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This namespace contains various utility functions, classes, and type aliases.

cloning

Clonable

A base class allowing inheriting classes define how they should be cloned.

Any class inheriting from Clonable gains these behaviors: (i) A new method named .clone() becomes available; (ii) __deepcopy__ and __copy__ work as aliases for .clone(); (iii) A new method, _get_cloned_state(self, *, memo: dict) is now defined and needs to be implemented by the inheriting class.

The method _get_cloned_state(...) expects a dictionary named memo, which maps from the ids of already cloned objects to their clones. If _get_cloned_state(...) is to use deep_clone(...) or deepcopy(...) within itself, this memo dictionary can be passed to these functions. The return value of _get_cloned_state(...) is a dictionary, which will be used as the __dict__ of the newly made clone.

Source code in evotorch/tools/cloning.py
class Clonable:
    """
    A base class allowing inheriting classes define how they should be cloned.

    Any class inheriting from Clonable gains these behaviors:
    (i) A new method named `.clone()` becomes available;
    (ii) `__deepcopy__` and `__copy__` work as aliases for `.clone()`;
    (iii) A new method, `_get_cloned_state(self, *, memo: dict)` is now
    defined and needs to be implemented by the inheriting class.

    The method `_get_cloned_state(...)` expects a dictionary named `memo`,
    which maps from the ids of already cloned objects to their clones.
    If `_get_cloned_state(...)` is to use `deep_clone(...)` or `deepcopy(...)`
    within itself, this `memo` dictionary can be passed to these functions.
    The return value of `_get_cloned_state(...)` is a dictionary, which will
    be used as the `__dict__` of the newly made clone.
    """

    def _get_cloned_state(self, *, memo: dict) -> dict:
        raise NotImplementedError

    def clone(self, *, memo: Optional[dict] = None) -> "Clonable":
        """
        Get a clone of this object.

        Args:
            memo: Optionally a dictionary which maps from the ids of the
                already cloned objects to their clones. In most scenarios,
                when this method is called from outside, this can be left
                as None.
        Returns:
            The clone of the object.
        """
        if memo is None:
            memo = {}

        self_id = id(self)
        if self_id in memo:
            return memo[self_id]

        new_object = object.__new__(type(self))
        memo[id(self)] = new_object

        new_object.__dict__.update(self._get_cloned_state(memo=memo))
        return new_object

    def __copy__(self) -> "Clonable":
        return self.clone()

    def __deepcopy__(self, memo: Optional[dict]):
        if memo is None:
            memo = {}
        return self.clone(memo=memo)

clone(self, *, memo=None)

Get a clone of this object.

Parameters:

Name Type Description Default
memo Optional[dict]

Optionally a dictionary which maps from the ids of the already cloned objects to their clones. In most scenarios, when this method is called from outside, this can be left as None.

None

Returns:

Type Description
Clonable

The clone of the object.

Source code in evotorch/tools/cloning.py
def clone(self, *, memo: Optional[dict] = None) -> "Clonable":
    """
    Get a clone of this object.

    Args:
        memo: Optionally a dictionary which maps from the ids of the
            already cloned objects to their clones. In most scenarios,
            when this method is called from outside, this can be left
            as None.
    Returns:
        The clone of the object.
    """
    if memo is None:
        memo = {}

    self_id = id(self)
    if self_id in memo:
        return memo[self_id]

    new_object = object.__new__(type(self))
    memo[id(self)] = new_object

    new_object.__dict__.update(self._get_cloned_state(memo=memo))
    return new_object

ReadOnlyClonable (Clonable)

Clonability base class for read-only and/or immutable objects.

This is a base class specialized for the immutable containers of EvoTorch. These immutable containers have two behaviors for cloning: one where the read-only attribute is preserved and one where a mutable clone is created.

Upon being copied or deep-copied (using the standard Python functions), the newly made clones are also read-only. However, when copied using the clone(...) method, the newly made clone is mutable by default (unless the clone(...) method was used with preserve_read_only=True). This default behavior of the clone(...) method was inspired by the copy() method of numpy arrays (the inspiration being that the .copy() of a read-only numpy array will not be read-only anymore).

Subclasses of evotorch.immutable.ImmutableContainer inherit from ReadOnlyClonable.

Source code in evotorch/tools/cloning.py
class ReadOnlyClonable(Clonable):
    """
    Clonability base class for read-only and/or immutable objects.

    This is a base class specialized for the immutable containers of EvoTorch.
    These immutable containers have two behaviors for cloning:
    one where the read-only attribute is preserved and one where a mutable
    clone is created.

    Upon being copied or deep-copied (using the standard Python functions),
    the newly made clones are also read-only. However, when copied using the
    `clone(...)` method, the newly made clone is mutable by default
    (unless the `clone(...)` method was used with `preserve_read_only=True`).
    This default behavior of the `clone(...)` method was inspired by the
    `copy()` method of numpy arrays (the inspiration being that the `.copy()`
    of a read-only numpy array will not be read-only anymore).

    Subclasses of `evotorch.immutable.ImmutableContainer` inherit from
    `ReadOnlyClonable`.
    """

    def _get_mutable_clone(self, *, memo: dict) -> Any:
        raise NotImplementedError

    def clone(self, *, memo: Optional[dict] = None, preserve_read_only: bool = False) -> Any:
        """
        Get a clone of this read-only object.

        Args:
            memo: Optionally a dictionary which maps from the ids of the
                already cloned objects to their clones. In most scenarios,
                when this method is called from outside, this can be left
                as None.
            preserve_read_only: Whether or not to preserve the read-only
                behavior in the clone.
        Returns:
            The clone of the object.
        """
        if memo is None:
            memo = {}
        if preserve_read_only:
            return super().clone(memo=memo)
        else:
            return self._get_mutable_clone(memo=memo)

    def __copy__(self) -> Any:
        return self.clone(preserve_read_only=True)

    def __deepcopy__(self, memo: Optional[dict]) -> Any:
        if memo is None:
            memo = {}
        return self.clone(memo=memo, preserve_read_only=True)

clone(self, *, memo=None, preserve_read_only=False)

Get a clone of this read-only object.

Parameters:

Name Type Description Default
memo Optional[dict]

Optionally a dictionary which maps from the ids of the already cloned objects to their clones. In most scenarios, when this method is called from outside, this can be left as None.

None
preserve_read_only bool

Whether or not to preserve the read-only behavior in the clone.

False

Returns:

Type Description
Any

The clone of the object.

Source code in evotorch/tools/cloning.py
def clone(self, *, memo: Optional[dict] = None, preserve_read_only: bool = False) -> Any:
    """
    Get a clone of this read-only object.

    Args:
        memo: Optionally a dictionary which maps from the ids of the
            already cloned objects to their clones. In most scenarios,
            when this method is called from outside, this can be left
            as None.
        preserve_read_only: Whether or not to preserve the read-only
            behavior in the clone.
    Returns:
        The clone of the object.
    """
    if memo is None:
        memo = {}
    if preserve_read_only:
        return super().clone(memo=memo)
    else:
        return self._get_mutable_clone(memo=memo)

Serializable (Clonable)

Base class allowing the inheriting classes become Clonable and picklable.

Any class inheriting from Serializable becomes Clonable (since Serializable is a subclass of Clonable) and therefore is expected to define its own _get_cloned_state(...) (see the documentation of the class Clonable for details).

A Serializable class gains a behavior for its __getstate__. In this already defined and implemented __getstate__ method, the resulting dictionary of _get_cloned_state(...) is used as the state dictionary. Therefore, for Serializable objects, the behavior defined in their _get_cloned_state(...) methods affect how they are pickled.

Classes inheriting from Serializable are evotorch.Problem, evotorch.Solution, evotorch.SolutionBatch, and evotorch.distributions.Distribution. In their _get_cloned_state(...) implementations, these classes use deep_clone(...) on themselves to make sure that their contained PyTorch tensors are copied using the .clone() method, ensuring that those tensors are detached from their old storages during the cloning operation. Thanks to being Serializable, their contained tensors are detached from their old storages both at the moment of copying/cloning AND at the moment of pickling.

Source code in evotorch/tools/cloning.py
class Serializable(Clonable):
    """
    Base class allowing the inheriting classes become Clonable and picklable.

    Any class inheriting from `Serializable` becomes `Clonable` (since
    `Serializable` is a subclass of `Clonable`) and therefore is expected to
    define its own `_get_cloned_state(...)` (see the documentation of the
    class `Clonable` for details).

    A `Serializable` class gains a behavior for its `__getstate__`. In this
    already defined and implemented `__getstate__` method, the resulting
    dictionary of `_get_cloned_state(...)` is used as the state dictionary.
    Therefore, for `Serializable` objects, the behavior defined in their
    `_get_cloned_state(...)` methods affect how they are pickled.

    Classes inheriting from `Serializable` are `evotorch.Problem`,
    `evotorch.Solution`, `evotorch.SolutionBatch`, and
    `evotorch.distributions.Distribution`. In their `_get_cloned_state(...)`
    implementations, these classes use `deep_clone(...)` on themselves to make
    sure that their contained PyTorch tensors are copied using the `.clone()`
    method, ensuring that those tensors are detached from their old storages
    during the cloning operation. Thanks to being `Serializable`, their
    contained tensors are detached from their old storages both at the moment
    of copying/cloning AND at the moment of pickling.
    """

    def __getstate__(self) -> dict:
        memo = {id(self): self}
        return self._get_cloned_state(memo=memo)

deep_clone(x, *, otherwise_deepcopy=False, otherwise_return=False, otherwise_fail=False, memo=None)

A recursive cloning function similar to the standard deepcopy.

The difference between deep_clone(...) and deepcopy(...) is that deep_clone(...), while recursively traversing, will run the .clone() method on the PyTorch tensors it encounters, so that the cloned tensors are forcefully detached from their storages (instead of cloning those storages as well).

At the moment of writing this documentation, the current behavior of PyTorch tensors upon being deep-copied is to clone themselves AND their storages. Therefore, if a PyTorch tensor is a slice of a large tensor (which has a large storage), then the large storage will also be deep-copied, and the newly made clone of the tensor will point to a newly made large storage. One might instead prefer to clone tensors in such a way that the newly made tensor points to a newly made storage that contains just enough data for the tensor (with the unused data being dropped). When such a behavior is desired, one can use this deep_clone(...) function.

Upon encountering a read-only and/or immutable data, this function will NOT modify the read-only behavior. For example, the deep-clone of a ReadOnlyTensor is still a ReadOnlyTensor, and the deep-clone of a read-only numpy array is still a read-only numpy array. Note that this behavior is different than the clone() method of a ReadOnlyTensor and the copy() method of a numpy array. The reason for this protective behavior is that since this is a deep-cloning operation, the encountered tensors and/or arrays might be the components of the root object, and changing their read-only attributes might affect the integrity of this root object.

The deep_clone(...) function needs to know what to do when an object of unrecognized type is encountered. Therefore, the user is expected to set one of these arguments as True (and leave the others as False): otherwise_deepcopy, otherwise_return, otherwise_fail.

Parameters:

Name Type Description Default
x Any

The object which will be deep-cloned. This object can be a standard Python container (i.e. list, tuple, dict, set), an instance of Problem, Solution, SolutionBatch, ObjectArray, ImmutableContainer, Clonable, and also any other type of object if either the argument otherwise_deepcopy or the argument otherwise_return is set as True.

required
otherwise_deepcopy bool

Setting this as True means that, when an unrecognized object is encountered, that object will be deep-copied. To handle shared and cyclic-referencing objects, the deep_clone(...) function stores its own memo dictionary. When the control is given to the standard deepcopy(...) function, the memo dictionary of deep_clone(...) will be passed to deepcopy.

False
otherwise_return bool

Setting this as True means that, when an unrecognized object is encountered, that object itself will be returned (i.e. will be a part of the created clone).

False
otherwise_fail bool

Setting this as True means that, when an unrecognized object is encountered, a TypeError will be raised.

False
memo Optional[dict]

Optionally a dictionary. In most scenarios, when this function is called from outside, this is expected to be left as None.

None

Returns:

Type Description
Any

The newly made clone of the original object.

Source code in evotorch/tools/cloning.py
def deep_clone(  # noqa: C901
    x: Any,
    *,
    otherwise_deepcopy: bool = False,
    otherwise_return: bool = False,
    otherwise_fail: bool = False,
    memo: Optional[dict] = None,
) -> Any:
    """
    A recursive cloning function similar to the standard `deepcopy`.

    The difference between `deep_clone(...)` and `deepcopy(...)` is that
    `deep_clone(...)`, while recursively traversing, will run the `.clone()`
    method on the PyTorch tensors it encounters, so that the cloned tensors
    are forcefully detached from their storages (instead of cloning those
    storages as well).

