def test_variational_eta_fail(self):
stan = os.path.join(DATAFILES_PATH, 'variational',
'eta_should_fail.stan')
model = CmdStanModel(stan_file=stan)
with self.assertRaisesRegex(RuntimeError,
'algorithm may not have converged'):
model.variational(algorithm='meanfield', seed=12345)
def test_variational_good(self):
stan = os.path.join(datafiles_path, 'variational',
'eta_should_be_big.stan')
model = CmdStanModel(stan_file=stan)
vi = model.variational(algorithm='meanfield', seed=12345)
self.assertEqual(vi.column_names,
('lp__', 'log_p__', 'log_g__', 'mu.1', 'mu.2'))
self.assertAlmostEqual(vi.variational_params_np[3], 31.0418, places=2)
self.assertAlmostEqual(vi.variational_params_np[4], 27.4463, places=2)
self.assertAlmostEqual(vi.variational_params_dict['mu.1'],
31.0418,
places=2)
self.assertAlmostEqual(vi.variational_params_dict['mu.2'],
27.4463,
places=2)
self.assertEqual(vi.variational_params_np[0],
vi.variational_params_pd['lp__'][0])
self.assertEqual(vi.variational_params_np[3],
vi.variational_params_pd['mu.1'][0])
self.assertEqual(vi.variational_params_np[4],
vi.variational_params_pd['mu.2'][0])
self.assertEqual(vi.variational_sample.shape, (1000, 5))
def test_variational_eta_small(self):
stan = os.path.join(datafiles_path, 'variational',
'eta_should_be_small.stan')
model = CmdStanModel(stan_file=stan)
vi = model.variational(algorithm='meanfield', seed=12345)
self.assertEqual(vi.column_names,
('lp__', 'log_p__', 'log_g__', 'mu.1', 'mu.2'))
self.assertAlmostEqual(fabs(vi.variational_params_dict['mu.1']),
0.08,
places=1)
self.assertAlmostEqual(fabs(vi.variational_params_dict['mu.2']),
0.09,
places=1)
self.assertTrue(True)
def test_variational_eta_small(self):
stan = os.path.join(DATAFILES_PATH, 'variational',
'eta_should_be_small.stan')
model = CmdStanModel(stan_file=stan)
variational = model.variational(algorithm='meanfield', seed=12345)
self.assertEqual(
variational.column_names,
('lp__', 'log_p__', 'log_g__', 'mu[1]', 'mu[2]'),
)
self.assertAlmostEqual(fabs(
variational.variational_params_dict['mu[1]']),
0.08,
places=1)
self.assertAlmostEqual(fabs(
variational.variational_params_dict['mu[2]']),
0.09,
places=1)
self.assertTrue(True)
cmdstanpy.install_cmdstan()
# instantiate, compile model
model_path = Path(args.models_path) / ('%s.stan' % args.model_name)
sicr_model = CmdStanModel(stan_file=model_path)
# create output directory for std out files cmdstan produces
save_dir = Path(args.fits_path)
save_dir.mkdir(parents=True, exist_ok=True)
output_dir = save_dir / 'std_out'
output_dir.mkdir(parents=True, exist_ok=True)
# run CmdStan's variational inference method, returns object `CmdStanVB`
sicr_model_vb = sicr_model.variational(data=stan_data,
algorithm=args.advi_algorithm,
grad_samples=4000,
elbo_samples=4000,
output_samples=4000,
eta=args.advi_eta,
adapt_iter=args.advi_adapt_iter,
output_dir=output_dir)
sicr_model_vb.variational_sample.shape
vb_results = sicr_model_vb.variational_params_dict # only gives means
vb_df = pd.DataFrame.from_dict(vb_results, orient="index")
print(vb_df)
# save_path = save_dir / ("%s_%s_ADVI_means.csv" % (args.model_name, args.roi))
# vb_df.to_csv(save_path)
