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._chains):
if i == 0:
dzero = check_csv(self.csv_files[i],
is_optimizing=self.is_optimizing)
else:
d = check_csv(self.csv_files[i],
is_optimizing=self.is_optimizing)
for key in dzero:
if key not in ('id',
'first_draw') and dzero[key] != d[key]:
raise ValueError(
'csv file header mismatch, '
'file {}, key {} is {}, expected {}'.format(
self.csv_files[i], key, dzero[key], d[key]))
self._draws = dzero['draws']
self._column_names = dzero['column_names']
self._num_params = dzero['num_params']
self._first_draw = dzero.get('first_draw')
self._metric_type = dzero.get('metric')
def test_check_csv_1(self):
csv_good = os.path.join(datafiles_path, 'bernoulli_output_1.csv')
dict = check_csv(csv_good)
self.assertEqual('bernoulli_model', dict['model'])
self.assertEqual('10', dict['num_samples'])
self.assertFalse('save_warmup' in dict)
self.assertEqual(10, dict['draws'])
self.assertEqual(8, len(dict['column_names']))
def _set_attrs_gq_csv_files(self, sample_csv_0: str) -> None:
"""
Propogate information from original sample to additional sample
returned by run_generated_quantities.
"""
sample_meta = check_csv(sample_csv_0,
is_optimizing=False,
is_sampling=True)
self._draws = sample_meta['draws']
self._num_params = sample_meta['num_params']
self._first_draw = sample_meta.get('first_draw')
self._metric_type = sample_meta.get('metric')
dzero = scan_stan_csv(self._csv_files[0], is_sampling=False)
self._column_names = dzero['column_names']
def test_check_csv_metric_4(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_metric_4.csv')
with self.assertRaisesRegex(
Exception, 'invalid or missing mass matrix specification'):
dict = check_csv(csv_bad)
def test_check_csv_metric_2(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_metric_2.csv')
with self.assertRaisesRegex(Exception, 'invalid stepsize'):
dict = check_csv(csv_bad)
def test_check_csv_metric_1(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_metric_1.csv')
with self.assertRaisesRegex(Exception, 'expecting metric'):
dict = check_csv(csv_bad)
def test_check_csv_4(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_rows.csv')
with self.assertRaisesRegex(Exception, 'found 9'):
dict = check_csv(csv_bad)
def test_check_csv_3(self):
csv_bad = os.path.join(datafiles_path, 'output_bad_cols.csv')
with self.assertRaisesRegex(Exception, '8 items'):
dict = check_csv(csv_bad)
def test_check_csv_2(self):
csv_bad = os.path.join(datafiles_path, 'no_such_file.csv')
with self.assertRaises(Exception):
dict = check_csv(csv_bad)