    At the moment of writing this documentation, the current behavior of
    PyTorch tensors upon being deep-copied is to clone themselves AND their
    storages. Therefore, if a PyTorch tensor is a slice of a large tensor
    (which has a large storage), then the large storage will also be
    deep-copied, and the newly made clone of the tensor will point to a newly
    made large storage. One might instead prefer to clone tensors in such a
    way that the newly made tensor points to a newly made storage that
    contains just enough data for the tensor (with the unused data being
    dropped). When such a behavior is desired, one can use this
    `deep_clone(...)` function.

    Upon encountering a read-only and/or immutable data, this function will
    NOT modify the read-only behavior. For example, the deep-clone of a
    ReadOnlyTensor is still a ReadOnlyTensor, and the deep-clone of a
    read-only numpy array is still a read-only numpy array. Note that this
    behavior is different than the `clone()` method of a ReadOnlyTensor
    and the `copy()` method of a numpy array. The reason for this
    protective behavior is that since this is a deep-cloning operation,
    the encountered tensors and/or arrays might be the components of the root
    object, and changing their read-only attributes might affect the integrity
    of this root object.

    The `deep_clone(...)` function needs to know what to do when an object
    of unrecognized type is encountered. Therefore, the user is expected to
    set one of these arguments as True (and leave the others as False):
    `otherwise_deepcopy`, `otherwise_return`, `otherwise_fail`.

    Args:
        x: The object which will be deep-cloned. This object can be a standard
            Python container (i.e. list, tuple, dict, set), an instance of
            Problem, Solution, SolutionBatch, ObjectArray, ImmutableContainer,
            Clonable, and also any other type of object if either the argument
            `otherwise_deepcopy` or the argument `otherwise_return` is set as
            True.
        otherwise_deepcopy: Setting this as True means that, when an
            unrecognized object is encountered, that object will be
            deep-copied. To handle shared and cyclic-referencing objects,
            the `deep_clone(...)` function stores its own memo dictionary.
            When the control is given to the standard `deepcopy(...)`
            function, the memo dictionary of `deep_clone(...)` will be passed
            to `deepcopy`.
        otherwise_return: Setting this as True means that, when an
            unrecognized object is encountered, that object itself will be
            returned (i.e. will be a part of the created clone).
        otherwise_fail: Setting this as True means that, when an unrecognized
            object is encountered, a TypeError will be raised.
        memo: Optionally a dictionary. In most scenarios, when this function
            is called from outside, this is expected to be left as None.
    Returns:
        The newly made clone of the original object.
    """
    from .objectarray import ObjectArray
    from .readonlytensor import ReadOnlyTensor

    if memo is None:
        # If a memo dictionary was not given, make a new one now.
        memo = {}

    # Get the id of the object being cloned.
    x_id = id(x)

    if x_id in memo:
        # If the id of the object being cloned is already in the memo dictionary, then this object was previously
        # cloned. We just return that clone.
        return memo[x_id]

    # Count how many of the arguments `otherwise_deepcopy`, `otherwise_return`, and `otherwise_fail` was set as True.
    # In this context, we call these arguments as fallback behaviors.
    fallback_behaviors = (otherwise_deepcopy, otherwise_return, otherwise_fail)
    enabled_behavior_count = sum(1 for behavior in fallback_behaviors if behavior)

    if enabled_behavior_count == 0:
        # If none of the fallback behaviors was enabled, then we raise an error.
        raise ValueError(
            "The action to take with objects of unrecognized types is not known because"
            " none of these arguments was set as True: `otherwise_deepcopy`, `otherwise_return`, `otherwise_fail`."
            " Please set one of these arguments as True."
        )
    elif enabled_behavior_count == 1:
        # If one of the fallback behaviors was enabled, then we received our expected input. We do nothing here.
        pass
    else:
        # If the number of enabled fallback behaviors is an unexpected value. then we raise an error.
        raise ValueError(
            f"The following arguments were received, which is conflicting: otherwise_deepcopy={otherwise_deepcopy},"
            f" otherwise_return={otherwise_return}, otherwise_fail={otherwise_fail}."
            f" Please set exactly one of these arguments as True and leave the others as False."
        )

    # This inner function specifies how the deep_clone function should call itself.
    def call_self(obj: Any) -> Any:
        return deep_clone(
            obj,
            otherwise_deepcopy=otherwise_deepcopy,
            otherwise_return=otherwise_return,
            otherwise_fail=otherwise_fail,
            memo=memo,
        )

    # Below, we handle the cloning behaviors case by case.
    if (x is None) or (x is NotImplemented) or (x is Ellipsis):
        result = deepcopy(x)
    elif isinstance(x, (Number, str, bytes, bytearray)):
        result = deepcopy(x, memo=memo)
    elif isinstance(x, np.ndarray):
        result = x.copy()
        result.flags["WRITEABLE"] = x.flags["WRITEABLE"]
    elif isinstance(x, (ObjectArray, ReadOnlyClonable)):
        result = x.clone(preserve_read_only=True, memo=memo)
    elif isinstance(x, ReadOnlyTensor):
        result = x.clone(preserve_read_only=True)
    elif isinstance(x, torch.Tensor):
        result = x.clone()
    elif isinstance(x, Clonable):
        result = x.clone(memo=memo)
    elif isinstance(x, (dict, OrderedDict)):
        result = type(x)()
        memo[x_id] = result
        for k, v in x.items():
            result[call_self(k)] = call_self(v)
    elif isinstance(x, list):
        result = type(x)()
        memo[x_id] = result
        for item in x:
            result.append(call_self(item))
    elif isinstance(x, set):
        result = type(x)()
        memo[x_id] = result
        for item in x:
            result.add(call_self(item))
    elif isinstance(x, tuple):
        result = []
        memo[x_id] = result
        for item in x:
            result.append(call_self(item))
        if hasattr(x, "_fields"):
            result = type(x)(*result)
        else:
            result = type(x)(result)
        memo[x_id] = result
    else:
        # If the object is not recognized, we use the fallback behavior.
        if otherwise_deepcopy:
            result = deepcopy(x, memo=memo)
        elif otherwise_return:
            result = x
        elif otherwise_fail:
            raise TypeError(f"Do not know how to clone {repr(x)} (of type {type(x)}).")
        else:
            raise RuntimeError("The function `deep_clone` reached an unexpected state. This might be a bug.")

    if (x_id not in memo) and (result is not x):
        # If the newly made clone is still not in the memo dictionary AND the "clone" is not just a reference to the
        # original object, we make sure that it is in the memo dictionary.
        memo[x_id] = result

    # Finally, the result is returned.
    return result

hook

This module contains the Hook class, which is used for event handling, and for defining additional behaviors to the class instances which own the Hook.

Hook (MutableSequence)

A Hook stores a list of callable objects to be called for handling certain events. A Hook itself is callable, which invokes the callables stored in its list. If the callables stored by the Hook return list-like objects or dict-like objects, their returned results are accumulated, and then those accumulated results are finally returned by the Hook.

Source code in evotorch/tools/hook.py
class Hook(MutableSequence):
    """
    A Hook stores a list of callable objects to be called for handling
    certain events. A Hook itself is callable, which invokes the callables
    stored in its list. If the callables stored by the Hook return list-like
    objects or dict-like objects, their returned results are accumulated,
    and then those accumulated results are finally returned by the Hook.
    """

    def __init__(
        self,
        callables: Optional[Iterable[Callable]] = None,
        *,
        args: Optional[Iterable] = None,
        kwargs: Optional[Mapping] = None,
    ):
        """
        Initialize the Hook.

        Args:
            callables: A sequence of callables to be stored by the Hook.
            args: Positional arguments which, when the Hook is called,
                are to be passed to every callable stored by the Hook.
                Please note that these positional arguments will be passed
                as the leftmost arguments, and, the other positional
                arguments passed via the `__call__(...)` method of the
                Hook will be added to the right of these arguments.
            kwargs: Keyword arguments which, when the Hook is called,
                are to be passed to every callable stored by the Hook.
                Please note that these keyword arguments could be overriden
                by the keyword arguments passed via the `__call__(...)`
                method of the Hook.
        """
        self._funcs: list = [] if callables is None else list(callables)
        self._args: list = [] if args is None else list(args)
        self._kwargs: dict = {} if kwargs is None else dict(kwargs)

    def __call__(self, *args: Any, **kwargs: Any) -> Optional[Union[dict, list]]:
        """
        Call every callable object stored by the Hook.
        The results of the stored callable objects (which can be dict-like
        or list-like objects) are accumulated and finally returned.

        Args:
            args: Additional positional arguments to be passed to the stored
                callables.
            kwargs: Additional keyword arguments to be passed to the stored
                keyword arguments.
        """

        all_args = []
        all_args.extend(self._args)
        all_args.extend(args)

        all_kwargs = {}
        all_kwargs.update(self._kwargs)
        all_kwargs.update(kwargs)

        result: Optional[Union[dict, list]] = None

        for f in self._funcs:
            tmp = f(*all_args, **all_kwargs)
            if tmp is not None:
                if isinstance(tmp, Mapping):
                    if result is None:
                        result = dict(tmp)
                    elif isinstance(result, list):
                        raise TypeError(
                            f"The function {f} returned a dict-like object."
                            f" However, previous function(s) in this hook had returned list-like object(s)."
                            f" Such incompatible results cannot be accumulated."
                        )
                    elif isinstance(result, dict):
                        result.update(tmp)
                    else:
                        raise RuntimeError
                elif isinstance(tmp, Iterable):
                    if result is None:
                        result = list(tmp)
                    elif isinstance(result, list):
                        result.extend(tmp)
                    elif isinstance(result, dict):
                        raise TypeError(
                            f"The function {f} returned a list-like object."
                            f" However, previous function(s) in this hook had returned dict-like object(s)."
                            f" Such incompatible results cannot be accumulated."
                        )
                    else:
                        raise RuntimeError
                else:
                    raise TypeError(
                        f"Expected the function {f} to return None, or a dict-like object, or a list-like object."
                        f" However, the function returned an object of type {repr(type(tmp))}."
                    )

        return result

    def accumulate_dict(self, *args: Any, **kwargs: Any) -> Optional[Union[dict, list]]:
        result = self(*args, **kwargs)
        if result is None:
            return {}
        elif isinstance(result, Mapping):
            return result
        else:
            raise TypeError(
                f"Expected the functions in this hook to accumulate"
                f" dictionary-like objects. Instead, accumulated"
                f" an object of type {type(result)}."
                f" Hint: are the functions registered in this hook"
                f" returning non-dictionary iterables?"
            )

    def accumulate_sequence(self, *args: Any, **kwargs: Any) -> Optional[Union[dict, list]]:
        result = self(*args, **kwargs)
        if result is None:
            return []
        elif isinstance(result, Mapping):
            raise TypeError(
                f"Expected the functions in this hook to accumulate"
                f" sequences (that are NOT dictionaries). Instead, accumulated"
                f" a dict-like object of type {type(result)}."
                f" Hint: are the functions registered in this hook"
                f" returning objects with Mapping interface?"
            )
        else:
            return result

    def _to_string(self) -> str:
        init_args = [repr(self._funcs)]

        if len(self._args) > 0:
            init_args.append(f"args={self._args}")

        if len(self._kwargs) > 0:
            init_args.append(f"kwargs={self._kwargs}")

        s_init_args = ", ".join(init_args)

        return f"{type(self).__name__}({s_init_args})"

    def __repr__(self) -> str:
        return self._to_string()

    def __str__(self) -> str:
        return self._to_string()

    def __getitem__(self, i: Union[int, slice]) -> Union[Callable, "Hook"]:
        if isinstance(i, slice):
            return Hook(self._funcs[i], args=self._args, kwargs=self._kwargs)
        else:
            return self._funcs[i]

    def __setitem__(self, i: Union[int, slice], x: Iterable[Callable]):
        self._funcs[i] = x

    def __delitem__(self, i: Union[int, slice]):
        del self._funcs[i]

    def insert(self, i: int, x: Callable):
        self._funcs.insert(i, x)

    def __len__(self) -> int:
        return len(self._funcs)

    @property
    def args(self) -> list:
        """Positional arguments that will be passed to the stored callables"""
        return self._args

    @property
    def kwargs(self) -> dict:
        """Keyword arguments that will be passed to the stored callables"""
        return self._kwargs

args: list property readonly

Positional arguments that will be passed to the stored callables

kwargs: dict property readonly

Keyword arguments that will be passed to the stored callables

__call__(self, *args, **kwargs) special

Call every callable object stored by the Hook. The results of the stored callable objects (which can be dict-like or list-like objects) are accumulated and finally returned.

Parameters:

Name Type Description Default
args Any

Additional positional arguments to be passed to the stored callables.

()
kwargs Any

Additional keyword arguments to be passed to the stored keyword arguments.

