def test_get_posterior_summary_variational(self):
FILENAME = 'tests/test_output_variational.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_summary(stan_read_csv(FILENAME))
keys = ['mu', 'theta', 'tau']
subkeys = ['mean', 'std', 'p5', 'p25', 'p50', 'p75', 'p95']
self.assertListEqual(keys, list(result.keys()))
for key in keys:
self.assertListEqual(subkeys, list(result[key].keys()))
assert_allclose(result['mu']['mean'], np.array([1.84578]))
assert_allclose(result['mu']['std'], np.array([3.574338]))
assert_allclose(result['mu']['p5'], np.array([-3.888642]))
assert_allclose(result['mu']['p25'], np.array([-0.645503]), rtol=1e-6)
assert_allclose(result['mu']['p50'], np.array([1.88189]))
assert_allclose(result['mu']['p75'], np.array([4.229085]))
assert_allclose(result['mu']['p95'], np.array([7.662146]))
assert_allclose(result['theta']['mean'],
np.array([4.64194, 2.73732,
-0.087907, 2.4676, -0.007914,
1.06119, 5.76268, 1.77541]), rtol=1e-4)
assert_allclose(result['tau']['mean'], np.array([9.59118]))
def test_get_posterior_summary_variational(self):
FILENAME = 'tests/test_output_variational.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_summary(stan_read_csv(FILENAME))
keys = ['mu', 'theta', 'tau']
subkeys = ['mean', 'std', 'p5', 'p25', 'p50', 'p75', 'p95']
self.assertListEqual(keys, list(result.keys()))
for key in keys:
self.assertListEqual(subkeys, list(result[key].keys()))
assert_allclose(result['mu']['mean'], np.array([1.84578]))
assert_allclose(result['mu']['std'], np.array([3.574338]))
assert_allclose(result['mu']['p5'], np.array([-3.888642]))
assert_allclose(result['mu']['p25'], np.array([-0.645503]), rtol=1e-6)
assert_allclose(result['mu']['p50'], np.array([1.88189]))
assert_allclose(result['mu']['p75'], np.array([4.229085]))
assert_allclose(result['mu']['p95'], np.array([7.662146]))
assert_allclose(result['theta']['mean'],
np.array([
4.64194, 2.73732, -0.087907, 2.4676, -0.007914,
1.06119, 5.76268, 1.77541
]),
rtol=1e-4)
assert_allclose(result['tau']['mean'], np.array([9.59118]))
def test_read_csv_optimize(self):
FILENAME = 'tests/test_output_optimize.csv'
assert os.path.isfile(FILENAME)
result = stan_read_csv(FILENAME)
keys = ['lp__', 'mu', 'theta', 'tau']
self.assertListEqual(keys, list(result.keys()))
self.assertEqual(result['lp__'].shape, (1,))
self.assertEqual(result['mu'].shape, (1,))
self.assertEqual(result['theta'].shape, (8,))
self.assertEqual(result['tau'].shape, (1,))
def test_get_posterior_summary_sample(self):
FILENAME = 'tests/test_output_sample.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_summary(stan_read_csv(FILENAME))
keys = [
'lp__', 'accept_stat__', 'stepsize__', 'treedepth__',
'n_leapfrog__', 'divergent__', 'energy__', 'mu', 'theta', 'tau'
]
subkeys = ['mean', 'std', 'p5', 'p25', 'p50', 'p75', 'p95']
self.assertListEqual(keys, list(result.keys()))
for key in keys:
self.assertListEqual(subkeys, list(result[key].keys()))
assert_allclose(result['mu']['mean'], np.array([8.073827]))
assert_allclose(result['mu']['std'], np.array([5.25104899]))
assert_allclose(result['mu']['p5'], np.array([-0.4602165]))
assert_allclose(result['mu']['p25'], np.array([4.81581]))
