Avoiding recompilation in Stan models

Compiling models takes time so if the same model is going to be used repeatedly, we would like to compile it just once.

Within sessions you can avoid recompiling a model in two ways. The simplest method is to reuse a fit object in the call to stan:

model_code = {
'data {'
'    int<lower=0> N;'
'    int<lower=0,upper=1> y[N];'
'}'
'parameters {'
'    real<lower=0,upper=1> theta;'
'}'
'model {'
'for (n in 1:N)'
'    y[n] ~ bernoulli(theta);'
'}'
};

data = struct('N',10,'y',[0, 1, 0, 0, 0, 0, 0, 0, 0, 1]);

% This call will compile the model
fit = stan('model_code',model_code,'data',data);
print(fit);

new_data = struct('N',10,'y',[0, 1, 0, 1, 0, 1, 0, 1, 1, 1]);

% Passing in StanFit object skips recompilation
fit2 = stan('fit',fit,'data',new_data);
print(fit2);

Alternatively, we can create an StanModel instance and call the compile method first:

sm = StanModel('model_code',model_code);
sm.compile();

% subsequent calls will skip recompilation
fit3 = sm.sampling('data',data);
print(fit3);

fit4 = sm.sampling('data',data);
print(fit4);

This could for example be used to fit the same model to many data sets:

% True values for theta
theta = 0.1:.2:1;

% Set this to the number of cores of your machine to avoid warnings
ncores = 4;

for i = 1:numel(theta)
   % Generate some fake data
   data = struct('N',10,'y',double(rand(1,10)<theta(i)));
   
   % Sample, using the model compiled above for each data set
   fit(i) = stan('fit',sm,'data',data,'chains',min(i,ncores),'iter',150000);
   
   fprintf('Model: %s, id: %s, #chains=%g, seed=%g\n',...
      fit(i).model.model_name,fit(i).model.id,...
      fit(i).model.chains,fit(i).model.seed);
end

theta
% Run the following command after all fits are completed
arrayfun(@(x) mean(x.extract.theta),fit)