{}
Source code in evotorch/tools/hook.py
def __call__(self, *args: Any, **kwargs: Any) -> Optional[Union[dict, list]]:
    """
    Call every callable object stored by the Hook.
    The results of the stored callable objects (which can be dict-like
    or list-like objects) are accumulated and finally returned.

    Args:
        args: Additional positional arguments to be passed to the stored
            callables.
        kwargs: Additional keyword arguments to be passed to the stored
            keyword arguments.
    """

    all_args = []
    all_args.extend(self._args)
    all_args.extend(args)

    all_kwargs = {}
    all_kwargs.update(self._kwargs)
    all_kwargs.update(kwargs)

    result: Optional[Union[dict, list]] = None

    for f in self._funcs:
        tmp = f(*all_args, **all_kwargs)
        if tmp is not None:
            if isinstance(tmp, Mapping):
                if result is None:
                    result = dict(tmp)
                elif isinstance(result, list):
                    raise TypeError(
                        f"The function {f} returned a dict-like object."
                        f" However, previous function(s) in this hook had returned list-like object(s)."
                        f" Such incompatible results cannot be accumulated."
                    )
                elif isinstance(result, dict):
                    result.update(tmp)
                else:
                    raise RuntimeError
            elif isinstance(tmp, Iterable):
                if result is None:
                    result = list(tmp)
                elif isinstance(result, list):
                    result.extend(tmp)
                elif isinstance(result, dict):
                    raise TypeError(
                        f"The function {f} returned a list-like object."
                        f" However, previous function(s) in this hook had returned dict-like object(s)."
                        f" Such incompatible results cannot be accumulated."
                    )
                else:
                    raise RuntimeError
            else:
                raise TypeError(
                    f"Expected the function {f} to return None, or a dict-like object, or a list-like object."
                    f" However, the function returned an object of type {repr(type(tmp))}."
                )

    return result

__init__(self, callables=None, *, args=None, kwargs=None) special

Initialize the Hook.

Parameters:

Name Type Description Default
callables Optional[Iterable[Callable]]

A sequence of callables to be stored by the Hook.

None
args Optional[Iterable]

Positional arguments which, when the Hook is called, are to be passed to every callable stored by the Hook. Please note that these positional arguments will be passed as the leftmost arguments, and, the other positional arguments passed via the __call__(...) method of the Hook will be added to the right of these arguments.

None
kwargs Optional[collections.abc.Mapping]

Keyword arguments which, when the Hook is called, are to be passed to every callable stored by the Hook. Please note that these keyword arguments could be overriden by the keyword arguments passed via the __call__(...) method of the Hook.

None
Source code in evotorch/tools/hook.py
def __init__(
    self,
    callables: Optional[Iterable[Callable]] = None,
    *,
    args: Optional[Iterable] = None,
    kwargs: Optional[Mapping] = None,
):
    """
    Initialize the Hook.

    Args:
        callables: A sequence of callables to be stored by the Hook.
        args: Positional arguments which, when the Hook is called,
            are to be passed to every callable stored by the Hook.
            Please note that these positional arguments will be passed
            as the leftmost arguments, and, the other positional
            arguments passed via the `__call__(...)` method of the
            Hook will be added to the right of these arguments.
        kwargs: Keyword arguments which, when the Hook is called,
            are to be passed to every callable stored by the Hook.
            Please note that these keyword arguments could be overriden
            by the keyword arguments passed via the `__call__(...)`
            method of the Hook.
    """
    self._funcs: list = [] if callables is None else list(callables)
    self._args: list = [] if args is None else list(args)
    self._kwargs: dict = {} if kwargs is None else dict(kwargs)

insert(self, i, x)

S.insert(index, value) -- insert value before index

Source code in evotorch/tools/hook.py
def insert(self, i: int, x: Callable):
    self._funcs.insert(i, x)

misc

Miscellaneous utility functions

DTypeAndDevice (tuple)

DTypeAndDevice(dtype, device)

__getnewargs__(self) special

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

Source code in evotorch/tools/misc.py
def __getnewargs__(self):
    'Return self as a plain tuple.  Used by copy and pickle.'
    return _tuple(self)

__new__(_cls, dtype, device) special staticmethod

Create new instance of DTypeAndDevice(dtype, device)

__repr__(self) special

Return a nicely formatted representation string

Source code in evotorch/tools/misc.py
def __repr__(self):
    'Return a nicely formatted representation string'
    return self.__class__.__name__ + repr_fmt % self

ErroneousResult

Representation of a caught error being returned as a result.

Source code in evotorch/tools/misc.py
class ErroneousResult:
    """
    Representation of a caught error being returned as a result.
    """

    def __init__(self, error: Exception):
        self.error = error

    def _to_string(self) -> str:
        return f"<{type(self).__name__}, error: {self.error}>"

    def __str__(self) -> str:
        return self._to_string()

    def __repr__(self) -> str:
        return self._to_string()

    def __bool__(self) -> bool:
        return False

    @staticmethod
    def call(f, *args, **kwargs) -> Any:
        """
        Call a function with the given arguments.
        If the function raises an error, wrap the error in an ErroneousResult
        object, and return that ErroneousResult object instead.

        Returns:
            The result of the function if there was no error,
            or an ErroneousResult if there was an error.
        """
        try:
            result = f(*args, **kwargs)
        except Exception as ex:
            result = ErroneousResult(ex)
        return result

call(f, *args, **kwargs) staticmethod

Call a function with the given arguments. If the function raises an error, wrap the error in an ErroneousResult object, and return that ErroneousResult object instead.

Returns:

Type Description
Any

The result of the function if there was no error, or an ErroneousResult if there was an error.

Source code in evotorch/tools/misc.py
@staticmethod
def call(f, *args, **kwargs) -> Any:
    """
    Call a function with the given arguments.
    If the function raises an error, wrap the error in an ErroneousResult
    object, and return that ErroneousResult object instead.

    Returns:
        The result of the function if there was no error,
        or an ErroneousResult if there was an error.
    """
    try:
        result = f(*args, **kwargs)
    except Exception as ex:
        result = ErroneousResult(ex)
    return result

as_tensor(x, *, dtype=None, device=None)

Get the tensor counterpart of the given object x.

This function can be used to convert native Python objects to tensors:

my_tensor = as_tensor([1.0, 2.0, 3.0], dtype="float32")

One can also use this function to convert an existing tensor to another dtype:

my_new_tensor = as_tensor(my_tensor, dtype="float16")

This function can also be used for moving a tensor from one device to another:

my_gpu_tensor = as_tensor(my_tensor, device="cuda:0")

This function can also create ObjectArray instances when dtype is given as object or Any or "object" or "O".

my_objects = as_tensor([1, {"a": 3}], dtype=object)

Parameters:

Name Type Description Default
x Any

Any object to be converted to a tensor.

required
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "float32") or a PyTorch dtype (e.g. torch.float32) or, for creating an ObjectArray, "object" (as string) or object or Any. If dtype is not specified, the default behavior of torch.as_tensor(...) will be used, that is, dtype will be inferred from x.

None
device Union[str, torch.device]

The device in which the resulting tensor will be stored.

None

Returns:

Type Description
Iterable

The tensor counterpart of the given object x.

Source code in evotorch/tools/misc.py
def as_tensor(x: Any, *, dtype: Optional[DType] = None, device: Optional[Device] = None) -> Iterable:
    """
    Get the tensor counterpart of the given object `x`.

    This function can be used to convert native Python objects to tensors:

        my_tensor = as_tensor([1.0, 2.0, 3.0], dtype="float32")

    One can also use this function to convert an existing tensor to another
    dtype:

        my_new_tensor = as_tensor(my_tensor, dtype="float16")

    This function can also be used for moving a tensor from one device to
    another:

        my_gpu_tensor = as_tensor(my_tensor, device="cuda:0")

    This function can also create ObjectArray instances when dtype is
    given as `object` or `Any` or "object" or "O".

        my_objects = as_tensor([1, {"a": 3}], dtype=object)

    Args:
        x: Any object to be converted to a tensor.
        dtype: Optionally a string (e.g. "float32") or a PyTorch dtype
            (e.g. torch.float32) or, for creating an `ObjectArray`,
            "object" (as string) or `object` or `Any`.
            If `dtype` is not specified, the default behavior of
            `torch.as_tensor(...)` will be used, that is, dtype will be
            inferred from `x`.
        device: The device in which the resulting tensor will be stored.
    Returns:
        The tensor counterpart of the given object `x`.
    """
    from .objectarray import ObjectArray

    if (dtype is None) and isinstance(x, (torch.Tensor, ObjectArray)):
        if (device is None) or (str(device) == "cpu"):
            return x
        else:
            raise ValueError(
                f"An ObjectArray cannot be moved into a device other than 'cpu'." f" The received device is: {device}."
            )
    elif is_dtype_object(dtype):
        if (device is None) or (str(device) == "cpu"):
            raise ValueError(
                f"An ObjectArray cannot be created on a device other than 'cpu'." f" The received device is: {device}."
            )
        if isinstance(x, ObjectArray):
            return x
        else:
            x = list(x)
            n = len(x)
            result = ObjectArray(n)
            result[:] = x
            return result
    else:
        dtype = to_torch_dtype(dtype)
        return torch.as_tensor(x, dtype=dtype, device=device)

cast_tensors_in_container(container, *, dtype=None, device=None, memo=None)

Cast and/or transfer all the tensors in a Python container.

Parameters:

Name Type Description Default
dtype Union[str, torch.dtype, numpy.dtype, Type]

If given as a dtype and not as None, then all the PyTorch tensors in the container will be cast to this dtype.

None
device Union[str, torch.device]

If given as a device and not as None, then all the PyTorch tensors in the container will be copied to this device.

None
memo Optional[dict]

Optionally a memo dictionary to handle shared objects and circular references. In most scenarios, when calling this function from outside, this is expected as None.

None

Returns:

Type Description
Any

A new copy of the original container in which the tensors have the desired dtype and/or device.

Source code in evotorch/tools/misc.py
def cast_tensors_in_container(
    container: Any,
    *,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
    memo: Optional[dict] = None,
) -> Any:
    """
    Cast and/or transfer all the tensors in a Python container.

    Args:
        dtype: If given as a dtype and not as None, then all the PyTorch
            tensors in the container will be cast to this dtype.
        device: If given as a device and not as None, then all the PyTorch
            tensors in the container will be copied to this device.
        memo: Optionally a memo dictionary to handle shared objects and
            circular references. In most scenarios, when calling this
            function from outside, this is expected as None.
    Returns:
        A new copy of the original container in which the tensors have the
        desired dtype and/or device.
    """

    if memo is None:
        memo = {}

    container_id = id(container)
    if container_id in memo:
        return memo[container_id]

    cast_kwargs = {}
    if dtype is not None:
        cast_kwargs["dtype"] = to_torch_dtype(dtype)
    if device is not None:
        cast_kwargs["device"] = device

    def call_self(sub_container: Any) -> Any:
        return cast_tensors_in_container(sub_container, dtype=dtype, device=device, memo=memo)

    if isinstance(container, torch.Tensor):
        result = torch.as_tensor(container, **cast_kwargs)
        memo[container_id] = result
    elif (container is None) or isinstance(container, (Number, str, bytes, bool)):
        result = container
    elif isinstance(container, set):
        result = set()
        memo[container_id] = result
        for x in container:
            result.add(call_self(x))
    elif isinstance(container, Mapping):
        result = {}
        memo[container_id] = result
        for k, v in container.items():
            result[k] = call_self(v)
    elif isinstance(container, tuple):
        result = []
        memo[container_id] = result
        for x in container:
            result.append(call_self(x))
        if hasattr(container, "_fields"):
            result = type(container)(*result)
        else:
            result = type(container)(result)
        memo[container_id] = result
    elif isinstance(container, Iterable):
        result = []
        memo[container_id] = result
        for x in container:
            result.append(call_self(x))
    else:
        raise TypeError(f"Encountered an object of unrecognized type: {type(container)}")

    return result

clip_tensor(x, lb=None, ub=None, ensure_copy=True)

Clip the values of a tensor with respect to the given bounds.

Parameters:

Name Type Description Default
x Tensor

The PyTorch tensor whose values will be clipped.

required
lb Union[float, Iterable]

Lower bounds, as a PyTorch tensor. Can be None if there are no lower bounds.

None
ub Union[float, Iterable]

Upper bounds, as a PyTorch tensor. Can be None if there are no upper bonuds.

None
ensure_copy bool

If ensure_copy is True, the result will be a clipped copy of the original tensor. If ensure_copy is False, and both lb and ub are None, then there is nothing to do, so, the result will be the original tensor itself, not a copy of it.

True

Returns:

Type Description
Tensor

The clipped tensor.

