def validate(self, chains: int) -> None:
"""
Check arguments correctness and consistency.
* adaptation and warmup args are consistent
* if file(s) for metric are supplied, check contents.
* length of per-chain lists equals specified # of chains
"""
if not isinstance(chains, Integral) or chains < 1:
raise ValueError(
'sampler expects number of chains to be greater than 0')
if not (self.adapt_delta is None and self.adapt_init_phase is None
and self.adapt_metric_window is None
and self.adapt_step_size is None):
if self.adapt_engaged is False:
msg = 'conflicting arguments: adapt_engaged: False'
if self.adapt_delta is not None:
msg = '{}, adapt_delta: {}'.format(msg, self.adapt_delta)
if self.adapt_init_phase is not None:
msg = '{}, adapt_init_phase: {}'.format(
msg, self.adapt_init_phase)
if self.adapt_metric_window is not None:
msg = '{}, adapt_metric_window: {}'.format(
msg, self.adapt_metric_window)
if self.adapt_step_size is not None:
msg = '{}, adapt_step_size: {}'.format(
msg, self.adapt_step_size)
raise ValueError(msg)
if self.iter_warmup is not None:
if self.iter_warmup < 0 or not isinstance(self.iter_warmup,
Integral):
raise ValueError('iter_warmup must be a non-negative integer,'
' found {}'.format(self.iter_warmup))
if self.iter_warmup > 0 and not self.adapt_engaged:
raise ValueError(
'adapt_engaged is False, cannot specify warmup iterations')
if self.iter_sampling is not None:
if self.iter_sampling < 0 or not isinstance(
self.iter_sampling, Integral):
raise ValueError(
'iter_sampling must be a non-negative integer,'
' found {}'.format(self.iter_sampling))
if self.thin is not None:
if self.thin < 1 or not isinstance(self.thin, Integral):
raise ValueError('thin must be a positive integer,'
'found {}'.format(self.thin))
if self.max_treedepth is not None:
if self.max_treedepth < 1 or not isinstance(
self.max_treedepth, Integral):
raise ValueError('max_treedepth must be a positive integer,'
' found {}'.format(self.max_treedepth))
if self.step_size is not None:
if isinstance(self.step_size, Real):
if self.step_size <= 0:
raise ValueError('step_size must be > 0, found {}'.format(
self.step_size))
else:
if len(self.step_size) != chains:
raise ValueError(
'number of step_sizes must match number of chains,'
' found {} step_sizes for {} chains'.format(
len(self.step_size), chains))
for step_size in self.step_size:
if step_size < 0:
raise ValueError(
'step_size must be > 0, found {}'.format(
step_size))
if self.metric is not None:
dims = []
if isinstance(self.metric, str):
if self.metric in ['diag', 'diag_e']:
self.metric = 'diag_e'
elif self.metric in ['dense', 'dense_e']:
self.metric = 'dense_e'
else:
if not os.path.exists(self.metric):
raise ValueError('no such file {}'.format(self.metric))
dims = read_metric(self.metric)
elif isinstance(self.metric, (list, tuple)):
if len(self.metric) != chains:
raise ValueError(
'number of metric files must match number of chains,'
' found {} metric files for {} chains'.format(
len(self.metric), chains))
names_set = set(self.metric)
if len(names_set) != len(self.metric):
raise ValueError(
'each chain must have its own metric file,'
' found duplicates in metric files list.')
