def load_model_with_all_samples(depends_on, includes, burn):
"""Create a new model then load all the existing samples."""
paths = make_paths(depends_on, includes, 0)
sample_files = [f for f in ld(paths['model_dir']) if 'samples_' in f]
m = regression_model(depends_on, includes, True)
m.sample(3)
stochs = m.get_stochastics()
for node in stochs.node:
node.trace._trace[0] = np.array([])
for f in sample_files:
samples = pd.read_csv(pj(paths['model_dir'], f))
for node in stochs.node:
name = node.__name__
print node, name
s1 = copy(node.trace._trace[0])
s2 = samples[name].values[burn:]
node.trace._trace[0] = np.concatenate([s1, s2])
m.gen_stats()
return m
def run(args):
start = time()
depends_on, includes, it = args2lists(args)
model_paths = make_paths(depends_on, includes, it)
sys.stdout = open(model_paths['log_path'], 'w')
m = regression_model(depends_on, includes)
m.sample(8000)
m.get_traces().to_csv(model_paths['samples_path'])
print 'all done in %.1f seconds' % (time() - start)