Source code in evotorch/tools/misc.py
@torch.no_grad()
def clip_tensor(
    x: torch.Tensor,
    lb: Optional[Union[float, Iterable]] = None,
    ub: Optional[Union[float, Iterable]] = None,
    ensure_copy: bool = True,
) -> torch.Tensor:
    """
    Clip the values of a tensor with respect to the given bounds.

    Args:
        x: The PyTorch tensor whose values will be clipped.
        lb: Lower bounds, as a PyTorch tensor.
            Can be None if there are no lower bounds.
        ub: Upper bounds, as a PyTorch tensor.
            Can be None if there are no upper bonuds.
        ensure_copy: If `ensure_copy` is True, the result will be
            a clipped copy of the original tensor.
            If `ensure_copy` is False, and both `lb` and `ub`
            are None, then there is nothing to do, so, the result
            will be the original tensor itself, not a copy of it.
    Returns:
        The clipped tensor.
    """
    result = x
    if lb is not None:
        lb = torch.as_tensor(lb, dtype=x.dtype, device=x.device)
        result = torch.max(result, lb)
    if ub is not None:
        ub = torch.as_tensor(ub, dtype=x.dtype, device=x.device)
        result = torch.min(result, ub)
    if ensure_copy and result is x:
        result = x.clone()
    return result

clone(x, *, memo=None)

Get a deep copy of the given object.

The cloning is done in no_grad mode.

When this function is used on read-only containers (e.g. ReadOnlyTensor, ImmutableContainer, etc.), the created clones preserve their read-only behaviors. For creating a mutable clone of an immutable object, use their clone() method instead.

Returns:

Type Description
Any

The deep copy of the given object.

Source code in evotorch/tools/misc.py
@torch.no_grad()
def clone(x: Any, *, memo: Optional[dict] = None) -> Any:
    """
    Get a deep copy of the given object.

    The cloning is done in no_grad mode.

    When this function is used on read-only containers (e.g. ReadOnlyTensor,
    ImmutableContainer, etc.), the created clones preserve their read-only
    behaviors. For creating a mutable clone of an immutable object,
    use their `clone()` method instead.

    Returns:
        The deep copy of the given object.
    """
    from .cloning import deep_clone

    if memo is None:
        memo = {}
    return deep_clone(x, otherwise_deepcopy=True, memo=memo)

device_of(x)

Get the device of the given object.

Parameters:

Name Type Description Default
x Any

The object whose device is being queried. The object can be a PyTorch tensor, or a PyTorch module (in which case the device of the first parameter tensor will be returned), or an ObjectArray (in which case the returned device will be the cpu device), or any object with the attribute device.

required

Returns:

Type Description
Union[str, torch.device]

The device of the given object.

Source code in evotorch/tools/misc.py
def device_of(x: Any) -> Device:
    """
    Get the device of the given object.

    Args:
        x: The object whose device is being queried.
            The object can be a PyTorch tensor, or a PyTorch module
            (in which case the device of the first parameter tensor
            will be returned), or an ObjectArray (in which case
            the returned device will be the cpu device), or any object
            with the attribute `device`.
    Returns:
        The device of the given object.
    """
    if isinstance(x, nn.Module):
        result = None
        for param in x.parameters():
            result = param.device
            break
        if result is None:
            raise ValueError(f"Cannot determine the device of the module {x}")
        return result
    else:
        return x.device

device_of_container(container, *, visited=None, visiting=None)

Get the device of the given container.

It is assumed that the given container stores PyTorch tensors from which the device information will be extracted. If the container contains only basic types like int, float, string, bool, or None, or if the container is empty, then the returned device will be None. If the container contains unrecognized objects, an error will be raised.

Parameters:

Name Type Description Default
container Any

A sequence or a dictionary of objects from which the device information will be extracted.

required
visited Optional[dict]

Optionally a dictionary which stores the (sub)containers which are already visited. In most cases, when this function is called from outside, this is expected as None.

None
visiting Optional[str]

Optionally a set which stores the (sub)containers which are being visited. This set is used to prevent recursion errors while handling circular references. In most cases, when this function is called from outside, this argument is expected as None.

None

Returns:

Type Description
Optional[torch.device]

The device if available, None otherwise.

Source code in evotorch/tools/misc.py
def device_of_container(
    container: Any, *, visited: Optional[dict] = None, visiting: Optional[str] = None
) -> Optional[torch.device]:
    """
    Get the device of the given container.

    It is assumed that the given container stores PyTorch tensors from
    which the device information will be extracted.
    If the container contains only basic types like int, float, string,
    bool, or None, or if the container is empty, then the returned device
    will be None.
    If the container contains unrecognized objects, an error will be
    raised.

    Args:
        container: A sequence or a dictionary of objects from which the
            device information will be extracted.
        visited: Optionally a dictionary which stores the (sub)containers
            which are already visited. In most cases, when this function
            is called from outside, this is expected as None.
        visiting: Optionally a set which stores the (sub)containers
            which are being visited. This set is used to prevent recursion
            errors while handling circular references. In most cases,
            when this function is called from outside, this argument is
            expected as None.
    Returns:
        The device if available, None otherwise.
    """
    container_id = id(container)

    if visited is None:
        visited = {}

    if container_id in visited:
        return visited[container_id]

    if visiting is None:
        visiting = set()

    if container_id in visiting:
        return None

    class result:
        device: Optional[torch.device] = None

        @classmethod
        def update(cls, new_device: Optional[torch.device]):
            if new_device is not None:
                if cls.device is None:
                    cls.device = new_device
                else:
                    if new_device != cls.device:
                        raise ValueError(f"Encountered tensors whose `device`s mismatch: {new_device}, {cls.device}")

    def call_self(sub_container):
        return device_of_container(sub_container, visited=visited, visiting=visiting)

    if isinstance(container, torch.Tensor):
        result.update(container.device)
    elif (container is None) or isinstance(container, (Number, str, bytes, bool)):
        pass
    elif isinstance(container, Mapping):
        visiting.add(container_id)
        try:
            for _, v in container.items():
                result.update(call_self(v))
        finally:
            visiting.remove(container_id)
    elif isinstance(container, Iterable):
        visiting.add(container_id)
        try:
            for v in container:
                result.update(call_self(v))
        finally:
            visiting.remove(container_id)
    else:
        raise TypeError(f"Encountered an object of unrecognized type: {type(container)}")

    visited[container_id] = result.device
    return result.device

dtype_of(x)

Get the dtype of the given object.

Parameters:

Name Type Description Default
x Any

The object whose dtype is being queried. The object can be a PyTorch tensor, or a PyTorch module (in which case the dtype of the first parameter tensor will be returned), or an ObjectArray (in which case the returned dtype will be object), or any object with the attribute dtype.

required

Returns:

Type Description
Union[str, torch.dtype, numpy.dtype, Type]

The dtype of the given object.

Source code in evotorch/tools/misc.py
def dtype_of(x: Any) -> DType:
    """
    Get the dtype of the given object.

    Args:
        x: The object whose dtype is being queried.
            The object can be a PyTorch tensor, or a PyTorch module
            (in which case the dtype of the first parameter tensor
            will be returned), or an ObjectArray (in which case
            the returned dtype will be `object`), or any object with
            the attribute `dtype`.
    Returns:
        The dtype of the given object.
    """
    if isinstance(x, nn.Module):
        result = None
        for param in x.parameters():
            result = param.dtype
            break
        if result is None:
            raise ValueError(f"Cannot determine the dtype of the module {x}")
        return result
    else:
        return x.dtype

dtype_of_container(container, *, visited=None, visiting=None)

Get the dtype of the given container.

It is assumed that the given container stores PyTorch tensors from which the dtype information will be extracted. If the container contains only basic types like int, float, string, bool, or None, or if the container is empty, then the returned dtype will be None. If the container contains unrecognized objects, an error will be raised.

Parameters:

Name Type Description Default
container Any

A sequence or a dictionary of objects from which the dtype information will be extracted.

required
visited Optional[dict]

Optionally a dictionary which stores the (sub)containers which are already visited. In most cases, when this function is called from outside, this is expected as None.

None
visiting Optional[str]

Optionally a set which stores the (sub)containers which are being visited. This set is used to prevent recursion errors while handling circular references. In most cases, when this function is called from outside, this argument is expected as None.

None

Returns:

Type Description
Optional[torch.dtype]

The dtype if available, None otherwise.

Source code in evotorch/tools/misc.py
def dtype_of_container(
    container: Any, *, visited: Optional[dict] = None, visiting: Optional[str] = None
) -> Optional[torch.dtype]:
    """
    Get the dtype of the given container.

    It is assumed that the given container stores PyTorch tensors from
    which the dtype information will be extracted.
    If the container contains only basic types like int, float, string,
    bool, or None, or if the container is empty, then the returned dtype
    will be None.
    If the container contains unrecognized objects, an error will be
    raised.

    Args:
        container: A sequence or a dictionary of objects from which the
            dtype information will be extracted.
        visited: Optionally a dictionary which stores the (sub)containers
            which are already visited. In most cases, when this function
            is called from outside, this is expected as None.
        visiting: Optionally a set which stores the (sub)containers
            which are being visited. This set is used to prevent recursion
            errors while handling circular references. In most cases,
            when this function is called from outside, this argument is
            expected as None.
    Returns:
        The dtype if available, None otherwise.
    """

    container_id = id(container)

    if visited is None:
        visited = {}

    if container_id in visited:
        return visited[container_id]

    if visiting is None:
        visiting = set()

    if container_id in visiting:
        return None

    class result:
        dtype: Optional[torch.dtype] = None

        @classmethod
        def update(cls, new_dtype: Optional[torch.dtype]):
            if new_dtype is not None:
                if cls.dtype is None:
                    cls.dtype = new_dtype
                else:
                    if new_dtype != cls.dtype:
                        raise ValueError(f"Encountered tensors whose `dtype`s mismatch: {new_dtype}, {cls.dtype}")

    def call_self(sub_container):
        return dtype_of_container(sub_container, visited=visited, visiting=visiting)

    if isinstance(container, torch.Tensor):
        result.update(container.dtype)
    elif (container is None) or isinstance(container, (Number, str, bytes, bool)):
        pass
    elif isinstance(container, Mapping):
        visiting.add(container_id)
        try:
            for _, v in container.items():
                result.update(call_self(v))
        finally:
            visiting.remove(container_id)
    elif isinstance(container, Iterable):
        visiting.add(container_id)
        try:
            for v in container:
                result.update(call_self(v))
        finally:
            visiting.remove(container_id)
    else:
        raise TypeError(f"Encountered an object of unrecognized type: {type(container)}")

    visited[container_id] = result.dtype
    return result.dtype

empty_tensor_like(source, *, shape=None, length=None, dtype=None, device=None)

Make an empty tensor with attributes taken from a source tensor.

The source tensor can be a PyTorch tensor, or an ObjectArray.

Unlike torch.empty_like(...), this function allows one to redefine the shape and/or length of the new empty tensor.

Parameters:

Name Type Description Default
source Any

The source tensor whose shape, dtype, and device will be used by default for the new empty tensor.

required
shape Union[tuple, int]

If given as None (which is the default), then the shape of the source tensor will be used for the new empty tensor. If given as a tuple or a torch.Size instance, then the new empty tensor will be in this given shape instead. This argument cannot be used together with length.

None
length Optional[int]

If given as None (which is the default), then the length of the new empty tensor will be equal to the length of the source tensor (where length here means the size of the outermost dimension, i.e., what is returned by len(...)). If given as an integer, the length of the empty tensor will be this given length instead. This argument cannot be used together with shape.

None
dtype Union[str, torch.dtype, numpy.dtype, Type]

If given as None, the dtype of the new empty tensor will be the dtype of the source tensor. If given as a torch.dtype instance, then the dtype of the tensor will be this given dtype instead.

None
device Union[str, torch.device]

If given as None, the device of the new empty tensor will be the device of the source tensor. If given as a torch.device instance, then the device of the tensor will be this given device instead.

None

Returns:

Type Description
Any

The new empty tensor.

Source code in evotorch/tools/misc.py
def empty_tensor_like(
    source: Any,
    *,
    shape: Optional[Union[tuple, int]] = None,
    length: Optional[int] = None,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
) -> Any:
    """
    Make an empty tensor with attributes taken from a source tensor.

    The source tensor can be a PyTorch tensor, or an ObjectArray.

    Unlike `torch.empty_like(...)`, this function allows one to redefine the
    shape and/or length of the new empty tensor.