assert_allclose(result['mu']['p50'], np.array([8.11732]))
assert_allclose(result['mu']['p75'], np.array([11.429175]))
assert_allclose(result['mu']['p95'], np.array([16.19353]))
assert_allclose(
result['theta']['mean'],
np.array([
12.419558, 7.816183, 5.874417, 7.385253, 4.876437, 5.872688,
11.357495, 8.674288
]))
assert_allclose(
result['theta']['std'],
np.array([
8.61228728, 6.68721892, 7.91427956, 7.39105258, 6.32559623,
7.03889131, 6.97848441, 8.82055328
]))
assert_allclose(
result['theta']['p5'],
np.array([
0.1057379, -2.863044, -8.5084845, -5.1880425, -5.63355,
-6.229635, 0.9704559, -4.523796
]))
assert_allclose(
result['theta']['p50'],
np.array([
11.26905, 7.880545, 6.40003, 7.525235, 5.318285, 6.118715,
10.94805, 8.87839
]))
assert_allclose(
result['theta']['p95'],
np.array([
28.27765, 19.00013, 17.481145, 19.8772, 14.34689, 16.32348,
24.068015, 23.40457
]))
def test_read_csv_optimize(self):
FILENAME = 'tests/test_output_optimize.csv'
assert os.path.isfile(FILENAME)
result = stan_read_csv(FILENAME)
keys = ['lp__', 'mu', 'theta', 'tau']
self.assertListEqual(keys, list(result.keys()))
self.assertEqual(result['lp__'].shape, (1, ))
self.assertEqual(result['mu'].shape, (1, ))
self.assertEqual(result['theta'].shape, (8, ))
self.assertEqual(result['tau'].shape, (1, ))
def test_get_posterior_estimates_variational(self):
FILENAME = 'tests/test_output_variational.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_estimates(stan_read_csv(FILENAME))
keys = ['mu', 'theta', 'tau']
self.assertListEqual(keys, list(result.keys()))
assert_allclose(result['mu'], np.array([1.84578]))
assert_allclose(result['theta'],
np.array([4.64194, 2.73732,
-0.087907, 2.4676, -0.007914,
1.06119, 5.76268, 1.77541]), rtol=1e-4)
assert_allclose(result['tau'], np.array([9.59118]))
def test_read_csv_sample(self):
FILENAME = 'tests/test_output_sample.csv'
assert os.path.isfile(FILENAME)
result = stan_read_csv(FILENAME)
keys = ['lp__', 'accept_stat__', 'stepsize__', 'treedepth__',
'n_leapfrog__', 'divergent__', 'energy__', 'mu', 'theta',
'tau']
self.assertListEqual(keys, list(result.keys()))
self.assertEqual(result['lp__'].shape, (1000, 1))
self.assertEqual(result['mu'].shape, (1000, 1))
self.assertEqual(result['theta'].shape, (1000, 8))
self.assertEqual(result['tau'].shape, (1000, 1))
def test_read_csv_sample(self):
FILENAME = 'tests/test_output_sample.csv'
assert os.path.isfile(FILENAME)
result = stan_read_csv(FILENAME)
keys = [
'lp__', 'accept_stat__', 'stepsize__', 'treedepth__',
'n_leapfrog__', 'divergent__', 'energy__', 'mu', 'theta', 'tau'
]
self.assertListEqual(keys, list(result.keys()))
self.assertEqual(result['lp__'].shape, (1000, 1))
self.assertEqual(result['mu'].shape, (1000, 1))
self.assertEqual(result['theta'].shape, (1000, 8))
self.assertEqual(result['tau'].shape, (1000, 1))
def test_get_posterior_estimates_optimize(self):
FILENAME = 'tests/test_output_optimize.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_estimates(stan_read_csv(FILENAME))
keys = ['lp__', 'mu', 'theta', 'tau']
self.assertListEqual(keys, list(result.keys()))
assert_allclose(result['lp__'], np.array([276.575]))
assert_allclose(result['mu'], np.array([0.765896]))
