# Getting a runtime ititalization failure error on a poisson GLM

**URL:** <https://discourse.mc-stan.org/t/getting-a-runtime-ititalization-failure-error-on-a-poisson-glm/12633>\
**Category:** Modeling\
**Created:** [January 14, 2020, 3:40pm UTC](https://discourse.mc-stan.org/t/getting-a-runtime-ititalization-failure-error-on-a-poisson-glm/12633 "2020-01-14T15:40:40Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![Jordan\_Howell](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/jordan_howell/32/4786_2.png) [@Jordan\_Howell](https://discourse.mc-stan.org/u/Jordan_Howell)\
**Post date:** [January 14, 2020, 3:40pm UTC](https://discourse.mc-stan.org/t/getting-a-runtime-ititalization-failure-error-on-a-poisson-glm/12633/1 "2020-01-14T15:40:40Z")

</div>

Hello,

I’m trying to run the same Poisson GLM model from the python `statsmodel` package in Pystan. As I’ve added more than two variables, I’m starting to get an `RuntimeError: Initialization failed.` error.

The below is the model code and full error traceback.

I’m not sure why this is happening.

```stan
liability_code = """
data {
    int<lower=0> N;
    vector[N] BIlmt_model2;
    vector[N] multi_policy_count_model;
    vector[N] unit_value_model2;
    vector[N] yrs_owned_model2;
    vector[N] credit_c5_score;
    vector[N] credit_c6_score;
    vector[N] c6_2;
    vector[N] c6_3;
    vector[N] credit_52778_score;
    vector[N] nohit_52778;
    vector[N] thinfile_52778;
    vector[N] class_model2_Sport;
    vector[N] class_model2_Street;
    vector[N] class_model2_other;
    vector[N] Single_Driver_Age_model;
    vector[N] Driver_Age_model;
    int<lower=0> y1[N];
}
parameters {            
    real BIlmt_coeff;      
    real unit_value_model2_coeff;
    real yrs_owned_model2_coeff;
    real credit_c5_score_coeff;
    real credit_c6_score_coeff;
    real c6_2_coeff;
    real c6_3_coeff;
    real credit_52778_score_coeff;
    real thinfile_52778_coeff;
    real nohit_52778_coeff;
    real class_model2_Sport_coeff;
    real class_model2_Street_coeff;
    real class_model2_other_coeff;
    real Single_Driver_Age_model_coeff;
    real Driver_Age_model_coeff;
    real intercept; // parameter for mean or intercept   
}
model {
    BIlmt_coeff ~ normal(0, 1);  
    unit_value_model2_coeff ~ normal(0, 1); 
    yrs_owned_model2_coeff ~ normal(0, 1); 
    credit_c5_score_coeff ~ normal(0, 1); 
    credit_c6_score_coeff ~ normal(0, 1); 
    c6_2_coeff ~ normal(0, 1); 
    c6_3_coeff ~ normal(0, 1); 
    credit_52778_score_coeff ~ normal(0,1);
    thinfile_52778_coeff ~ normal(0,1);
    nohit_52778_coeff ~ normal(0,1);
    class_model2_Sport_coeff ~ normal(0, 1); 
    class_model2_Street_coeff ~ normal(0, 1); 
    class_model2_other_coeff ~ normal(0, 1); 
    Single_Driver_Age_model_coeff ~ normal(0, 1); 
    Driver_Age_model_coeff ~ normal(0, 1); 
    intercept ~ normal(0,5);
    
    y1 ~ poisson(BIlmt_model2*BIlmt_coeff + multi_policy_count_model*.9 + \
                  unit_value_model2*unit_value_model2_coeff + yrs_owned_model2*unit_value_model2_coeff +\
                  yrs_owned_model2*yrs_owned_model2_coeff + credit_c5_score*credit_c5_score_coeff +\
                  credit_c6_score*credit_c6_score_coeff + c6_2*c6_2_coeff + c6_3*c6_3_coeff +\
                  class_model2_Sport*class_model2_Sport_coeff + class_model2_Street*class_model2_Street_coeff +\
                  class_model2_other*class_model2_other_coeff + \
                  Single_Driver_Age_model*Single_Driver_Age_model_coeff + \
                  Driver_Age_model*Driver_Age_model_coeff + credit_52778_score*credit_52778_score_coeff + \
                  thinfile_52778*thinfile_52778_coeff + nohit_52778*nohit_52778_coeff + intercept);   
}
"""

sm_liab = StanModel(model_code=liability_code)

sm_fit = sm_liab.sampling(data=data1, warmup=500, verbose=True)

print(sm_fit)