for i, metric in enumerate(self.metric):
if not os.path.exists(metric):
raise ValueError('no such file {}'.format(metric))
if i == 0:
dims = read_metric(metric)
else:
dims2 = read_metric(metric)
if len(dims) != len(dims2):
raise ValueError('metrics files {}, {},'
' inconsistent metrics'.format(
self.metric[0], metric))
for dim, dim2 in zip(dims, dims2):
if dim != dim2:
raise ValueError(
'metrics files {}, {},'
' inconsistent metrics'.format(
self.metric[0], metric))
if any(dims):
if len(dims) > 2 or (len(dims) == 2 and dims[0] != dims[1]):
raise ValueError('bad metric specifiation')
self.metric_file = self.metric
if len(dims) == 1:
self.metric = 'diag_e'
elif len(dims) == 2:
self.metric = 'dense_e'
if self.adapt_delta is not None:
if not 0 < self.adapt_delta < 1:
raise ValueError('adapt_delta must be between 0 and 1,'
' found {}'.format(self.adapt_delta))
if self.adapt_init_phase is not None:
if self.adapt_init_phase < 0 or not isinstance(
self.adapt_init_phase, Integral):
raise ValueError(
'adapt_init_phase must be a non-negative integer,'
'found {}'.format(self.adapt_init_phase))
if self.adapt_metric_window is not None:
if self.adapt_metric_window < 0 or not isinstance(
self.adapt_metric_window, Integral):
raise ValueError(
'adapt_metric_window must be a non-negative integer,'
'found {}'.format(self.adapt_metric_window))
if self.adapt_step_size is not None:
if self.adapt_step_size < 0 or not isinstance(
self.adapt_step_size, Integral):
raise ValueError(
'adapt_step_size must be a non-negative integer,'
'found {}'.format(self.adapt_step_size))
if self.fixed_param and (
(self.iter_warmup is not None and self.iter_warmup > 0)
or self.save_warmup or self.max_treedepth is not None
or self.metric is not None or self.step_size is not None or
not (self.adapt_delta is None and self.adapt_init_phase is None
and self.adapt_metric_window is None
and self.adapt_step_size is None)):
raise ValueError('when fixed_param=True, cannot specify warmup'
' or adaptation parameters.')
def validate(self, chains: int) -> None:
"""
Check arguments correctness and consistency.
* adaptation and warmup args are consistent
* if file(s) for metric are supplied, check contents.
* length of per-chain lists equals specified # of chains
"""
if not isinstance(chains, Integral) or chains < 1:
raise ValueError(
'sampler expects number of chains to be greater than 0'
)
if self.warmup_iters is not None:
if self.warmup_iters < 0:
raise ValueError(
'warmup_iters must be a non-negative integer'.format(
self.warmup_iters
)
)
if self.adapt_engaged and self.warmup_iters == 0:
raise ValueError(
'adaptation requested but 0 warmup iterations specified, '
'must run warmup iterations'
)
if self.sampling_iters is not None:
if self.sampling_iters < 0:
raise ValueError(
'sampling_iters must be a non-negative integer'.format(
self.sampling_iters
)
)
if self.thin is not None:
if self.thin < 1:
raise ValueError(
'thin must be at least 1, found {}'.format(self.thin)
)
if self.max_treedepth is not None:
if self.max_treedepth < 1:
raise ValueError(
'max_treedepth must be at least 1, found {}'.format(
self.max_treedepth
)
)
if self.step_size is not None:
if isinstance(self.step_size, Real):
if self.step_size < 0:
raise ValueError(
'step_size must be > 0, found {}'.format(self.step_size)
)
else:
if len(self.step_size) != chains:
raise ValueError(
'number of step_sizes must match number of chains '
' found {} step_sizes for {} chains '.format(
len(self.step_size), chains
)
)
for step_size in self.step_size:
if step_size < 0:
raise ValueError(
'step_size must be > 0, found {}'.format(step_size)
)
if self.metric is not None:
dims = None
if isinstance(self.metric, str):
if self.metric in ['diag', 'diag_e']:
self.metric = 'diag_e'
elif self.metric in ['dense', 'dense_e']:
self.metric = 'dense_e'
else:
if not os.path.exists(self.metric):
raise ValueError('no such file {}'.format(self.metric))
dims = read_metric(self.metric)
elif isinstance(self.metric, (list, tuple)):
if len(self.metric) != chains:
raise ValueError(
'number of metric files must match number of chains '
' found {} metric files for {} chains '.format(
len(self.metric), chains
)
)
names_set = set(self.metric)
if len(names_set) != len(self.metric):
raise ValueError(
'each chain must have its own metric file,'
' found duplicates in metric files list.'
)
for i, metric in enumerate(self.metric):
if not os.path.exists(metric):
raise ValueError('no such file {}'.format(metric))
if i == 0:
dims = read_metric(metric)
else:
dims2 = read_metric(metric)
if len(dims) != len(dims2):
raise ValueError(
'metrics files {}, {},'
' inconsistent metrics'.format(
self.metric[0], metric
)
)
for j, dim in enumerate(dims):
if dims[j] != dims2[j]:
raise ValueError(
'metrics files {}, {},'
' inconsistent metrics'.format(
self.metric[0], metric
)
)
if dims is not None:
if len(dims) > 2 or (len(dims) == 2 and dims[0] != dims[1]):
raise ValueError('bad metric specifiation')
self.metric_file = self.metric
if len(dims) == 1:
self.metric = 'diag_e'
elif len(dims) == 2:
self.metric = 'dense_e'
if self.adapt_delta is not None:
if not 0 < self.adapt_delta < 1:
raise ValueError(
'adapt_delta must be between 0 and 1,'
' found {}'.format(self.adapt_delta)
)
def validate(self, chains: Optional[int]) -> None:
"""
Check arguments correctness and consistency.