    Args:
        source: The source tensor whose shape, dtype, and device will be used
            by default for the new empty tensor.
        shape: If given as None (which is the default), then the shape of the
            source tensor will be used for the new empty tensor.
            If given as a tuple or a `torch.Size` instance, then the new empty
            tensor will be in this given shape instead.
            This argument cannot be used together with `length`.
        length: If given as None (which is the default), then the length of
            the new empty tensor will be equal to the length of the source
            tensor (where length here means the size of the outermost
            dimension, i.e., what is returned by `len(...)`).
            If given as an integer, the length of the empty tensor will be
            this given length instead.
            This argument cannot be used together with `shape`.
        dtype: If given as None, the dtype of the new empty tensor will be
            the dtype of the source tensor.
            If given as a `torch.dtype` instance, then the dtype of the
            tensor will be this given dtype instead.
        device: If given as None, the device of the new empty tensor will be
            the device of the source tensor.
            If given as a `torch.device` instance, then the device of the
            tensor will be this given device instead.
    Returns:
        The new empty tensor.
    """
    from .objectarray import ObjectArray

    if isinstance(source, ObjectArray):
        if length is not None and shape is None:
            n = int(length)
        elif shape is not None and length is None:
            if isinstance(shape, Iterable):
                if len(shape) != 1:
                    raise ValueError(
                        f"An ObjectArray must always be 1-dimensional."
                        f" Therefore, this given shape is incompatible: {shape}"
                    )
                n = int(shape[0])
        elif length is None and shape is None:
            n = len(source)
        else:
            raise ValueError("`length` and `shape` cannot be used together")

        if device is not None:
            if str(device) != "cpu":
                raise ValueError(
                    f"An ObjectArray can only be allocated on cpu. However, the specified `device` is: {device}."
                )

        if dtype is not None:
            if not is_dtype_object(dtype):
                raise ValueError(
                    f"The dtype of an ObjectArray can only be `object`. However, the specified `dtype` is: {dtype}."
                )

        return ObjectArray(n)
    elif isinstance(source, torch.Tensor):
        if length is not None:
            if shape is not None:
                raise ValueError("`length` and `shape` cannot be used together")
            if source.ndim == 0:
                raise ValueError("`length` can only be used when the source tensor is at least 1-dimensional")
            newshape = [int(length)]
            newshape.extend(source.shape[1:])
            shape = tuple(newshape)

        if not ((dtype is None) or isinstance(dtype, torch.dtype)):
            dtype = to_torch_dtype(dtype)

        return torch.empty(
            source.shape if shape is None else shape,
            dtype=(source.dtype if dtype is None else dtype),
            device=(source.device if device is None else device),
        )
    else:
        raise TypeError(f"The source tensor is of an unrecognized type: {type(source)}")

ensure_ray()

Ensure that the ray parallelization engine is initialized. If ray is already initialized, this function does nothing.

Source code in evotorch/tools/misc.py
def ensure_ray():
    """
    Ensure that the ray parallelization engine is initialized.
    If ray is already initialized, this function does nothing.
    """
    import ray

    if not ray.is_initialized():
        ray.init()

ensure_tensor_length_and_dtype(t, length, dtype, about=None, *, allow_scalar=False, device=None)

Return the given sequence as a tensor while also confirming its length, dtype, and device. If the given object is already a tensor conforming to the desired length, dtype, and device, the object will be returned as it is (there will be no copying).

Parameters:

Name Type Description Default
t Any

The tensor, or a sequence which is convertible to a tensor.

required
length int

The length to which the tensor is expected to conform.

required
dtype Union[str, torch.dtype, numpy.dtype, Type]

The dtype to which the tensor is expected to conform.

required
about Optional[str]

The prefix for the error message. Can be left as None.

None
allow_scalar bool

Whether or not to accept scalars in addition to vector of the desired length. If allow_scalar is False, then scalars will be converted to sequences of the desired length. The sequence will contain the same scalar, repeated. If allow_scalar is True, then the scalar itself will be converted to a PyTorch scalar, and then will be returned.

False
device Union[str, torch.device]

The device in which the sequence is to be stored. If the given sequence is on a different device than the desired device, a copy on the correct device will be made. If device is None, the default behavior of torch.tensor(...) will be used, that is: if t is already a tensor, the result will be on the same device, otherwise, the result will be on the cpu.

None

Returns:

Type Description
Any

The sequence whose correctness in terms of length, dtype, and device is ensured.

Exceptions:

Type Description
ValueError

if there is a length mismatch.

Source code in evotorch/tools/misc.py
@torch.no_grad()
def ensure_tensor_length_and_dtype(
    t: Any,
    length: int,
    dtype: DType,
    about: Optional[str] = None,
    *,
    allow_scalar: bool = False,
    device: Optional[Device] = None,
) -> Any:
    """
    Return the given sequence as a tensor while also confirming its
    length, dtype, and device.
    If the given object is already a tensor conforming to the desired
    length, dtype, and device, the object will be returned as it is
    (there will be no copying).

    Args:
        t: The tensor, or a sequence which is convertible to a tensor.
        length: The length to which the tensor is expected to conform.
        dtype: The dtype to which the tensor is expected to conform.
        about: The prefix for the error message. Can be left as None.
        allow_scalar: Whether or not to accept scalars in addition
            to vector of the desired length.
            If `allow_scalar` is False, then scalars will be converted
            to sequences of the desired length. The sequence will contain
            the same scalar, repeated.
            If `allow_scalar` is True, then the scalar itself will be
            converted to a PyTorch scalar, and then will be returned.
        device: The device in which the sequence is to be stored.
            If the given sequence is on a different device than the
            desired device, a copy on the correct device will be made.
            If device is None, the default behavior of `torch.tensor(...)`
            will be used, that is: if `t` is already a tensor, the result
            will be on the same device, otherwise, the result will be on
            the cpu.
    Returns:
        The sequence whose correctness in terms of length, dtype, and
        device is ensured.
    Raises:
        ValueError: if there is a length mismatch.
    """
    device_args = {}
    if device is not None:
        device_args["device"] = device

    t = as_tensor(t, dtype=dtype, **device_args)

    if t.ndim == 0:
        if allow_scalar:
            return t
        else:
            return t.repeat(length)
    else:
        if t.ndim != 1 or len(t) != length:
            if about is not None:
                err_prefix = about + ": "
            else:
                err_prefix = ""
            raise ValueError(
                f"{err_prefix}Expected a 1-dimensional tensor of length {length}, but got a tensor with shape: {t.shape}"
            )
        return t

expect_none(msg_prefix, **kwargs)

Expect the values associated with the given keyword arguments to be None. If not, raise error.

Parameters:

Name Type Description Default
msg_prefix str

Prefix of the error message.

required
kwargs

Keyword arguments whose values are expected to be None.

{}

Exceptions:

Type Description
ValueError

if at least one of the keyword arguments has a value other than None.

Source code in evotorch/tools/misc.py
def expect_none(msg_prefix: str, **kwargs):
    """
    Expect the values associated with the given keyword arguments
    to be None. If not, raise error.

    Args:
        msg_prefix: Prefix of the error message.
        kwargs: Keyword arguments whose values are expected to be None.
    Raises:
        ValueError: if at least one of the keyword arguments has a value
            other than None.
    """
    for k, v in kwargs.items():
        if v is not None:
            raise ValueError(f"{msg_prefix}: expected `{k}` as None, however, it was found to be {repr(v)}")

is_bool(x)

Return True if x represents a bool.

Parameters:

Name Type Description Default
x Any

An object whose type is being queried.

required

Returns:

Type Description
bool

True if x is a bool; False otherwise.

Source code in evotorch/tools/misc.py
def is_bool(x: Any) -> bool:
    """
    Return True if `x` represents a bool.

    Args:
        x: An object whose type is being queried.
    Returns:
        True if `x` is a bool; False otherwise.
    """
    if isinstance(x, (bool, np.bool_)):
        return True
    elif isinstance(x, (torch.Tensor, np.ndarray)):
        if x.ndim > 0:
            return False
        else:
            return is_dtype_bool(x.dtype)
    else:
        return False

is_bool_vector(x)

Return True if x is a vector consisting of bools.

Parameters:

Name Type Description Default
x Any

An object whose elements' types are to be queried.

required

Returns:

Type Description
bool

True if the elements of x are bools; False otherwise.

Source code in evotorch/tools/misc.py
def is_bool_vector(x: Any) -> bool:
    """
    Return True if `x` is a vector consisting of bools.

    Args:
        x: An object whose elements' types are to be queried.
    Returns:
        True if the elements of `x` are bools; False otherwise.
    """
    if isinstance(x, (torch.Tensor, np.ndarray)):
        if x.ndim != 1:
            return False
        else:
            return is_dtype_bool(x.dtype)
    elif isinstance(x, Iterable):
        for item in x:
            if not is_bool(item):
                return False
        return True
    else:
        return False

is_dtype_bool(t)

Return True if the given dtype is an bool type.

Parameters:

Name Type Description Default
t Union[str, torch.dtype, numpy.dtype, Type]

The dtype, which can be a dtype string, a numpy dtype, or a PyTorch dtype.

required

Returns:

Type Description
bool

True if t is a bool type; False otherwise.

Source code in evotorch/tools/misc.py
def is_dtype_bool(t: DType) -> bool:
    """
    Return True if the given dtype is an bool type.

    Args:
        t: The dtype, which can be a dtype string, a numpy dtype,
            or a PyTorch dtype.
    Returns:
        True if t is a bool type; False otherwise.
    """
    t: np.dtype = to_numpy_dtype(t)
    return t.kind.startswith("b")

is_dtype_float(t)

Return True if the given dtype is an float type.

Parameters:

Name Type Description Default
t Union[str, torch.dtype, numpy.dtype, Type]

The dtype, which can be a dtype string, a numpy dtype, or a PyTorch dtype.

required

Returns:

Type Description
bool

True if t is an float type; False otherwise.

Source code in evotorch/tools/misc.py
def is_dtype_float(t: DType) -> bool:
    """
    Return True if the given dtype is an float type.

    Args:
        t: The dtype, which can be a dtype string, a numpy dtype,
            or a PyTorch dtype.
    Returns:
        True if t is an float type; False otherwise.
    """
    t: np.dtype = to_numpy_dtype(t)
    return t.kind.startswith("f")

is_dtype_integer(t)

Return True if the given dtype is an integer type.

Parameters:

Name Type Description Default
t Union[str, torch.dtype, numpy.dtype, Type]

The dtype, which can be a dtype string, a numpy dtype, or a PyTorch dtype.

required

Returns:

Type Description
bool

True if t is an integer type; False otherwise.

Source code in evotorch/tools/misc.py
def is_dtype_integer(t: DType) -> bool:
    """
    Return True if the given dtype is an integer type.

    Args:
        t: The dtype, which can be a dtype string, a numpy dtype,
            or a PyTorch dtype.
    Returns:
        True if t is an integer type; False otherwise.
    """
    t: np.dtype = to_numpy_dtype(t)
    return t.kind.startswith("u") or t.kind.startswith("i")

is_dtype_object(dtype)

Return True if the given dtype is object or Any.

Returns:

Type Description
bool

True if the given dtype is object or Any; False otherwise.

Source code in evotorch/tools/misc.py
def is_dtype_object(dtype: DType) -> bool:
    """
    Return True if the given dtype is `object` or `Any`.

    Returns:
        True if the given dtype is `object` or `Any`; False otherwise.
    """
    if isinstance(dtype, str):
        return dtype in ("object", "Any", "O")
    elif dtype is object or dtype is Any:
        return True
    else:
        return False

is_dtype_real(t)

Return True if the given dtype represents real numbers (i.e. if dtype is an integer type or is a float type).

Parameters:

Name Type Description Default
t Union[str, torch.dtype, numpy.dtype, Type]

The dtype, which can be a dtype string, a numpy dtype, or a PyTorch dtype.

required

Returns:

Type Description
bool

True if t represents a real numbers type; False otherwise.

Source code in evotorch/tools/misc.py
def is_dtype_real(t: DType) -> bool:
    """
    Return True if the given dtype represents real numbers
    (i.e. if dtype is an integer type or is a float type).

    Args:
        t: The dtype, which can be a dtype string, a numpy dtype,
            or a PyTorch dtype.
    Returns:
        True if t represents a real numbers type; False otherwise.
    """
    return is_dtype_float(t) or is_dtype_integer(t)

is_integer(x)

Return True if x is an integer.

Note that this function does NOT consider booleans as integers.

Parameters:

Name Type Description Default
x Any

An object whose type is being queried.

required

Returns:

Type Description
bool

True if x is an integer; False otherwise.

Source code in evotorch/tools/misc.py
def is_integer(x: Any) -> bool:
    """
    Return True if `x` is an integer.

    Note that this function does NOT consider booleans as integers.

    Args:
        x: An object whose type is being queried.
    Returns:
        True if `x` is an integer; False otherwise.
    """
    if is_bool(x):
        return False
    elif isinstance(x, Integral):
        return True
    elif isinstance(x, (torch.Tensor, np.ndarray)):
        if x.ndim > 0:
            return False
        else:
            return is_dtype_integer(x.dtype)
    else:
        return False

is_integer_vector(x)

Return True if x is a vector consisting of integers.

