def _fit_stan_model(self, vb: bool, sm: StanModel, data_dict: Dict,
pars: List, gen_init: Union[str, Callable],
nchain: int, niter: int, nwarmup: int, nthin: int,
adapt_delta: float, stepsize: float,
max_treedepth: int, ncore: int) -> Any:
"""Fit the stan model.
Parameters
----------
vb
Whether to perform variational Bayesian analysis.
sm
The StanModel object to use to fit the model.
data_dict
Dict holding the data to pass to Stan.
pars
List specifying the parameters of interest.
gen_init
String or function to specify how to generate the initial values.
nchain
Number of chains to run.
niter
Number of iterations per chain.
nwarmup
Number of warm-up iterations.
nthin
Use every `i == nthin` sample to generate posterior distribution.
adapt_delta
Advanced control argument for sampler.
stepsize
Advanced control argument for sampler.
max_treedepth
Advanced control argument for sampler.
ncore
Argument for parallel computing while sampling multiple chains.
Returns
-------
fit
The fitted result returned by `vb` or `sampling` function.
"""
if vb:
return sm.vb(data=data_dict, pars=pars, init=gen_init)
else:
return sm.sampling(data=data_dict,
pars=pars,
init=gen_init,
chains=nchain,
iter=niter,
warmup=nwarmup,
thin=nthin,
control={
'adapt_delta': adapt_delta,
'stepsize': stepsize,
'max_treedepth': max_treedepth
},
n_jobs=ncore)
import pandas as pd
from pystan import StanModel
import matplotlib.pyplot as plt
import pickle
d = pd.read_csv('input/data-attendance-1.txt')
d.Score /= 200
data = d.to_dict('list')
data.update({'N':len(d)})
stanmodel = StanModel(file='model/model5-3.stan')
# NUTS (No U-Turn Sampler)
fit_nuts = stanmodel.sampling(data=data, n_jobs=1)
mcmc_sample = fit_nuts.extract()
mu_est = mcmc_sample['mu']
# ADVI (Automatic Differentiation Variational Inference)
fit_vb = stanmodel.vb(data=data)
vb_sample = pd.read_csv(fit_vb['args']['sample_file'].decode('utf-8'), comment='#')
vb_sample = vb_sample.drop([0,1])
mu_est = vb_sample.filter(regex='mu\.\d+')
with open('output/model_and_result.pkl', 'wb') as f:
pickle.dump(stanmodel, f)
pickle.dump(fit_nuts, f)
}
X ~ grg(r,N);
}
generated quantities{
real log_lik;
//likelihood eval
log_lik = grg_lpdf(X|r,N);
}
"""
#Inference
#fit = pystan.stan(model_code = stan_code, data = data, iter=1000, chains = 4, n_jobs=1, verbose = False);
#log_lik = fit.extract('log_lik')['log_lik'];
m = StanModel(model_code=stan_code)
fit = m.vb(data=data)
#print fit.keys();
print "log-likelihood:", fit['mean_pars'][-1]
#log_lik = functions['log_lik'];
#print "log-likelihood:", np.mean(log_lik);
#print(fit['args']['sample_file'])
#print "log-likelihood:", np.mean(log_lik);
