BrokenProcessPool Error

Modeling
,
1

Hi,

I am new to the Bayesian modelling world, and this is the first time I am using pystan for a Recommender Systems project.

I am trying to model user clicks using a simple Bayesian model with clicks being modelled as a binomial variable (for each user, item pairs). The number of impressions is the total number of trails for a given (user, item) pair and ‘click’ is a success.

The corresponding pystan code is as follows:

non_hierarchical_model = """
data{
int U;
int I;
int N;

int<lower=1> click[N];
int<lower=1> impressions[N];
int<lower=0> prod_idx[N];
int<lower=0> user_idx[N];
}

parameters {
  real<lower=0, upper=1> Alpha[I];
  real<lower=0, upper=1> Beta[I];
  
  real<lower=0, upper=1> Alpha1[U];
  real<lower=0, upper=1> Beta1[U];
  
  real<lower=0, upper=1> obs[N];
  real<lower=0, upper=1> rel[N];
  
}

model{

for (n in 1:N) {
    Alpha[prod_idx[n]] ~ beta(0.5, 0.5);
    Beta[prod_idx[n]] ~ beta(0.5,  0.5);
    
    Alpha1[user_idx[n]] ~ beta(0.5, 0.5);
    Beta1[user_idx[n]] ~ beta(0.5,  0.5);
    
    obs[n] ~ beta(Alpha[prod_idx[n]], Beta[prod_idx[n]]);
    rel[n] ~ beta(Alpha1[user_idx[n]], Beta1[user_idx[n]]);
    
    click[n] ~ binomial_logit(impressions[n],  obs[n] * rel[n]);
  }
}

"""

I am trying to model clicks as Observation * Relevance, where observation is defined for items and relevance is defined for users, and the final click probability is the product of the two.

When I try to sample from this model, I am getting the following error:

RuntimeError: Exception during call to services function: `BrokenProcessPool('A process in the process pool was terminated abruptly while the future was running or pending.')`, traceback: `['  File "/ivi/ilps/personal/sgupta/anaconda3/envs/pystan/lib/python3.7/site-packages/httpstan/services_stub.py", line 153, in call\n    future.result()\n']`

I have tried putting the code inside “main” block as per some discussion, but no success. Is it because of some modelling issue or something else all together?

2

How do you call pystan?

What os?

3

Thanks for your response!

I am calling it using:

posterior = stan.build(non_hierarchical_model, data=data)
fit = posterior.sample(num_chains=1, num_samples=100)

I am running it on CentOS Linux server.