def run(
self,
output_prefix,
n_iter,
obs=None,
save_params_every=50,
write_every=50,
step_size=1,
show_progress=True,
):
"""
Executes thetime evolution for `n_iter` steps of `dt` and writing values of
the observables `obs` to the output. The output are JSON file at
`output_prefix.{log,state}`, overwriting files with the same prefix.
Args:
:output_prefix: The prefix at which JSON output should be stored.
:n_iter: the total number of iterations
:obs: An iterable containing all observables that should be computed
:save_params_every: Every how many steps the parameters of the network should be
serialized to disk (ignored if logger is provided)
:write_every: Every how many steps the json data should be flushed to disk (ignored if
logger is provided)
:step_size: Every how many steps should observables be logged to disk (default=1)
:show_progress: If true displays a progress bar (default=True)
"""
if obs is None:
obs = {}
logger = _JsonLog(output_prefix, save_params_every, write_every)
# Don't log on non-root nodes
if self._mpi_nodes != 0:
logger = None
with tqdm(
self.iter(n_iter, step_size), total=n_iter, disable=not show_progress
) as itr:
for step in itr:
# if the cost-function is defined then report it in the progress bar
energy = self.estimate(self._h(self.t))
itr.set_postfix_str(
"t={:.2e}, Energy={:.6e}".format(self.t, energy.mean.real)
)
obs_data = self.estimate(obs)
obs_data["Energy"] = energy
log_data = {}
if self._loss_stats is not None:
obs_data[self._loss_name] = self._loss_stats
log_data["Time"] = self.t
if logger is not None:
logger(step, log_data, _MockMachine(self.state))
# flush at the end of the evolution so that final values are saved to
# file
if logger is not None:
logger.flush(_MockMachine(self.state))
def run(
self,
output_prefix,
n_iter,
obs=None,
save_params_every=50,
write_every=50,
step_size=1,
show_progress=True,
):
"""
Executes the Monte Carlo Variational optimization, updating the weights of the network
stored in this driver for `n_iter` steps and dumping values of the observables `obs`
in the output. The output is a json file at `output_prefix`, overwriting files with
the same prefix.
Args:
:output_prefix: The prefix at which json output should be stored (ignored if logger
is provided).
:n_iter: the total number of iterations
:obs: An iterable containing all observables that should be computed
:save_params_every: Every how many steps the parameters of the network should be
serialized to disk (ignored if logger is provided)
:write_every: Every how many steps the json data should be flushed to disk (ignored if
logger is provided)
:step_size: Every how many steps should observables be logged to disk (default=1)
:show_progress: If true displays a progress bar (default=True)
"""
if obs is None:
# TODO remove the first case after deprecation of self._obs in 3.0
if len(self._obs) != 0:
obs = self._obs
else:
obs = {}
logger = _JsonLog(output_prefix, save_params_every, write_every)
# Don't log on non-root nodes
if self._mynode != 0:
logger = None
with tqdm(
self.iter(n_iter, step_size), total=n_iter, disable=not show_progress
) as itr:
for step in itr:
# if the cost-function is defined then report it in the progress bar
if self._loss_stats is not None:
itr.set_postfix_str(self._loss_name + "=" + str(self._loss_stats))
obs_data = self.estimate(obs)
if self._loss_stats is not None:
obs_data[self._loss_name] = self._loss_stats
log_data = tree_map(_obs_stat_to_dict, obs_data)
if logger is not None:
logger(step, log_data, self.machine)
# flush at the end of the evolution so that final values are saved to
# file
if logger is not None:
logger.flush(self.machine)
def run(
self,
n_iter,
out=None,
obs=None,
show_progress=True,
save_params_every=50, # for default logger
write_every=50, # for default logger
step_size=1, # for default logger
callback=lambda *x: True,
):
"""
Executes the Monte Carlo Variational optimization, updating the weights of the network
stored in this driver for `n_iter` steps and dumping values of the observables `obs`
in the output `logger`. If no logger is specified, creates a json file at `out`,
overwriting files with the same prefix.
