def validate_csv_files(self) -> None:
"""
Checks that csv output files for all chains are consistent.
Populates attributes for draws, column_names, num_params, metric_type.
Raises exception when inconsistencies detected.
"""
dzero = {}
for i in range(self.runset.chains):
if i == 0:
dzero = check_sampler_csv(
path=self.runset.csv_files[i],
is_fixed_param=self._is_fixed_param,
iter_sampling=self._iter_sampling,
iter_warmup=self._iter_warmup,
save_warmup=self._save_warmup,
thin=self._thin,
)
else:
drest = check_sampler_csv(
path=self.runset.csv_files[i],
is_fixed_param=self._is_fixed_param,
iter_sampling=self._iter_sampling,
iter_warmup=self._iter_warmup,
save_warmup=self._save_warmup,
thin=self._thin,
)
for key in dzero:
if (
key not in ['id', 'diagnostic_file']
and dzero[key] != drest[key]
):
raise ValueError(
'csv file header mismatch, '
'file {}, key {} is {}, expected {}'.format(
self.runset.csv_files[i],
key,
dzero[key],
drest[key],
)
)
self._draws_sampling = dzero['draws_sampling']
if self._save_warmup:
self._draws_warmup = dzero['draws_warmup']
else:
self._draws_warmup = 0
self._column_names = dzero['column_names']
if not self._is_fixed_param:
self._num_params = dzero['num_params']
self._metric_type = dzero.get('metric')
self._stan_variable_dims = parse_var_dims(dzero['column_names'])
def test_parse_scalar_vec_scalar(self):
x = [
'foo',
'phi.1',
'phi.2',
'phi.3',
'phi.4',
'phi.5',
'phi.6',
'phi.7',
'phi.8',
'phi.9',
'phi.10',
'bar',
]
vars_dict = parse_var_dims(x)
self.assertEqual(len(vars_dict), 3)
self.assertEqual(vars_dict['foo'], 1)
self.assertEqual(vars_dict['phi'], (10,))
self.assertEqual(vars_dict['bar'], 1)
def test_parse_scalar_vec_scalar(self):
x = [
'foo',
'phi[1]',
'phi[2]',
'phi[3]',
'phi[4]',
'phi[5]',
'phi[6]',
'phi[7]',
'phi[8]',
'phi[9]',
'phi[10]',
'bar',
]
vars_dict = parse_var_dims(x)
self.assertEqual(len(vars_dict), 3)
self.assertEqual(vars_dict['foo'], 1)
self.assertEqual(vars_dict['phi'], (10, ))
self.assertEqual(vars_dict['bar'], 1)
def test_parse_scalar_matrix_vec(self):
x = [
'foo',
'phi.1.1',
'phi.1.2',
'phi.1.3',
'phi.1.4',
'phi.1.5',
'phi.2.1',
'phi.2.2',
'phi.2.3',
'phi.2.4',
'phi.2.5',
'bar.1',
'bar.2',
]
vars_dict = parse_var_dims(x)
self.assertEqual(len(vars_dict), 3)
self.assertEqual(vars_dict['foo'], 1)
self.assertEqual(vars_dict['phi'], (2, 5))
self.assertEqual(vars_dict['bar'], (2,))
def test_parse_scalar_matrix_vec(self):
x = [
'foo',
'phi[1,1]',
'phi[1,2]',
'phi[1,3]',
'phi[1,4]',
'phi[1,5]',
'phi[2,1]',
'phi[2,2]',
'phi[2,3]',
'phi[2,4]',
'phi[2,5]',
'bar[1]',
'bar[2]',
]
vars_dict = parse_var_dims(x)
self.assertEqual(len(vars_dict), 3)
self.assertEqual(vars_dict['foo'], 1)
self.assertEqual(vars_dict['phi'], (2, 5))
self.assertEqual(vars_dict['bar'], (2, ))
def test_parse_scalars(self):
x = ['foo', 'foo1']
vars_dict = parse_var_dims(x)
self.assertEqual(len(vars_dict), 2)
self.assertEqual(vars_dict['foo'], 1)
self.assertEqual(vars_dict['foo1'], 1)
def test_parse_missing(self):
with self.assertRaises(ValueError):
parse_var_dims(None)
def test_parse_empty(self):
x = []
vars_dict = parse_var_dims(x)
self.assertEqual(len(vars_dict), 0)