Parameters:

Name Type Description Default
x Any

An object whose elements' types are to be queried.

required

Returns:

Type Description
bool

True if the elements of x are integers; False otherwise.

Source code in evotorch/tools/misc.py
def is_integer_vector(x: Any) -> bool:
    """
    Return True if `x` is a vector consisting of integers.

    Args:
        x: An object whose elements' types are to be queried.
    Returns:
        True if the elements of `x` are integers; False otherwise.
    """
    if isinstance(x, (torch.Tensor, np.ndarray)):
        if x.ndim != 1:
            return False
        else:
            return is_dtype_integer(x.dtype)
    elif isinstance(x, Iterable):
        for item in x:
            if not is_integer(item):
                return False
        return True
    else:
        return False

is_real(x)

Return True if x is a real number.

Note that this function does NOT consider booleans as real numbers.

Parameters:

Name Type Description Default
x Any

An object whose type is being queried.

required

Returns:

Type Description
bool

True if x is a real number; False otherwise.

Source code in evotorch/tools/misc.py
def is_real(x: Any) -> bool:
    """
    Return True if `x` is a real number.

    Note that this function does NOT consider booleans as real numbers.

    Args:
        x: An object whose type is being queried.
    Returns:
        True if `x` is a real number; False otherwise.
    """
    if is_bool(x):
        return False
    elif isinstance(x, Real):
        return True
    elif isinstance(x, (torch.Tensor, np.ndarray)):
        if x.ndim > 0:
            return False
        else:
            return is_dtype_real(x.dtype)
    else:
        return False

is_real_vector(x)

Return True if x is a vector consisting of real numbers.

Parameters:

Name Type Description Default
x Any

An object whose elements' types are to be queried.

required

Returns:

Type Description
bool

True if the elements of x are real numbers; False otherwise.

Source code in evotorch/tools/misc.py
def is_real_vector(x: Any) -> bool:
    """
    Return True if `x` is a vector consisting of real numbers.

    Args:
        x: An object whose elements' types are to be queried.
    Returns:
        True if the elements of `x` are real numbers; False otherwise.
    """
    if isinstance(x, (torch.Tensor, np.ndarray)):
        if x.ndim != 1:
            return False
        else:
            return is_dtype_real(x.dtype)
    elif isinstance(x, Iterable):
        for item in x:
            if not is_real(item):
                return False
        return True
    else:
        return False

is_sequence(x)

Return True if x is a sequence. Note that this function considers str and bytes as scalars, not as sequences.

Parameters:

Name Type Description Default
x Any

The object whose sequential nature is being queried.

required

Returns:

Type Description
bool

True if x is a sequence; False otherwise.

Source code in evotorch/tools/misc.py
def is_sequence(x: Any) -> bool:
    """
    Return True if `x` is a sequence.
    Note that this function considers `str` and `bytes` as scalars,
    not as sequences.

    Args:
        x: The object whose sequential nature is being queried.
    Returns:
        True if `x` is a sequence; False otherwise.
    """
    if isinstance(x, (str, bytes)):
        return False
    elif isinstance(x, (np.ndarray, torch.Tensor)):
        return x.ndim > 0
    elif isinstance(x, Iterable):
        return True
    else:
        return False

is_tensor_on_cpu(tensor)

Return True of the given PyTorch tensor or ObjectArray is on cpu.

Source code in evotorch/tools/misc.py
def is_tensor_on_cpu(tensor) -> bool:
    """
    Return True of the given PyTorch tensor or ObjectArray is on cpu.
    """
    return str(tensor.device) == "cpu"

make_I(size=None, *, out=None, dtype=None, device=None)

Make a new identity matrix (I), or change an existing tensor into one.

The following example creates a 3x3 identity matrix:

identity_matrix = make_I(3, dtype="float32")

The following example changes an already existing square matrix such that its values will store an identity matrix:

make_I(out=existing_tensor)

Parameters:

Name Type Description Default
size Optional[int]

A single integer or a tuple containing a single integer, where the integer specifies the length of the target square matrix. In this context, "length" means both rowwise length and columnwise length, since the target is a square matrix. Note that, if the user wishes to fill an existing tensor with identity values, then size is expected to be left as None.

None
out Optional[torch.Tensor]

Optionally, the existing tensor whose values will be changed so that they represent an identity matrix. If an out tensor is given, then size is expected as None.

None
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "float32") or a PyTorch dtype (e.g. torch.float32). If dtype is not specified, the default choice of torch.empty(...) is used, that is, torch.float32. If an out tensor is specified, then dtype is expected as None.

None
device Union[str, torch.device]

The device in which the new tensor will be stored. If not specified, "cpu" will be used. If an out tensor is specified, then device is expected as None.

None

Returns:

Type Description
Tensor

The created or modified tensor after placing the I matrix values

Source code in evotorch/tools/misc.py
def make_I(
    size: Optional[int] = None,
    *,
    out: Optional[torch.Tensor] = None,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
) -> torch.Tensor:
    """
    Make a new identity matrix (I), or change an existing tensor into one.

    The following example creates a 3x3 identity matrix:

        identity_matrix = make_I(3, dtype="float32")

    The following example changes an already existing square matrix such that
    its values will store an identity matrix:

        make_I(out=existing_tensor)

    Args:
        size: A single integer or a tuple containing a single integer,
            where the integer specifies the length of the target square
            matrix. In this context, "length" means both rowwise length
            and columnwise length, since the target is a square matrix.
            Note that, if the user wishes to fill an existing tensor with
            identity values, then `size` is expected to be left as None.
        out: Optionally, the existing tensor whose values will be changed
            so that they represent an identity matrix.
            If an `out` tensor is given, then `size` is expected as None.
        dtype: Optionally a string (e.g. "float32") or a PyTorch dtype
            (e.g. torch.float32).
            If `dtype` is not specified, the default choice of
            `torch.empty(...)` is used, that is, `torch.float32`.
            If an `out` tensor is specified, then `dtype` is expected
            as None.
        device: The device in which the new tensor will be stored.
            If not specified, "cpu" will be used.
            If an `out` tensor is specified, then `device` is expected
            as None.
    Returns:
        The created or modified tensor after placing the I matrix values
    """
    if size is None:
        if out is None:
            raise ValueError(
                "When the `size` argument is missing, the function `make_I(...)` expects an `out` tensor."
                " However, the `out` argument was received as None."
            )
        size = tuple()
    else:
        if isinstance(size, tuple):
            if len(size) == 1:
                size = size[0]
            else:
                raise ValueError(
                    f"When the `size` argument is given as a tuple,"
                    f" the function `make_I(...)` expects this tuple to contain exactly one element."
                    f" The received tuple is {size}."
                )
        n = int(size)
        size = (n, n)
    out = _out_tensor(*size, out=out, dtype=dtype, device=device)
    out.zero_()
    out.fill_diagonal_(1)
    return out

make_empty(*size, *, dtype=None, device=None)

Make an empty tensor.

Parameters:

Name Type Description Default
size Union[int, torch.Size]

Shape of the empty tensor to be created. expected as multiple positional arguments of integers, or as a single positional argument containing a tuple of integers. Note that when the user wishes to create an ObjectArray (i.e. when dtype is given as object), then the size is expected as a single integer, or as a single-element tuple containing an integer (because ObjectArray can only be one-dimensional).

()
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "float32") or a PyTorch dtype (e.g. torch.float32) or, for creating an ObjectArray, "object" (as string) or object or Any. If dtype is not specified, the default choice of torch.empty(...) is used, that is, torch.float32.

None
device Union[str, torch.device]

The device in which the new empty tensor will be stored. If not specified, "cpu" will be used.

None

Returns:

Type Description
Iterable

The new empty tensor, which can be a PyTorch tensor or an ObjectArray.

Source code in evotorch/tools/misc.py
def make_empty(
    *size: Size,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
) -> Iterable:
    """
    Make an empty tensor.

    Args:
        size: Shape of the empty tensor to be created.
            expected as multiple positional arguments of integers,
            or as a single positional argument containing a tuple of
            integers.
            Note that when the user wishes to create an `ObjectArray`
            (i.e. when `dtype` is given as `object`), then the size
            is expected as a single integer, or as a single-element
            tuple containing an integer (because `ObjectArray` can only
            be one-dimensional).
        dtype: Optionally a string (e.g. "float32") or a PyTorch dtype
            (e.g. torch.float32) or, for creating an `ObjectArray`,
            "object" (as string) or `object` or `Any`.
            If `dtype` is not specified, the default choice of
            `torch.empty(...)` is used, that is, `torch.float32`.
        device: The device in which the new empty tensor will be stored.
            If not specified, "cpu" will be used.
    Returns:
        The new empty tensor, which can be a PyTorch tensor or an
        `ObjectArray`.
    """
    from .objectarray import ObjectArray

    if (dtype is not None) and is_dtype_object(dtype):
        if (device is None) or (str(device) == "cpu"):
            if len(size) == 1:
                size = size[0]
            return ObjectArray(size)
        else:
            return ValueError(
                f"Invalid device for ObjectArray: {repr(device)}. Note: an ObjectArray can only be stored on 'cpu'."
            )
    else:
        kwargs = {}
        if dtype is not None:
            kwargs["dtype"] = to_torch_dtype(dtype)
        if device is not None:
            kwargs["device"] = device
        return torch.empty(*size, **kwargs)

make_gaussian(*size, *, center=None, stdev=None, symmetric=False, out=None, dtype=None, device=None, generator=None)

Make a new or existing tensor filled by Gaussian distributed values. This function can work only with float dtypes.

Parameters:

Name Type Description Default
size Union[int, torch.Size]

Size of the new tensor to be filled with Gaussian distributed values. This can be given as multiple positional arguments, each such positional argument being an integer, or as a single positional argument of a tuple, the tuple containing multiple integers. Note that, if the user wishes to fill an existing tensor instead, then no positional argument is expected.

()
center Union[float, Iterable[float], torch.Tensor]

Center point (i.e. mean) of the Gaussian distribution. Can be a scalar, or a tensor. If not specified, the center point will be taken as 0. Note that, if one specifies center, then stdev is also expected to be explicitly specified.

None
stdev Union[float, Iterable[float], torch.Tensor]

Standard deviation for the Gaussian distributed values. Can be a scalar, or a tensor. If not specified, the standard deviation will be taken as 1. Note that, if one specifies stdev, then center is also expected to be explicitly specified.

None
symmetric bool

Whether or not the values should be sampled in a symmetric (i.e. antithetic) manner. The default is False.

False
out Optional[torch.Tensor]

Optionally, the tensor to be filled by Gaussian distributed values. If an out tensor is given, then no size argument is expected.

None
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "float32") or a PyTorch dtype (e.g. torch.float32). If dtype is not specified, the default choice of torch.empty(...) is used, that is, torch.float32. If an out tensor is specified, then dtype is expected as None.

None
device Union[str, torch.device]

The device in which the new tensor will be stored. If not specified, "cpu" will be used. If an out tensor is specified, then device is expected as None.

None
generator Any

Pseudo-random number generator to be used when sampling the values. Can be a torch.Generator, or an object with a generator attribute (such as Problem). If left as None, the global generator of PyTorch will be used.

None

Returns:

Type Description
Tensor

The created or modified tensor after placing the Gaussian distributed values.

Source code in evotorch/tools/misc.py
def make_gaussian(
    *size: Size,
    center: Optional[RealOrVector] = None,
    stdev: Optional[RealOrVector] = None,
    symmetric: bool = False,
    out: Optional[torch.Tensor] = None,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
    generator: Any = None,
) -> torch.Tensor:
    """
    Make a new or existing tensor filled by Gaussian distributed values.
    This function can work only with float dtypes.