Args:
:n_iter: the total number of iterations
:out: A logger object, or an iterable of loggers, to be used to store simulation log and data.
If this argument is a string, it will be used as output prefix for the standard JSON logger.
:obs: An iterable containing all observables that should be computed
:save_params_every: Every how many steps the parameters of the network should be
serialized to disk (ignored if logger is provided)
:write_every: Every how many steps the json data should be flushed to disk (ignored if
logger is provided)
:step_size: Every how many steps should observables be logged to disk (default=1)
:show_progress: If true displays a progress bar (default=True)
:callback: Callable or list of callable callback functions to stop training given a condition
"""
if not isinstance(n_iter, numbers.Number):
raise ValueError(
"n_iter, the first positional argument to `run`, must be a number!"
)
if obs is None:
obs = {}
if out is None:
out = tuple()
print(
"No output specified (out=[apath|nk.logging.JsonLogger(...)])."
"Running the optimization but not saving the output."
)
# Log only non-root nodes
if self._mynode == 0:
# if out is a path, create an overwriting Json Log for output
if isinstance(out, str):
loggers = (_JsonLog(out, "w", save_params_every, write_every),)
else:
loggers = _to_iterable(out)
else:
loggers = tuple()
show_progress = False
callbacks = _to_iterable(callback)
callback_stop = False
with tqdm(
self.iter(n_iter, step_size), total=n_iter, disable=not show_progress
) as itr:
for step in itr:
log_data = self.estimate(obs)
# if the cost-function is defined then report it in the progress bar
if self._loss_stats is not None:
itr.set_postfix_str(self._loss_name + "=" + str(self._loss_stats))
log_data[self._loss_name] = self._loss_stats
for logger in loggers:
logger(self.step_count, log_data, self.machine)
for callback in callbacks:
if not callback(step, log_data, self):
callback_stop = True
if callback_stop:
break
# flush at the end of the evolution so that final values are saved to
# file
for logger in loggers:
logger.flush(self.machine)
def run(
self,
n_iter,
out=None,
obs=None,
show_progress=True,
save_params_every=50, # for default logger
write_every=50, # for default logger
step_size=1, # for default logger
output_prefix=None, # TODO: deprecated
):
"""
Executes the Monte Carlo Variational optimization, updating the weights of the network
stored in this driver for `n_iter` steps and dumping values of the observables `obs`
in the output `logger`. If no logger is specified, creates a json file at `output_prefix`,
overwriting files with the same prefix.
!! Compatibility v2.1
Before v2.1 the order of the first two arguments, `n_iter` and `output_prefix` was
reversed. The reversed ordering will still be supported until v3.0, but is deprecated.
Args:
:n_iter: the total number of iterations
:out: A logger object to be used to store simulation log and data.
If this argument is a string, it will be used as output prefix for the standard JSON logger.
:obs: An iterable containing all observables that should be computed
:save_params_every: Every how many steps the parameters of the network should be
serialized to disk (ignored if logger is provided)
:write_every: Every how many steps the json data should be flushed to disk (ignored if
logger is provided)
:step_size: Every how many steps should observables be logged to disk (default=1)
:show_progress: If true displays a progress bar (default=True)
:output_prefix: (Deprecated) The prefix at which json output should be stored (ignored if out
is provided).
"""
# TODO Remove this deprecated code in v3.0
# manage deprecated where argument names are not specified, and
# prefix is passed as the first positional argument and the number
# of iterations as a second argument.
if type(n_iter) is str and type(out) is int:
n_iter, out = out, n_iter
warn_deprecation(
"The positional syntax run(output_prefix, n_iter, **args) is deprecated, use run(n_iter, output_prefix, **args) instead."
)
if obs is None:
# TODO remove the first case after deprecation of self._obs in 3.0
if len(self._obs) != 0:
obs = self._obs
else:
obs = {}
# output_prefix is deprecated. out should be used and takes over
# error out if both are passed
# TODO: remove in v3.0
if out is not None and output_prefix is not None:
raise ValueError(
"Invalid out and output_prefix arguments. Only one of the two can be passed. Note that output_prefix is deprecated and you should use out."