assert_allclose(result['theta'],
np.array([0.765896, 0.765896,
0.765896, 0.765896, 0.765896,
0.765896, 0.765896, 0.765896]))
assert_allclose(result['tau'], np.array([4.335870e-16]))
def test_get_posterior_estimates_variational(self):
FILENAME = 'tests/test_output_variational.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_estimates(stan_read_csv(FILENAME))
keys = ['mu', 'theta', 'tau']
self.assertListEqual(keys, list(result.keys()))
assert_allclose(result['mu'], np.array([1.84578]))
assert_allclose(result['theta'],
np.array([
4.64194, 2.73732, -0.087907, 2.4676, -0.007914,
1.06119, 5.76268, 1.77541
]),
rtol=1e-4)
assert_allclose(result['tau'], np.array([9.59118]))
def test_get_posterior_estimates_optimize(self):
FILENAME = 'tests/test_output_optimize.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_estimates(stan_read_csv(FILENAME))
keys = ['lp__', 'mu', 'theta', 'tau']
self.assertListEqual(keys, list(result.keys()))
assert_allclose(result['lp__'], np.array([276.575]))
assert_allclose(result['mu'], np.array([0.765896]))
assert_allclose(
result['theta'],
np.array([
0.765896, 0.765896, 0.765896, 0.765896, 0.765896, 0.765896,
0.765896, 0.765896
]))
assert_allclose(result['tau'], np.array([4.335870e-16]))
def test_get_posterior_estimates_sample(self):
FILENAME = 'tests/test_output_sample.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_estimates(stan_read_csv(FILENAME))
keys = ['lp__', 'accept_stat__', 'stepsize__', 'treedepth__',
'n_leapfrog__', 'divergent__', 'energy__', 'mu', 'theta',
'tau']
self.assertListEqual(keys, list(result.keys()))
assert_allclose(result['lp__'], np.array([-18.94640541]))
assert_allclose(result['mu'], np.array([8.073827]))
assert_allclose(result['theta'],
np.array([12.419558, 7.816183,
5.874417, 7.385253, 4.876437,
5.872688, 11.357495, 8.674288]))
assert_allclose(result['tau'], np.array([7.735637]))
def test_read_csv_variational(self):
FILENAME = 'tests/test_output_variational.csv'
assert os.path.isfile(FILENAME)
result = stan_read_csv(FILENAME)
keys = ['mean_pars', 'sampled_pars']
self.assertListEqual(keys, list(result.keys()))
keys_param = ['mu', 'theta', 'tau']
self.assertListEqual(keys_param, list(result['mean_pars'].keys()))
self.assertListEqual(keys_param, list(result['sampled_pars'].keys()))
self.assertEqual(result['mean_pars']['mu'].shape, (1,))
self.assertEqual(result['mean_pars']['theta'].shape, (8,))
self.assertEqual(result['mean_pars']['tau'].shape, (1,))
self.assertEqual(result['sampled_pars']['mu'].shape, (1000, 1))
self.assertEqual(result['sampled_pars']['theta'].shape, (1000, 8))
self.assertEqual(result['sampled_pars']['tau'].shape, (1000, 1))
def test_read_csv_variational(self):
FILENAME = 'tests/test_output_variational.csv'
assert os.path.isfile(FILENAME)
result = stan_read_csv(FILENAME)
keys = ['mean_pars', 'sampled_pars']
self.assertListEqual(keys, list(result.keys()))
keys_param = ['mu', 'theta', 'tau']
self.assertListEqual(keys_param, list(result['mean_pars'].keys()))
self.assertListEqual(keys_param, list(result['sampled_pars'].keys()))
self.assertEqual(result['mean_pars']['mu'].shape, (1, ))
self.assertEqual(result['mean_pars']['theta'].shape, (8, ))