    Args:
        size: Size of the new tensor to be filled with Gaussian distributed
            values. This can be given as multiple positional arguments, each
            such positional argument being an integer, or as a single
            positional argument of a tuple, the tuple containing multiple
            integers. Note that, if the user wishes to fill an existing
            tensor instead, then no positional argument is expected.
        center: Center point (i.e. mean) of the Gaussian distribution.
            Can be a scalar, or a tensor.
            If not specified, the center point will be taken as 0.
            Note that, if one specifies `center`, then `stdev` is also
            expected to be explicitly specified.
        stdev: Standard deviation for the Gaussian distributed values.
            Can be a scalar, or a tensor.
            If not specified, the standard deviation will be taken as 1.
            Note that, if one specifies `stdev`, then `center` is also
            expected to be explicitly specified.
        symmetric: Whether or not the values should be sampled in a
            symmetric (i.e. antithetic) manner.
            The default is False.
        out: Optionally, the tensor to be filled by Gaussian distributed
            values. If an `out` tensor is given, then no `size` argument is
            expected.
        dtype: Optionally a string (e.g. "float32") or a PyTorch dtype
            (e.g. torch.float32).
            If `dtype` is not specified, the default choice of
            `torch.empty(...)` is used, that is, `torch.float32`.
            If an `out` tensor is specified, then `dtype` is expected
            as None.
        device: The device in which the new tensor will be stored.
            If not specified, "cpu" will be used.
            If an `out` tensor is specified, then `device` is expected
            as None.
        generator: Pseudo-random number generator to be used when sampling
            the values. Can be a `torch.Generator`, or an object with
            a `generator` attribute (such as `Problem`).
            If left as None, the global generator of PyTorch will be used.
    Returns:
        The created or modified tensor after placing the Gaussian
        distributed values.
    """
    scalar_requested = _scalar_requested(*size)
    if scalar_requested:
        size = (1,)

    out = _out_tensor(*size, out=out, dtype=dtype, device=device)
    gen_kwargs = _generator_kwargs(generator)

    if symmetric:
        leftmost_dim = out.shape[0]
        if (leftmost_dim % 2) != 0:
            raise ValueError(
                f"Symmetric sampling cannot be done if the leftmost dimension of the target tensor is odd."
                f" The shape of the target tensor is: {repr(out.shape)}."
            )
        out[0::2, ...].normal_(**gen_kwargs)
        out[1::2, ...] = out[0::2, ...]
        out[1::2, ...] *= -1
    else:
        out.normal_(**gen_kwargs)

    if (center is None) and (stdev is None):
        pass  # do nothing
    elif (center is not None) and (stdev is not None):
        stdev = torch.as_tensor(stdev, dtype=out.dtype, device=out.device)
        out *= stdev
        center = torch.as_tensor(center, dtype=out.dtype, device=out.device)
        out += center
    else:
        raise ValueError(
            f"Please either specify none of `stdev` and `center`, or both of them."
            f" Currently, `center` is {center}"
            f" and `stdev` is {stdev}."
        )

    if scalar_requested:
        out = out[0]

    return out

make_nan(*size, *, out=None, dtype=None, device=None)

Make a new tensor filled with NaN, or fill an existing tensor with NaN.

The following example creates a float32 tensor filled with NaN values, of shape (3, 5):

nan_values = make_nan(3, 5, dtype="float32")

The following example fills an existing tensor with NaNs.

make_nan(out=existing_tensor)

Parameters:

Name Type Description Default
size Union[int, torch.Size]

Size of the new tensor to be filled with NaNs. This can be given as multiple positional arguments, each such positional argument being an integer, or as a single positional argument of a tuple, the tuple containing multiple integers. Note that, if the user wishes to fill an existing tensor with NaN values, then no positional argument is expected.

()
out Optional[torch.Tensor]

Optionally, the tensor to be filled by NaN values. If an out tensor is given, then no size argument is expected.

None
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "float32") or a PyTorch dtype (e.g. torch.float32). If dtype is not specified, the default choice of torch.empty(...) is used, that is, torch.float32. If an out tensor is specified, then dtype is expected as None.

None
device Union[str, torch.device]

The device in which the new tensor will be stored. If not specified, "cpu" will be used. If an out tensor is specified, then device is expected as None.

None

Returns:

Type Description
Tensor

The created or modified tensor after placing NaN values.

Source code in evotorch/tools/misc.py
def make_nan(
    *size: Size,
    out: Optional[torch.Tensor] = None,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
) -> torch.Tensor:
    """
    Make a new tensor filled with NaN, or fill an existing tensor with NaN.

    The following example creates a float32 tensor filled with NaN values,
    of shape (3, 5):

        nan_values = make_nan(3, 5, dtype="float32")

    The following example fills an existing tensor with NaNs.

        make_nan(out=existing_tensor)

    Args:
        size: Size of the new tensor to be filled with NaNs.
            This can be given as multiple positional arguments, each such
            positional argument being an integer, or as a single positional
            argument of a tuple, the tuple containing multiple integers.
            Note that, if the user wishes to fill an existing tensor with
            NaN values, then no positional argument is expected.
        out: Optionally, the tensor to be filled by NaN values.
            If an `out` tensor is given, then no `size` argument is expected.
        dtype: Optionally a string (e.g. "float32") or a PyTorch dtype
            (e.g. torch.float32).
            If `dtype` is not specified, the default choice of
            `torch.empty(...)` is used, that is, `torch.float32`.
            If an `out` tensor is specified, then `dtype` is expected
            as None.
        device: The device in which the new tensor will be stored.
            If not specified, "cpu" will be used.
            If an `out` tensor is specified, then `device` is expected
            as None.
    Returns:
        The created or modified tensor after placing NaN values.
    """
    if _scalar_requested(*size):
        return _scalar_tensor(float("nan"), out=out, dtype=dtype, device=device)
    else:
        out = _out_tensor(*size, out=out, dtype=dtype, device=device)
        out[:] = float("nan")
        return out

make_ones(*size, *, out=None, dtype=None, device=None)

Make a new tensor filled with 1, or fill an existing tensor with 1.

The following example creates a float32 tensor filled with 1 values, of shape (3, 5):

zero_values = make_ones(3, 5, dtype="float32")

The following example fills an existing tensor with 1s:

make_ones(out=existing_tensor)

Parameters:

Name Type Description Default
size Union[int, torch.Size]

Size of the new tensor to be filled with 1. This can be given as multiple positional arguments, each such positional argument being an integer, or as a single positional argument of a tuple, the tuple containing multiple integers. Note that, if the user wishes to fill an existing tensor with 1 values, then no positional argument is expected.

()
out Optional[torch.Tensor]

Optionally, the tensor to be filled by 1 values. If an out tensor is given, then no size argument is expected.

None
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "float32") or a PyTorch dtype (e.g. torch.float32). If dtype is not specified, the default choice of torch.empty(...) is used, that is, torch.float32. If an out tensor is specified, then dtype is expected as None.

None
device Union[str, torch.device]

The device in which the new tensor will be stored. If not specified, "cpu" will be used. If an out tensor is specified, then device is expected as None.

None

Returns:

Type Description
Tensor

The created or modified tensor after placing 1 values.

Source code in evotorch/tools/misc.py
def make_ones(
    *size: Size,
    out: Optional[torch.Tensor] = None,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
) -> torch.Tensor:
    """
    Make a new tensor filled with 1, or fill an existing tensor with 1.

    The following example creates a float32 tensor filled with 1 values,
    of shape (3, 5):

        zero_values = make_ones(3, 5, dtype="float32")

    The following example fills an existing tensor with 1s:

        make_ones(out=existing_tensor)

    Args:
        size: Size of the new tensor to be filled with 1.
            This can be given as multiple positional arguments, each such
            positional argument being an integer, or as a single positional
            argument of a tuple, the tuple containing multiple integers.
            Note that, if the user wishes to fill an existing tensor with
            1 values, then no positional argument is expected.
        out: Optionally, the tensor to be filled by 1 values.
            If an `out` tensor is given, then no `size` argument is expected.
        dtype: Optionally a string (e.g. "float32") or a PyTorch dtype
            (e.g. torch.float32).
            If `dtype` is not specified, the default choice of
            `torch.empty(...)` is used, that is, `torch.float32`.
            If an `out` tensor is specified, then `dtype` is expected
            as None.
        device: The device in which the new tensor will be stored.
            If not specified, "cpu" will be used.
            If an `out` tensor is specified, then `device` is expected
            as None.
    Returns:
        The created or modified tensor after placing 1 values.
    """
    if _scalar_requested(*size):
        return _scalar_tensor(1, out=out, dtype=dtype, device=device)
    else:
        out = _out_tensor(*size, out=out, dtype=dtype, device=device)
        out[:] = 1
        return out

make_randint(*size, *, n, out=None, dtype=None, device=None, generator=None)

Make a new or existing tensor filled by random integers. The integers are uniformly distributed within [0 ... n-1]. This function can be used with integer or float dtypes.

Parameters:

Name Type Description Default
size Union[int, torch.Size]

Size of the new tensor to be filled with uniformly distributed values. This can be given as multiple positional arguments, each such positional argument being an integer, or as a single positional argument of a tuple, the tuple containing multiple integers. Note that, if the user wishes to fill an existing tensor instead, then no positional argument is expected.

()
n Union[int, float, torch.Tensor]

Number of choice(s) for integer sampling. The lowest possible value will be 0, and the highest possible value will be n - 1. n can be a scalar, or a tensor.

required
out Optional[torch.Tensor]

Optionally, the tensor to be filled by the random integers. If an out tensor is given, then no size argument is expected.

None
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "int64") or a PyTorch dtype (e.g. torch.int64). If dtype is not specified, torch.int64 will be used.

None
device Union[str, torch.device]

The device in which the new tensor will be stored. If not specified, "cpu" will be used. If an out tensor is specified, then device is expected as None.

None
generator Any

Pseudo-random number generator to be used when sampling the values. Can be a torch.Generator, or an object with a generator attribute (such as Problem). If left as None, the global generator of PyTorch will be used.

None

Returns:

Type Description
Tensor

The created or modified tensor after placing the uniformly distributed values.

Source code in evotorch/tools/misc.py
def make_randint(
    *size: Size,
    n: Union[int, float, torch.Tensor],
    out: Optional[torch.Tensor] = None,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
    generator: Any = None,
) -> torch.Tensor:
    """
    Make a new or existing tensor filled by random integers.
    The integers are uniformly distributed within `[0 ... n-1]`.
    This function can be used with integer or float dtypes.

    Args:
        size: Size of the new tensor to be filled with uniformly distributed
            values. This can be given as multiple positional arguments, each
            such positional argument being an integer, or as a single
            positional argument of a tuple, the tuple containing multiple
            integers. Note that, if the user wishes to fill an existing
            tensor instead, then no positional argument is expected.
        n: Number of choice(s) for integer sampling.
            The lowest possible value will be 0, and the highest possible
            value will be n - 1.
            `n` can be a scalar, or a tensor.
        out: Optionally, the tensor to be filled by the random integers.
            If an `out` tensor is given, then no `size` argument is
            expected.
        dtype: Optionally a string (e.g. "int64") or a PyTorch dtype
            (e.g. torch.int64).
            If `dtype` is not specified, torch.int64 will be used.
        device: The device in which the new tensor will be stored.
            If not specified, "cpu" will be used.
            If an `out` tensor is specified, then `device` is expected
            as None.
        generator: Pseudo-random number generator to be used when sampling
            the values. Can be a `torch.Generator`, or an object with
            a `generator` attribute (such as `Problem`).
            If left as None, the global generator of PyTorch will be used.
    Returns:
            The created or modified tensor after placing the uniformly
            distributed values.
    """
    scalar_requested = _scalar_requested(*size)
    if scalar_requested:
        size = (1,)

    if (dtype is None) and (out is None):
        dtype = torch.int64
    out = _out_tensor(*size, out=out, dtype=dtype, device=device)
    gen_kwargs = _generator_kwargs(generator)
    out.random_(**gen_kwargs)
    out %= n

    if scalar_requested:
        out = out[0]

    return out

make_tensor(data, *, dtype=None, device=None, read_only=False)

Make a new tensor.

This function can be used to create PyTorch tensors, or ObjectArray instances with or without read-only behavior.

The following example creates a 2-dimensional PyTorch tensor:

my_tensor = make_tensor(
    [[1, 2], [3, 4]],
    dtype="float32",    # alternatively, torch.float32
    device="cpu",
)

The following example creates an ObjectArray from a list that contains arbitrary data:

my_obj_tensor = make_tensor(["a_string", (1, 2)], dtype=object)

Parameters:

Name Type Description Default
data Any

The data to be converted to a tensor. If one wishes to create a PyTorch tensor, this can be anything that can be stored by a PyTorch tensor. If one wishes to create an ObjectArray and therefore passes dtype=object, then the provided data is expected as an Iterable.

required
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "float32"), or a PyTorch dtype (e.g. torch.float32), or object or "object" (as a string) or Any if one wishes to create an ObjectArray. If dtype is not specified, it will be assumed that the user wishes to create a PyTorch tensor (not an ObjectArray) and then dtype will be inferred from the provided data (according to the default behavior of PyTorch).

None
device Union[str, torch.device]

The device in which the tensor will be stored. If device is not specified, it will be understood from the given data (according to the default behavior of PyTorch).

None
read_only bool

Whether or not the created tensor will be read-only. By default, this is False.

False

Returns:

Type Description
Iterable

A PyTorch tensor or an ObjectArray.

Source code in evotorch/tools/misc.py
def make_tensor(
    data: Any,
    *,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
    read_only: bool = False,
) -> Iterable:
    """
    Make a new tensor.

    This function can be used to create PyTorch tensors, or ObjectArray
    instances with or without read-only behavior.

