Misc

OptimizerFunctions (tuple)

OptimizerFunctions(initialize, ask, tell)

Source code in evotorch/algorithms/functional/misc.py

class OptimizerFunctions(NamedTuple):
    initialize: Callable
    ask: Callable
    tell: Callable

__getnewargs__(self) special

Return self as a plain tuple. Used by copy and pickle.

Source code in evotorch/algorithms/functional/misc.py

def __getnewargs__(self):
    'Return self as a plain tuple.  Used by copy and pickle.'
    return _tuple(self)

__new__(_cls, initialize, ask, tell) special staticmethod

Create new instance of OptimizerFunctions(initialize, ask, tell)

__repr__(self) special

Return a nicely formatted representation string

Source code in evotorch/algorithms/functional/misc.py

def __repr__(self):
    'Return a nicely formatted representation string'
    return self.__class__.__name__ + repr_fmt % self

get_functional_optimizer(optimizer)

Get a tuple of optimizer-related functions, from the given optimizer name.

For example, if the given string is "adam", the returned tuple will be (adam, adam_ask, adam_tell), where adam is the function that will initialize the Adam optimizer, adam_ask is the function that will get the current search point as a tensor, and adam_tell is the function that will expect the gradient and will return the updated state of the Adam search after applying the given gradient.

In addition to "adam", the strings "clipup" and "sgd" are also supported.

If the given optimizer is a 3-element tuple, then, the three elements within the tuple are assumed to be the initialization, ask, and tell functions of a custom optimizer, and those functions are returned in the same order.

Parameters:

Name	Type	Description	Default
optimizer	Union[str, tuple]	The optimizer name as a string, or a 3-element tuple representing the functions related to the optimizer.	required

Returns:

Type	Description
tuple	A 3-element tuple in the form (optimizer, optimizer_ask, optimizer_tell), where each element is a function, the first one being responsible for initializing the optimizer and returning its first state.

Source code in evotorch/algorithms/functional/misc.py

def get_functional_optimizer(optimizer: Union[str, tuple]) -> tuple:
    """
    Get a tuple of optimizer-related functions, from the given optimizer name.

    For example, if the given string is "adam", the returned tuple will be
    `(adam, adam_ask, adam_tell)`, where
    [adam][evotorch.algorithms.functional.funcadam.adam]
    is the function that will initialize the Adam optimizer,
    [adam_ask][evotorch.algorithms.functional.funcadam.adam_ask]
    is the function that will get the current search point as a tensor, and
    [adam_tell][evotorch.algorithms.functional.funcadam.adam_tell]
    is the function that will expect the gradient and will return the updated
    state of the Adam search after applying the given gradient.

    In addition to "adam", the strings "clipup" and "sgd" are also supported.

    If the given optimizer is a 3-element tuple, then, the three elements
    within the tuple are assumed to be the initialization, ask, and tell
    functions of a custom optimizer, and those functions are returned
    in the same order.

    Args:
        optimizer: The optimizer name as a string, or a 3-element tuple
            representing the functions related to the optimizer.
    Returns:
        A 3-element tuple in the form
        `(optimizer, optimizer_ask, optimizer_tell)`, where each element
        is a function, the first one being responsible for initializing
        the optimizer and returning its first state.
    """
    from .funcadam import adam, adam_ask, adam_tell
    from .funcclipup import clipup, clipup_ask, clipup_tell
    from .funcsgd import sgd, sgd_ask, sgd_tell

    if optimizer == "adam":
        return OptimizerFunctions(initialize=adam, ask=adam_ask, tell=adam_tell)
    elif optimizer == "clipup":
        return OptimizerFunctions(initialize=clipup, ask=clipup_ask, tell=clipup_tell)
    elif optimizer in ("sgd", "sga", "momentum"):
        return OptimizerFunctions(initialize=sgd, ask=sgd_ask, tell=sgd_tell)
    elif isinstance(optimizer, str):
        raise ValueError(f"Unrecognized functional optimizer name: {optimizer}")
    elif isinstance(optimizer, Iterable):
        a, b, c = optimizer
        return OptimizerFunctions(initialize=a, ask=b, tell=c)
    else:
        raise TypeError(
            f"`get_functional_optimizer(...)` received an unrecognized argument: {repr(optimizer)}"
            f" (of type {type(optimizer)})"
        )