)
elif out is None:
warn_deprecation(
"The output_prefix argument is deprecated. Use out instead.")
out = output_prefix
# Log only non-root nodes
if self._mynode == 0:
# if out is a path, create an overwriting Json Log for output
if isinstance(out, str):
logger = _JsonLog(out, "w", save_params_every, write_every)
else:
logger = out
else:
logger = None
with tqdm(self.iter(n_iter, step_size),
total=n_iter,
disable=not show_progress) as itr:
for step in itr:
# if the cost-function is defined then report it in the progress bar
if self._loss_stats is not None:
itr.set_postfix_str(self._loss_name + "=" +
str(self._loss_stats))
obs_data = self.estimate(obs)
if self._loss_stats is not None:
obs_data[self._loss_name] = self._loss_stats
log_data = tree_map(_obs_stat_to_dict, obs_data)
if logger is not None:
logger(step, log_data, self.machine)
# flush at the end of the evolution so that final values are saved to
# file
if logger is not None:
logger.flush(self.machine)
def run(
self,
n_iter,
out=None,
obs=None,
show_progress=True,
save_params_every=50, # for default logger
write_every=50, # for default logger
step_size=1, # for default logger
):
"""
Executes the Monte Carlo Variational optimization, updating the weights of the network
stored in this driver for `n_iter` steps and dumping values of the observables `obs`
in the output `logger`. If no logger is specified, creates a json file at `out`,
overwriting files with the same prefix.
!! Compatibility v2.1
Before v2.1 the order of the first two arguments, `n_iter` and `out` was
reversed. The reversed ordering will still be supported until v3.0, but is deprecated.
Args:
:n_iter: the total number of iterations
:out: A logger object, or an iterable of loggers, to be used to store simulation log and data.
If this argument is a string, it will be used as output prefix for the standard JSON logger.
:obs: An iterable containing all observables that should be computed
:save_params_every: Every how many steps the parameters of the network should be
serialized to disk (ignored if logger is provided)
:write_every: Every how many steps the json data should be flushed to disk (ignored if
logger is provided)
:step_size: Every how many steps should observables be logged to disk (default=1)
:show_progress: If true displays a progress bar (default=True)
"""
# TODO Remove this deprecated code in v3.0
# manage deprecated where argument names are not specified, and
# prefix is passed as the first positional argument and the number
# of iterations as a second argument.
if type(n_iter) is str and type(out) is int:
n_iter, out = out, n_iter
warn_deprecation(
"The positional syntax run(out, n_iter, **args) is deprecated, use run(n_iter, out, **args) instead."
)
if obs is None:
# TODO remove the first case after deprecation of self._obs in 3.0
if len(self._obs) != 0:
obs = self._obs
else:
obs = {}
if out is None:
out = tuple()
print(
"No output specified (out=[apath|nk.logging.JsonLogger(...)])."
"Running the optimization but not saving the output.")
# Log only non-root nodes
if self._mynode == 0:
# if out is a path, create an overwriting Json Log for output
if isinstance(out, str):
loggers = (_JsonLog(out, "w", save_params_every,
write_every), )
elif hasattr(out, "__iter__"):
loggers = out
else:
loggers = (out, )
else:
loggers = tuple()
show_progress = False
with tqdm(self.iter(n_iter, step_size),
total=n_iter,
disable=not show_progress) as itr:
for step in itr:
log_data = self.estimate(obs)
# if the cost-function is defined then report it in the progress bar
if self._loss_stats is not None:
itr.set_postfix_str(self._loss_name + "=" +
str(self._loss_stats))
log_data[self._loss_name] = self._loss_stats
for logger in loggers:
logger(self.step_count, log_data, self.machine)
# flush at the end of the evolution so that final values are saved to
# file
for logger in loggers:
logger.flush(self.machine)