* adaptation and warmup args are consistent
* if file(s) for metric are supplied, check contents.
* length of per-chain lists equals specified # of chains
"""
if not isinstance(chains, int) or chains < 1:
raise ValueError(
'Sampler expects number of chains to be greater than 0.')
if not (self.adapt_delta is None and self.adapt_init_phase is None
and self.adapt_metric_window is None
and self.adapt_step_size is None):
if self.adapt_engaged is False:
msg = 'Conflicting arguments: adapt_engaged: False'
if self.adapt_delta is not None:
msg = '{}, adapt_delta: {}'.format(msg, self.adapt_delta)
if self.adapt_init_phase is not None:
msg = '{}, adapt_init_phase: {}'.format(
msg, self.adapt_init_phase)
if self.adapt_metric_window is not None:
msg = '{}, adapt_metric_window: {}'.format(
msg, self.adapt_metric_window)
if self.adapt_step_size is not None:
msg = '{}, adapt_step_size: {}'.format(
msg, self.adapt_step_size)
raise ValueError(msg)
if self.iter_warmup is not None:
if self.iter_warmup < 0 or not isinstance(self.iter_warmup, int):
raise ValueError(
'Value for iter_warmup must be a non-negative integer,'
' found {}.'.format(self.iter_warmup))
if self.iter_warmup > 0 and not self.adapt_engaged:
raise ValueError('Argument "adapt_engaged" is False, '
'cannot specify warmup iterations.')
if self.iter_sampling is not None:
if self.iter_sampling < 0 or not isinstance(
self.iter_sampling, int):
raise ValueError(
'Argument "iter_sampling" must be a non-negative integer,'
' found {}.'.format(self.iter_sampling))
if self.thin is not None:
if self.thin < 1 or not isinstance(self.thin, int):
raise ValueError('Argument "thin" must be a positive integer,'
'found {}.'.format(self.thin))
if self.max_treedepth is not None:
if self.max_treedepth < 1 or not isinstance(
self.max_treedepth, int):
raise ValueError(
'Argument "max_treedepth" must be a positive integer,'
' found {}.'.format(self.max_treedepth))
if self.step_size is not None:
if isinstance(self.step_size, (float, int)):
if self.step_size <= 0:
raise ValueError('Argument "step_size" must be > 0, '
'found {}.'.format(self.step_size))
else:
if len(self.step_size) != chains:
raise ValueError(
'Expecting {} per-chain step_size specifications, '
' found {}.'.format(chains, len(self.step_size)))
for i, step_size in enumerate(self.step_size):
if step_size < 0:
raise ValueError('Argument "step_size" must be > 0, '
'chain {}, found {}.'.format(
i + 1, step_size))
if self.metric is not None:
if isinstance(self.metric, str):
if self.metric in ['diag', 'diag_e']:
self.metric_type = 'diag_e'
elif self.metric in ['dense', 'dense_e']:
self.metric_type = 'dense_e'
elif self.metric in ['unit', 'unit_e']:
self.metric_type = 'unit_e'
else:
if not os.path.exists(self.metric):
raise ValueError('no such file {}'.format(self.metric))
dims = read_metric(self.metric)
if len(dims) == 1:
self.metric_type = 'diag_e'
else:
self.metric_type = 'dense_e'
self.metric_file = self.metric
elif isinstance(self.metric, Dict):
if 'inv_metric' not in self.metric:
raise ValueError(
'Entry "inv_metric" not found in metric dict.')
