11, iter=1000, chains=4)
print(fit11)

I get the initialization failed error as follows:

RuntimeError                              Traceback (most recent call last)
<ipython-input-266-e817eaef854e> in <module>()
     10 
     11 
---> 12 fit11 = pystan.stan(model_code = modelWithCensor5, data=data11, iter=1000, chains=4)
     13 print(fit11)

/usr/local/lib/python2.7/site-packages/pystan/api.pyc in stan(file, model_name, model_code, fit, data, pars, chains, iter, warmup, thin, init, seed, algorithm, control, sample_file, diagnostic_file, verbose, boost_lib, eigen_lib, n_jobs, **kwargs)
    400                      sample_file=sample_file, diagnostic_file=diagnostic_file,
    401                      verbose=verbose, algorithm=algorithm, control=control,
--> 402                      n_jobs=n_jobs, **kwargs)
    403     return fit

/usr/local/lib/python2.7/site-packages/pystan/model.pyc in sampling(self, data, pars, chains, iter, warmup, thin, seed, init, sample_file, diagnostic_file, verbose, algorithm, control, n_jobs, **kwargs)
    724         call_sampler_args = izip(itertools.repeat(data), args_list, itertools.repeat(pars))
    725         call_sampler_star = self.module._call_sampler_star
--> 726         ret_and_samples = _map_parallel(call_sampler_star, call_sampler_args, n_jobs)
    727         samples = [smpl for _, smpl in ret_and_samples]
    728 

/usr/local/lib/python2.7/site-packages/pystan/model.pyc in _map_parallel(function, args, n_jobs)
     79         try:
     80             pool = multiprocessing.Pool(processes=n_jobs)
---> 81             map_result = pool.map(function, args)
     82         finally:
     83             pool.close()

/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.pyc in map(self, func, iterable, chunksize)
    251         '''
    252         assert self._state == RUN
--> 253         return self.map_async(func, iterable, chunksize).get()
    254 
    255     def imap(self, func, iterable, chunksize=1):

/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.pyc in get(self, timeout)
    570             return self._value
    571         else:
--> 572             raise self._value
    573 
    574     def _set(self, i, obj):

RuntimeError: Initialization failed.

Can someone help me, please?

[edit: escaped code]

2

@ariddell or @ahartikainen — any idea what that error message means? I would hope to see an error from somewhere.

Are you letting Stan do the initialization? I don’t see where that’d fail if the inputs are all finite.

If censore is being used conditionally with ==, you should make it an integer.

If the data’s censored, don’t you want to use the censoring point? Is that what y_train[i] will be in censored cases?

3

Hmm,

Try first with n_jobs=1 and verbose=True

Also we recommend using

stan_model = pystan.StanModel(model_code=...)
fit = stan_model.sampling(data=...)

This way it is easier to see the problem.

This error might be somehow related to python 2.7.