```

ERROR

```
---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback: 
"""
Traceback (most recent call last):
  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\stan_env\lib\multiprocessing\pool.py", line 121, in worker
    result = (True, func(*args, **kwds))
  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\stan_env\lib\multiprocessing\pool.py", line 44, in mapstar
    return list(map(*args))
  File "stanfit4anon_model_ca67a6fa396bdaf9992be09ffdcd3b1c_5735096991081996350.pyx", line 373, in stanfit4anon_model_ca67a6fa396bdaf9992be09ffdcd3b1c_5735096991081996350._call_sampler_star
  File "stanfit4anon_model_ca67a6fa396bdaf9992be09ffdcd3b1c_5735096991081996350.pyx", line 406, in stanfit4anon_model_ca67a6fa396bdaf9992be09ffdcd3b1c_5735096991081996350._call_sampler
RuntimeError: Initialization failed.
"""

The above exception was the direct cause of the following exception:

RuntimeError Traceback (most recent call last)
<ipython-input-146-403e5a612b5d> in <module>
----> 1 sm_fit = sm_liab.sampling(data=data1, warmup=500, verbose=True)
      2 print(sm_fit)

~\AppData\Roaming\Python\Python37\site-packages\pystan\model.py in sampling(self, data, pars, chains, iter, warmup, thin, seed, init, sample_file, diagnostic_file, verbose, algorithm, control, n_jobs, **kwargs)
    811 call_sampler_args = izip(itertools.repeat(data), args_list, itertools.repeat(pars))
    812 call_sampler_star = self.module._call_sampler_star
--> 813 ret_and_samples = _map_parallel(call_sampler_star, call_sampler_args, n_jobs)
    814 samples = [smpl for _, smpl in ret_and_samples]
    815 

~\AppData\Roaming\Python\Python37\site-packages\pystan\model.py in _map_parallel(function, args, n_jobs)
     83 try:
     84 pool = multiprocessing.Pool(processes=n_jobs)
---> 85 map_result = pool.map(function, args)
     86 finally:
     87 pool.close()

~\AppData\Local\Continuum\anaconda3\envs\stan_env\lib\multiprocessing\pool.py in map(self, func, iterable, chunksize)
    266 in a list that is returned.
    267 '''
--> 268 return self._map_async(func, iterable, mapstar, chunksize).get()
    269 
    270 def starmap(self, func, iterable, chunksize=None):

~\AppData\Local\Continuum\anaconda3\envs\stan_env\lib\multiprocessing\pool.py in get(self, timeout)
    655 return self._value
    656 else:
--> 657 raise self._value
    658 
    659 def _set(self, i, obj):

RuntimeError: Initialization failed.

```

---

<div class="post-metadata">

**Author:** ![Max\_Mantei](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/max_mantei/32/7912_2.png) [@Max\_Mantei](https://discourse.mc-stan.org/u/Max_Mantei)\
**Post date:** [January 14, 2020, 3:54pm UTC](https://discourse.mc-stan.org/t/getting-a-runtime-ititalization-failure-error-on-a-poisson-glm/12633/2 "2020-01-14T15:54:48Z")

</div>

Hey! I think you did mean to fit you model on the log scale as is conventional for a Poisson GLM, right? In that case you need to change `y1 ~ poisson(...)` to `y1 ~ poisson_log(...)`. That should help Stan to find appropriate initial values.

---

<div class="post-metadata">

**Author:** ![Jordan\_Howell](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/jordan_howell/32/4786_2.png) [@Jordan\_Howell](https://discourse.mc-stan.org/u/Jordan_Howell)\
**Post date:** [January 14, 2020, 4:03pm UTC](https://discourse.mc-stan.org/t/getting-a-runtime-ititalization-failure-error-on-a-poisson-glm/12633/3 "2020-01-14T16:03:50Z")

</div>

Thank you. It’s running and hasn’t error’d out yet. I will let you know what happens. I assume this will take a long time at 251,000 observations.

---

<div class="post-metadata">

**Author:** ![Max\_Mantei](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/max_mantei/32/7912_2.png) [@Max\_Mantei](https://discourse.mc-stan.org/u/Max_Mantei)\
**Post date:** [January 14, 2020, 4:10pm UTC](https://discourse.mc-stan.org/t/getting-a-runtime-ititalization-failure-error-on-a-poisson-glm/12633/4 "2020-01-14T16:10:39Z")

</div>

I’m afraid, yes. You could try fitting subsets of your data (few thousands) until you have everything working.

For speed-ups, you can also look into using [`y ~ poisson_log_glm(x, alpha, beta)`](https://mc-stan.org/docs/2_21/functions-reference/poisson-log-glm.html). You’d have to put all predictors into a matrix and all coefficients into a vector, though.

Also, if your predictors are correlated, you can probably greatly increase speed of your model by using a [QR decomposed predictor matrix](https://mc-stan.org/docs/2_21/stan-users-guide/QR-reparameterization-section.html).

Cheers,  
Max