self.assertEqual(result['mean_pars']['tau'].shape, (1, ))
self.assertEqual(result['sampled_pars']['mu'].shape, (1000, 1))
self.assertEqual(result['sampled_pars']['theta'].shape, (1000, 8))
self.assertEqual(result['sampled_pars']['tau'].shape, (1000, 1))
def test_get_posterior_summary_sample(self):
FILENAME = 'tests/test_output_sample.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_summary(stan_read_csv(FILENAME))
keys = ['lp__', 'accept_stat__', 'stepsize__', 'treedepth__',
'n_leapfrog__', 'divergent__', 'energy__', 'mu', 'theta',
'tau']
subkeys = ['mean', 'std', 'p5', 'p25', 'p50', 'p75', 'p95']
self.assertListEqual(keys, list(result.keys()))
for key in keys:
self.assertListEqual(subkeys, list(result[key].keys()))
assert_allclose(result['mu']['mean'], np.array([8.073827]))
assert_allclose(result['mu']['std'], np.array([5.25104899]))
assert_allclose(result['mu']['p5'], np.array([-0.4602165]))
assert_allclose(result['mu']['p25'], np.array([4.81581]))
assert_allclose(result['mu']['p50'], np.array([8.11732]))
assert_allclose(result['mu']['p75'], np.array([11.429175]))
assert_allclose(result['mu']['p95'], np.array([16.19353]))
assert_allclose(result['theta']['mean'],
np.array([12.419558, 7.816183,
5.874417, 7.385253, 4.876437,
5.872688, 11.357495, 8.674288]))
assert_allclose(result['theta']['std'],
np.array([8.61228728, 6.68721892,
7.91427956, 7.39105258, 6.32559623,
7.03889131, 6.97848441, 8.82055328]))
assert_allclose(result['theta']['p5'],
np.array([0.1057379, -2.863044,
-8.5084845, -5.1880425, -5.63355,
-6.229635, 0.9704559, -4.523796]))
assert_allclose(result['theta']['p50'],
np.array([11.26905, 7.880545,
6.40003, 7.525235, 5.318285,
6.118715, 10.94805, 8.87839]))
assert_allclose(result['theta']['p95'],
np.array([28.27765, 19.00013,
17.481145, 19.8772, 14.34689,
16.32348, 24.068015, 23.40457]))
def test_get_posterior_estimates_sample(self):
FILENAME = 'tests/test_output_sample.csv'
assert os.path.isfile(FILENAME)
result = get_posterior_estimates(stan_read_csv(FILENAME))
keys = [
'lp__', 'accept_stat__', 'stepsize__', 'treedepth__',
'n_leapfrog__', 'divergent__', 'energy__', 'mu', 'theta', 'tau'
]
self.assertListEqual(keys, list(result.keys()))
assert_allclose(result['lp__'], np.array([-18.94640541]))
assert_allclose(result['mu'], np.array([8.073827]))
assert_allclose(
result['theta'],
np.array([
12.419558, 7.816183, 5.874417, 7.385253, 4.876437, 5.872688,
11.357495, 8.674288
]))
assert_allclose(result['tau'], np.array([7.735637]))
def test_variational_invalid(self):
FILENAME = 'tests/test_output_variational_invalid.csv'
assert os.path.isfile(FILENAME)
with self.assertRaises(RuntimeError):
stan_read_csv(FILENAME)
def test_no_method(self):
FILENAME = 'tests/test_output_no_method.csv'
assert os.path.isfile(FILENAME)
with self.assertRaises(RuntimeError):
stan_read_csv(FILENAME)
def test_no_method(self):
FILENAME = 'tests/test_output_no_method.csv'
assert os.path.isfile(FILENAME)
with self.assertRaises(RuntimeError):
stan_read_csv(FILENAME)
def test_variational_invalid(self):
FILENAME = 'tests/test_output_variational_invalid.csv'
assert os.path.isfile(FILENAME)
with self.assertRaises(RuntimeError):
stan_read_csv(FILENAME)