netket.driver.VMC

class netket.driver.VMC

Bases: AbstractOptimizationDriver

Energy minimization using Variational Monte Carlo (VMC).

Note

Consider using directly the new netket.driver.VMC_SR class instead of this one if you want to use Stochastic Reconfiguration.

Inheritance

__init__(hamiltonian, optimizer, *, variational_state, preconditioner=<netket.optimizer.preconditioner.IdentityPreconditioner object>)

Initializes the driver class.

Parameters:

• hamiltonian (AbstractOperator) – The Hamiltonian of the system.

• optimizer (Any) – Determines how optimization steps are performed given the bare energy gradient.

• variational_state (VariationalState) – The variational state for which the hamiltonian must be minimised.

• preconditioner (Callable[[VariationalState, Any, Any | None], Any]) – Determines which preconditioner to use for the loss gradient. This must be a tuple of (object, solver) as documented in the section preconditioners in the documentation. The standard preconditioner included with NetKet is Stochastic Reconfiguration. By default, no preconditioner is used and the bare gradient is passed to the optimizer.

Attributes

energy

Return MCMC statistics for the expectation value of observables in the current state of the driver.

optimizer

The optimizer used to update the parameters at every iteration.

preconditioner

The preconditioner used to modify the gradient.

This is a function with the following signature

precondtioner(vstate: VariationalState,
              grad: PyTree,
              step: Optional[Scalar] = None)

Where the first argument is a variational state, the second argument is the PyTree of the gradient to precondition and the last optional argument is the step, used to change some parameters along the optimisation.

Often, this is taken to be SR(). If it is set to None, then the identity is used.

state

Returns the machine that is optimized by this driver.

step_count

Returns a monotonic integer labelling all the steps performed by this driver. This can be used, for example, to identify the line in a log file.

info: Any | None

Methods

compute_loss_and_update()

Performs a number of VMC optimization steps.

Parameters:

n_steps (int) – Number of steps to perform.

estimate(observables, fullsum=False)

Return MCMC statistics for the expectation value of observables in the current state of the driver.

Parameters:

• observables – A pytree of operators for which statistics should be computed.

• fullsum (bool)

Returns:

A pytree of the same structure as the input, containing MCMC statistics for the corresponding operators as leaves.

replace(**kwargs)

Replace the values of the fields of the object with the values of the keyword arguments. If the object is a dataclass, dataclasses.replace will be used. Otherwise, a new object will be created with the same type as the original object.

Return type:

TypeVar(P, bound= Pytree)

Parameters:

• self (P)

• kwargs (Any)

reset()

Deprecated since version 3.22: Use reset_step() to reset the sampler state at the beginning of a step. Note that the old reset() also reset step_count to 0; this behaviour is no longer supported.

reset_step(hard=False)

Resets the state of the driver at the beginning of a new step.

This method is called at the beginning of every step in the optimization.

Parameters:

• hard (bool) – If True, the reset is a hard reset, resulting in a complete resampling even if resample_fraction

• None. (is not)

run(n_iter, out=(), obs=None, step_size=1, show_progress=True, save_params_every=50, write_every=50, callback=None, timeit=False, _graceful_keyboard_interrupt=True)

Runs this variational driver, 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.

It is possible to control more specifically what quantities are logged, when to stop the optimisation, or to execute arbitrary code at every step by specifying one or more callbacks, which are passed as a list of functions to the keyword argument callback.

Callbacks are functions that follow this signature:

def callback(step, log_data, driver) -> bool:
    ...
    return True/False

If a callback returns True, the optimisation continues, otherwise it is stopped. The log_data is a dictionary that can be modified in-place to change what is logged at every step. For example, this can be used to log additional quantities such as the acceptance rate of a sampler.

Alternatively, AbstractCallback subclasses can be used to hook into more stages of the loop. To stop the optimisation early from any callback hook, raise StopRun: the driver will catch it, finalise all callbacks via their on_run_end method, and return normally without propagating the exception.

Loggers are specified as an iterable passed to the keyword argument out. If only a string is specified, this will create by default a nk.logging.JsonLog. To know about the output format check its documentation. The logger object is also returned at the end of this function so that you can inspect the results without reading the json output.

Parameters:

• n_iter (int) – the total number of iterations to be performed during this run.

• out (Iterable[AbstractLog] | None) – 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 (dict[str, AbstractObservable] | None) – An iterable containing all observables that should be computed

• step_size (int) – Every how many steps should observables be logged to disk (default=1)

• callback (Union[Callable[[int, dict, AbstractDriver], bool], AbstractCallback, None]) – Callable or list of callable callback functions to stop training given a condition

• show_progress (bool) – If true displays a progress bar (default=True)

• save_params_every (int) – Every how many steps the parameters of the network should be serialized to disk (ignored if logger is provided)

• write_every (int) – Every how many steps the json data should be flushed to disk (ignored if logger is provided)

• timeit (bool) – If True, provide timing information.

• _graceful_keyboard_interrupt (bool) – (Internal flag, defaults to True) If True, the driver will gracefully handle a KeyboardInterrupt, usually arising from doing ctrl-C, returning the current state of the simulation. If False, the KeyboardInterrupt will be raised as usual. This only has an effect when running in interactive mode.

update_parameters(dp)

Updates the parameters of the machine using the optimizer in this driver.

Parameters:

dp – the pytree containing the updates to the parameters.