dims = list(np.asarray(self.metric['inv_metric']).shape)
if len(dims) == 1:
self.metric_type = 'diag_e'
else:
self.metric_type = 'dense_e'
dict_file = create_named_text_file(dir=_TMPDIR,
prefix="metric",
suffix=".json")
write_stan_json(dict_file, self.metric)
self.metric_file = dict_file
elif isinstance(self.metric, (list, tuple)):
if len(self.metric) != chains:
raise ValueError(
'Number of metric files must match number of chains,'
' found {} metric files for {} chains.'.format(
len(self.metric), chains))
if all(isinstance(elem, dict) for elem in self.metric):
metric_files: List[str] = []
for i, metric in enumerate(self.metric):
assert isinstance(metric,
dict) # make the typechecker happy
metric_dict: Dict[str, Any] = metric
if 'inv_metric' not in metric_dict:
raise ValueError(
'Entry "inv_metric" not found in metric dict '
'for chain {}.'.format(i + 1))
if i == 0:
dims = list(
np.asarray(metric_dict['inv_metric']).shape)
else:
dims2 = list(
np.asarray(metric_dict['inv_metric']).shape)
if dims != dims2:
raise ValueError(
'Found inconsistent "inv_metric" entry '
'for chain {}: entry has dims '
'{}, expected {}.'.format(
i + 1, dims, dims2))
dict_file = create_named_text_file(dir=_TMPDIR,
prefix="metric",
suffix=".json")
write_stan_json(dict_file, metric_dict)
metric_files.append(dict_file)
if len(dims) == 1:
self.metric_type = 'diag_e'
else:
self.metric_type = 'dense_e'
self.metric_file = metric_files
elif all(isinstance(elem, str) for elem in self.metric):
metric_files = []
for i, metric in enumerate(self.metric):
assert isinstance(metric, str) # typecheck
if not os.path.exists(metric):
raise ValueError('no such file {}'.format(metric))
if i == 0:
dims = read_metric(metric)
else:
dims2 = read_metric(metric)
if len(dims) != len(dims2):
raise ValueError(
'Metrics files {}, {},'
' inconsistent metrics'.format(
self.metric[0], metric))
if dims != dims2:
raise ValueError(
'Metrics files {}, {},'
' inconsistent metrics'.format(
self.metric[0], metric))
metric_files.append(metric)
if len(dims) == 1:
self.metric_type = 'diag_e'
else:
self.metric_type = 'dense_e'
self.metric_file = metric_files
else:
raise ValueError(
'Argument "metric" must be a list of pathnames or '
'Python dicts, found list of {}.'.format(
type(self.metric[0])))
else:
raise ValueError(
'Invalid metric specified, not a recognized metric type, '
'must be either a metric type name, a filepath, dict, '
'or list of per-chain filepaths or dicts. Found '
'an object of type {}.'.format(type(self.metric)))
if self.adapt_delta is not None:
if not 0 < self.adapt_delta < 1:
raise ValueError(
'Argument "adapt_delta" must be between 0 and 1,'
' found {}'.format(self.adapt_delta))
if self.adapt_init_phase is not None:
if self.adapt_init_phase < 0 or not isinstance(
self.adapt_init_phase, int):
raise ValueError(
'Argument "adapt_init_phase" must be a non-negative '
'integer, found {}'.format(self.adapt_init_phase))
if self.adapt_metric_window is not None:
if self.adapt_metric_window < 0 or not isinstance(
self.adapt_metric_window, int):
raise ValueError(
'Argument "adapt_metric_window" must be a non-negative '
' integer, found {}'.format(self.adapt_metric_window))
if self.adapt_step_size is not None:
if self.adapt_step_size < 0 or not isinstance(
self.adapt_step_size, int):
raise ValueError(
'Argument "adapt_step_size" must be a non-negative integer,'
'found {}'.format(self.adapt_step_size))
if self.fixed_param and (
self.max_treedepth is not None or self.metric is not None
or self.step_size is not None or
not (self.adapt_delta is None and self.adapt_init_phase is None
and self.adapt_metric_window is None
and self.adapt_step_size is None)):
raise ValueError(
'When fixed_param=True, cannot specify adaptation parameters.')
