Hi All,

Im new to stan and running a tutorial on a CLV model found here:

Running python 3.6 on a conda environment.

I cannot work out whats going wrong when I execute the model. Keep Getting this error (bottom piece only)

File “/Users/garforbe/anaconda3/lib/python3.6/multiprocessing/pool.py”, line 644, in get

raise self._value

RuntimeError: Initialization failed.

Building the date object as below:

# here’s the data we will provide to STAN :

data={‘n_cust’:len(rfm),

‘x’:rfm[‘frequency’].values,

‘tx’:rfm[‘recency’].values,

‘T’:rfm[‘T’].values

}

Then using the cache method and execution (here is where it fails). Model is below.

iterations = 1000

warmup = 500

# I recommend training for several 1000’s iterations. Here we run the STAN model :

pareto_nbd_fit = stan_cache(paretonbd_model, model_name=‘paretonbd_model’,

data=data, chains=1, iter=iterations, warmup=warmup)

```
data{
int<lower=0> n_cust; //number of customers
vector<lower=0>[n_cust] x;
vector<lower=0>[n_cust] tx;
vector<lower=0>[n_cust] T;
}
parameters{
// vectors of lambda and mu for each customer.
// Here I apply limits between 0 and 1 for each
// parameter. A value of lambda or mu > 1.0 is unphysical
// since you don't enough time resolution to go less than
// 1 time unit.
vector <lower=0,upper=1.0>[n_cust] lambda;
vector <lower=0,upper=1.0>[n_cust] mu;
// parameters of the prior distributions : r, alpha, s, beta.
// for both lambda and mu
real <lower=0>r;
real <lower=0>alpha;
real <lower=0>s;
real <lower=0>beta;
}
model{
// temporary variables :
vector[n_cust] like1; // likelihood
vector[n_cust] like2; // likelihood
// Establishing hyperpriors on parameters r, alpha, s, and beta.
r ~ normal(0.5,0.1);
alpha ~ normal(10,1);
s ~ normal(0.5,0.1);
beta ~ normal(10,1);
// Establishing the Prior Distributions for lambda and mu :
lambda ~ gamma(r,alpha);
mu ~ gamma(s,beta);
// The likelihood of the Pareto/NBD model :
like1 = x .* log(lambda) + log(mu) - log(mu+lambda) - tx .* (mu+lambda);
like2 = (x + 1) .* log(lambda) - log(mu+lambda) - T .* (lambda+mu);
// Here we increment the log probability density (target) accordingly
target+= log(exp(like1)+exp(like2));
}
```

I have a feeling its something with the data setup but im not entirely sure, any help would be so appreciated!

Regards