def test_metric_rdump_bad_2(self):
metric_file = os.path.join(datafiles_path, 'metric_bad_2.data.R')
with self.assertRaisesRegex(Exception,
'bad or missing entry "inv_metric"'):
dims = read_metric(metric_file)
def test_metric_missing(self):
metric_file = os.path.join(datafiles_path, 'no_such_file.json')
with self.assertRaisesRegex(Exception, 'No such file or directory'):
dims = read_metric(metric_file)
def test_metric_rdump_vec(self):
metric_file = os.path.join(datafiles_path, 'metric_diag.data.R')
dims = read_metric(metric_file)
self.assertEqual(1, len(dims))
self.assertEqual(3, dims[0])
def test_metric_rdump_matrix(self):
metric_file = os.path.join(datafiles_path, 'metric_dense.data.R')
dims = read_metric(metric_file)
self.assertEqual(2, len(dims))
self.assertEqual(dims[0], dims[1])
def test_metric_missing(self):
metric_file = os.path.join(DATAFILES_PATH, 'no_such_file.json')
with self.assertRaisesRegex(Exception, 'No such file or directory'):
read_metric(metric_file)
def test_metric_rdump_bad_2(self):
metric_file = os.path.join(DATAFILES_PATH, 'metric_bad_2.data.R')
with self.assertRaisesRegex(
Exception, 'bad or missing entry "inv_metric"'
):
read_metric(metric_file)
def test_metric_json_matrix(self):
metric_file = os.path.join(DATAFILES_PATH, 'metric_dense.data.json')
dims = read_metric(metric_file)
self.assertEqual(2, len(dims))
self.assertEqual(dims[0], dims[1])
def test_metric_json_vec(self):
metric_file = os.path.join(DATAFILES_PATH, 'metric_diag.data.json')
dims = read_metric(metric_file)
self.assertEqual(1, len(dims))
self.assertEqual(3, dims[0])
def validate(self) -> None:
"""
Check arguments correctness and consistency.
* input files must exist
* output files must be in a writeable directory
* adaptation and warmup args are consistent
* if file(s) for metric are supplied, check contents.
* if no seed specified, set random seed.
* length of per-chain lists equals specified # of chains
"""
if self.model is None:
raise ValueError('no stan model specified')
if self.model.exe_file is None:
raise ValueError('stan model must be compiled first,' +
' run command compile_model("{}")'.format(
self.model.stan_file))
if not os.path.exists(self.model.exe_file):
raise ValueError('cannot access model executable "{}"'.format(
self.model.exe_file))
if self.chain_ids is not None:
for i in range(len(self.chain_ids)):
if self.chain_ids[i] < 1:
raise ValueError('invalid chain_id {}'.format(
self.chain_ids[i]))
if self.output_file is not None:
if not os.path.exists(os.path.dirname(self.output_file)):
raise ValueError('invalid path for output files: {}'.format(
self.output_file))
try:
with open(self.output_file, 'w+') as fd:
pass
os.remove(self.output_file) # cleanup
except Exception:
raise ValueError('invalid path for output files: {}'.format(
self.output_file))
if self.output_file.endswith('.csv'):
self.output_file = self.output_file[:-4]
if self.seed is None:
rng = np.random.RandomState()
self.seed = rng.randint(1, 99999 + 1)
else:
if not isinstance(self.seed, (int, list)):
raise ValueError(
'seed must be an integer between 0 and 2**32-1,'
' found {}'.format(self.seed))
elif isinstance(self.seed, int):
if self.seed < 0 or self.seed > 2**32 - 1:
raise ValueError(
'seed must be an integer between 0 and 2**32-1,'
' found {}'.format(self.seed))
else:
if len(self.seed) != len(self.chain_ids):
raise ValueError(
'number of seeds must match number of chains '
' found {} seed for {} chains '.format(
len(self.seed), len(self.chain_ids)))
for i in range(len(self.seed)):
if self.seed[i] < 0 or self.seed[i] > 2**32 - 1:
raise ValueError('seed must be an integer value'
' between 0 and 2**32-1,'
' found {}'.format(self.seed[i]))
if self.data is not None:
if not os.path.exists(self.data):
raise ValueError('no such file {}'.format(self.data))
if self.inits is not None:
if isinstance(self.inits, (int, float)):
if self.inits < 0:
raise ValueError('inits must be > 0, found {}'.format(
self.inits))
elif isinstance(self.inits, str):
if not os.path.exists(self.inits):
raise ValueError('no such file {}'.format(self.inits))
elif isinstance(self.inits, List):
if len(self.inits) != len(self.chain_ids):
raise ValueError(
'number of inits files must match number of chains '
' found {} inits files for {} chains '.format(
len(self.inits), len(self.chain_ids)))
names_set = set(self.inits)
if len(names_set) != len(self.inits):
raise ValueError('each chain must have its own init file,'
' found duplicates in inits files list.')
for i in range(len(self.inits)):
if not os.path.exists(self.inits[i]):
raise ValueError('no such file {}'.format(
self.inits[i]))
if self.warmup_iters is not None:
if self.warmup_iters < 0:
raise ValueError(
'warmup_iters must be a non-negative integer'.format(
self.warmup_iters))
if self.adapt_engaged and self.warmup_iters == 0:
raise ValueError(
'adaptation requested but 0 warmup iterations specified, '
'must run warmup iterations')
if self.sampling_iters is not None:
if self.sampling_iters < 0:
raise ValueError(
'sampling_iters must be a non-negative integer'.format(
self.sampling_iters))
if self.warmup_schedule is not None:
if self.warmup_iters is not None and self.warmup_iters < 1:
raise ValueError(
'Config error: '
'warmup_schedule specified for 0 warmup iterations')
if len(self.warmup_schedule) != 3 or sum(self.warmup_schedule) > 1:
raise ValueError(
'warmup_schedule should be triple of precentages '
' that sums to 1, e.g. (0.1, 0.8, 0.1), found {}'.format(
self.warmup_iters))
for x in self.warmup_schedule:
if x < 0 or x > 1:
raise ValueError(
'warmup_schedule should be triple of precentages that'
' sum to 1, e.g. (0.1, 0.8, 0.1), found {}'.format(
self.warmup_schedule))
num_warmup = 1000
if self.warmup_iters is not None:
num_warmup = self.warmup_iters
self.init_buffer = math.floor(num_warmup * self.warmup_schedule[0])
self.term_buffer = math.floor(num_warmup * self.warmup_schedule[2])
if self.thin is not None:
if self.thin < 1:
raise ValueError('thin must be at least 1, found {}'.format(
self.thin))
if self.max_treedepth is not None:
if self.max_treedepth < 1:
raise ValueError(
'max_treedepth must be at least 1, found {}'.format(
self.max_treedepth))
if self.step_size is not None:
if isinstance(self.step_size, (int, float)):
if self.step_size < 0:
raise ValueError('step_size must be > 0, found {}'.format(
self.step_size))
else:
if len(self.step_size) != len(self.chain_ids):
raise ValueError(
'number of step_sizes must match number of chains '
' found {} step_sizes for {} chains '.format(
len(self.step_size), len(self.chain_ids)))
for i in range(len(self.step_size)):
if self.step_size[i] < 0:
raise ValueError(
'step_size must be > 0, found {}'.format(
self.step_size[i]))
if self.metric is not None:
dims = None
if isinstance(self.metric, str):
if self.metric in ['diag', 'diag_e']:
self.metric = 'diag_e'
elif self.metric in ['dense', 'dense_e']:
self.metric = 'dense_e'
else:
if not os.path.exists(self.metric):
raise ValueError('no such file {}'.format(self.metric))
dims = read_metric(self.metric)
elif isinstance(self.metric, list):
if len(self.metric) != len(self.chain_ids):
raise ValueError(
'number of metric files must match number of chains '
' found {} metric files for {} chains '.format(
len(self.metric), len(self.chain_ids)))
names_set = set(self.metric)
if len(names_set) != len(self.metric):
raise ValueError(
'each chain must have its own metric file,'
' found duplicates in metric files list.')
for i in range(len(self.metric)):
if not os.path.exists(self.metric[i]):
raise ValueError('no such file {}'.format(
self.metric[i]))
if i == 0:
dims = read_metric(self.metric[i])
else:
dims2 = read_metric(self.metric[i])
if len(dims) != len(dims2):
raise ValueError('metrics files {}, {},'
' inconsistent metrics'.format(
self.metric[0],
self.metric[i]))
for j in range(len(dims)):
if dims[j] != dims2[j]:
raise ValueError(
'metrics files {}, {},'
' inconsistent metrics'.format(
self.metric[0], self.metric[i]))
if dims is not None:
if len(dims) > 2 or (len(dims) == 2 and dims[0] != dims[1]):
raise ValueError('bad metric specifiation')
self.metric_file = self.metric
if len(dims) == 1:
self.metric = 'diag_e'
elif len(dims) == 2:
self.metric = 'dense_e'
if self.adapt_delta is not None:
if self.adapt_delta < 0.0 or self.adapt_delta > 1.0:
raise ValueError('adapt_delta must be between 0 and 1,'
' found {}'.format(self.adapt_delta))
pass