    The following example creates a 2-dimensional PyTorch tensor:

        my_tensor = make_tensor(
            [[1, 2], [3, 4]],
            dtype="float32",    # alternatively, torch.float32
            device="cpu",
        )

    The following example creates an ObjectArray from a list that contains
    arbitrary data:

        my_obj_tensor = make_tensor(["a_string", (1, 2)], dtype=object)

    Args:
        data: The data to be converted to a tensor.
            If one wishes to create a PyTorch tensor, this can be anything
            that can be stored by a PyTorch tensor.
            If one wishes to create an `ObjectArray` and therefore passes
            `dtype=object`, then the provided `data` is expected as an
            `Iterable`.
        dtype: Optionally a string (e.g. "float32"), or a PyTorch dtype
            (e.g. torch.float32), or `object` or "object" (as a string)
            or `Any` if one wishes to create an `ObjectArray`.
            If `dtype` is not specified, it will be assumed that the user
            wishes to create a PyTorch tensor (not an `ObjectArray`) and
            then `dtype` will be inferred from the provided `data`
            (according to the default behavior of PyTorch).
        device: The device in which the tensor will be stored.
            If `device` is not specified, it will be understood from the
            given `data` (according to the default behavior of PyTorch).
        read_only: Whether or not the created tensor will be read-only.
            By default, this is False.
    Returns:
        A PyTorch tensor or an ObjectArray.
    """
    from .objectarray import ObjectArray
    from .readonlytensor import as_read_only_tensor

    if (dtype is not None) and is_dtype_object(dtype):
        if not hasattr(data, "__len__"):
            data = list(data)
        n = len(data)
        result = ObjectArray(n)
        result[:] = data
    else:
        kwargs = {}
        if dtype is not None:
            kwargs["dtype"] = to_torch_dtype(dtype)
        if device is not None:
            kwargs["device"] = device
        result = torch.tensor(data, **kwargs)

    if read_only:
        result = as_read_only_tensor(result)

    return result

make_uniform(*size, *, lb=None, ub=None, out=None, dtype=None, device=None, generator=None)

Make a new or existing tensor filled by uniformly distributed values. Both lower and upper bounds are inclusive. This function can work with both float and int dtypes.

Parameters:

Name Type Description Default
size Union[int, torch.Size]

Size of the new tensor to be filled with uniformly distributed values. This can be given as multiple positional arguments, each such positional argument being an integer, or as a single positional argument of a tuple, the tuple containing multiple integers. Note that, if the user wishes to fill an existing tensor instead, then no positional argument is expected.

()
lb Union[float, Iterable[float], torch.Tensor]

Lower bound for the uniformly distributed values. Can be a scalar, or a tensor. If not specified, the lower bound will be taken as 0. Note that, if one specifies lb, then ub is also expected to be explicitly specified.

None
ub Union[float, Iterable[float], torch.Tensor]

Upper bound for the uniformly distributed values. Can be a scalar, or a tensor. If not specified, the upper bound will be taken as 1. Note that, if one specifies ub, then lb is also expected to be explicitly specified.

None
out Optional[torch.Tensor]

Optionally, the tensor to be filled by uniformly distributed values. If an out tensor is given, then no size argument is expected.

None
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "float32") or a PyTorch dtype (e.g. torch.float32). If dtype is not specified, the default choice of torch.empty(...) is used, that is, torch.float32. If an out tensor is specified, then dtype is expected as None.

None
device Union[str, torch.device]

The device in which the new tensor will be stored. If not specified, "cpu" will be used. If an out tensor is specified, then device is expected as None.

None
generator Any

Pseudo-random number generator to be used when sampling the values. Can be a torch.Generator, or an object with a generator attribute (such as Problem). If left as None, the global generator of PyTorch will be used.

None

Returns:

Type Description
Tensor

The created or modified tensor after placing the uniformly distributed values.

Source code in evotorch/tools/misc.py
def make_uniform(
    *size: Size,
    lb: Optional[RealOrVector] = None,
    ub: Optional[RealOrVector] = None,
    out: Optional[torch.Tensor] = None,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
    generator: Any = None,
) -> torch.Tensor:
    """
    Make a new or existing tensor filled by uniformly distributed values.
    Both lower and upper bounds are inclusive.
    This function can work with both float and int dtypes.

    Args:
        size: Size of the new tensor to be filled with uniformly distributed
            values. This can be given as multiple positional arguments, each
            such positional argument being an integer, or as a single
            positional argument of a tuple, the tuple containing multiple
            integers. Note that, if the user wishes to fill an existing
            tensor instead, then no positional argument is expected.
        lb: Lower bound for the uniformly distributed values.
            Can be a scalar, or a tensor.
            If not specified, the lower bound will be taken as 0.
            Note that, if one specifies `lb`, then `ub` is also expected to
            be explicitly specified.
        ub: Upper bound for the uniformly distributed values.
            Can be a scalar, or a tensor.
            If not specified, the upper bound will be taken as 1.
            Note that, if one specifies `ub`, then `lb` is also expected to
            be explicitly specified.
        out: Optionally, the tensor to be filled by uniformly distributed
            values. If an `out` tensor is given, then no `size` argument is
            expected.
        dtype: Optionally a string (e.g. "float32") or a PyTorch dtype
            (e.g. torch.float32).
            If `dtype` is not specified, the default choice of
            `torch.empty(...)` is used, that is, `torch.float32`.
            If an `out` tensor is specified, then `dtype` is expected
            as None.
        device: The device in which the new tensor will be stored.
            If not specified, "cpu" will be used.
            If an `out` tensor is specified, then `device` is expected
            as None.
        generator: Pseudo-random number generator to be used when sampling
            the values. Can be a `torch.Generator`, or an object with
            a `generator` attribute (such as `Problem`).
            If left as None, the global generator of PyTorch will be used.
    Returns:
        The created or modified tensor after placing the uniformly
        distributed values.
    """

    scalar_requested = _scalar_requested(*size)
    if scalar_requested:
        size = (1,)

    def _invalid_bound_args():
        raise ValueError(
            f"Expected both `lb` and `ub` as None, or both `lb` and `ub` as not None."
            f" It appears that one of them is None, while the other is not."
            f" lb: {repr(lb)}."
            f" ub: {repr(ub)}."
        )

    out = _out_tensor(*size, out=out, dtype=dtype, device=device)
    gen_kwargs = _generator_kwargs(generator)

    def _cast_bounds():
        nonlocal lb, ub
        lb = torch.as_tensor(lb, dtype=out.dtype, device=out.device)
        ub = torch.as_tensor(ub, dtype=out.dtype, device=out.device)

    if out.dtype == torch.bool:
        out.random_(**gen_kwargs)
        if (lb is None) and (ub is None):
            pass  # nothing to do
        elif (lb is not None) and (ub is not None):
            _cast_bounds()
            lb_shape_matches = lb.shape == out.shape
            ub_shape_matches = ub.shape == out.shape
            if (not lb_shape_matches) or (not ub_shape_matches):
                all_false = torch.zeros_like(out)
                if not lb_shape_matches:
                    lb = lb | all_false
                if not ub_shape_matches:
                    ub = ub | all_false
            mask_for_always_false = (~lb) & (~ub)
            mask_for_always_true = lb & ub
            out[mask_for_always_false] = False
            out[mask_for_always_true] = True
        else:
            _invalid_bound_args()
    elif out.dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64):
        out.random_(**gen_kwargs)
        if (lb is None) and (ub is None):
            out %= 2
        elif (lb is not None) and (ub is not None):
            _cast_bounds()
            diff = (ub - lb) + 1
            out -= lb
            out %= diff
            out += lb
        else:
            _invalid_bound_args()
    else:
        out.uniform_(**gen_kwargs)
        if (lb is None) and (ub is None):
            pass  # nothing to do
        elif (lb is not None) and (ub is not None):
            _cast_bounds()
            diff = ub - lb
            out *= diff
            out += lb
        else:
            _invalid_bound_args()

    if scalar_requested:
        out = out[0]

    return out

make_zeros(*size, *, out=None, dtype=None, device=None)

Make a new tensor filled with 0, or fill an existing tensor with 0.

The following example creates a float32 tensor filled with 0 values, of shape (3, 5):

zero_values = make_zeros(3, 5, dtype="float32")

The following example fills an existing tensor with 0s:

make_zeros(out=existing_tensor)

Parameters:

Name Type Description Default
size Union[int, torch.Size]

Size of the new tensor to be filled with 0. This can be given as multiple positional arguments, each such positional argument being an integer, or as a single positional argument of a tuple, the tuple containing multiple integers. Note that, if the user wishes to fill an existing tensor with 0 values, then no positional argument is expected.

()
out Optional[torch.Tensor]

Optionally, the tensor to be filled by 0 values. If an out tensor is given, then no size argument is expected.

None
dtype Union[str, torch.dtype, numpy.dtype, Type]

Optionally a string (e.g. "float32") or a PyTorch dtype (e.g. torch.float32). If dtype is not specified, the default choice of torch.empty(...) is used, that is, torch.float32. If an out tensor is specified, then dtype is expected as None.

None
device Union[str, torch.device]

The device in which the new tensor will be stored. If not specified, "cpu" will be used. If an out tensor is specified, then device is expected as None.

None

Returns:

Type Description
Tensor

The created or modified tensor after placing 0 values.

Source code in evotorch/tools/misc.py
def make_zeros(
    *size: Size,
    out: Optional[torch.Tensor] = None,
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
) -> torch.Tensor:
    """
    Make a new tensor filled with 0, or fill an existing tensor with 0.

    The following example creates a float32 tensor filled with 0 values,
    of shape (3, 5):

        zero_values = make_zeros(3, 5, dtype="float32")

    The following example fills an existing tensor with 0s:

        make_zeros(out=existing_tensor)

    Args:
        size: Size of the new tensor to be filled with 0.
            This can be given as multiple positional arguments, each such
            positional argument being an integer, or as a single positional
            argument of a tuple, the tuple containing multiple integers.
            Note that, if the user wishes to fill an existing tensor with
            0 values, then no positional argument is expected.
        out: Optionally, the tensor to be filled by 0 values.
            If an `out` tensor is given, then no `size` argument is expected.
        dtype: Optionally a string (e.g. "float32") or a PyTorch dtype
            (e.g. torch.float32).
            If `dtype` is not specified, the default choice of
            `torch.empty(...)` is used, that is, `torch.float32`.
            If an `out` tensor is specified, then `dtype` is expected
            as None.
        device: The device in which the new tensor will be stored.
            If not specified, "cpu" will be used.
            If an `out` tensor is specified, then `device` is expected
            as None.
    Returns:
        The created or modified tensor after placing 0 values.
    """
    if _scalar_requested(*size):
        return _scalar_tensor(0, out=out, dtype=dtype, device=device)
    else:
        out = _out_tensor(*size, out=out, dtype=dtype, device=device)
        out.zero_()
        return out

message_from(sender, message)

Prepend the sender object's name and id to a string message.

Let us imagine that we have a class named Example:

from evotorch.tools import message_from


class Example:
    def say_hello(self):
        print(message_from(self, "Hello!"))

Let us now instantiate this class and use its say_hello method:

ex = Example()
ex.say_hello()

The output becomes something like this:

Instance of `Example` (id:...) -- Hello!

Parameters:

Name Type Description Default
sender object

The object which produces the message

required
message Any

The message, as something that can be converted to string

required

Returns:

Type Description
str

The new message string, with the details regarding the sender object inserted to the beginning.

Source code in evotorch/tools/misc.py
def message_from(sender: object, message: Any) -> str:
    """
    Prepend the sender object's name and id to a string message.

    Let us imagine that we have a class named `Example`:

    ```python
    from evotorch.tools import message_from


    class Example:
        def say_hello(self):
            print(message_from(self, "Hello!"))
    ```

    Let us now instantiate this class and use its `say_hello` method:

    ```python
    ex = Example()
    ex.say_hello()
    ```

    The output becomes something like this:

    ```
    Instance of `Example` (id:...) -- Hello!
    ```

    Args:
        sender: The object which produces the message
        message: The message, as something that can be converted to string
    Returns:
        The new message string, with the details regarding the sender object
        inserted to the beginning.
    """
    sender_type = type(sender).__name__
    sender_id = id(sender)

    return f"Instance of `{sender_type}` (id:{sender_id}) -- {message}"

modify_tensor(original, target, lb=None, ub=None, max_change=None, in_place=False)

Return the modified version of the original tensor, with bounds checking.

Parameters:

Name Type Description Default
original Tensor

The original tensor.

required
target Tensor

The target tensor which contains the values to replace the old ones in the original tensor.

required
lb Union[float, torch.Tensor]

The lower bound(s), as a scalar or as an tensor. Values below these bounds are clipped in the resulting tensor. None means -inf.

None
ub Union[float, torch.Tensor]

The upper bound(s), as a scalar or as an tensor. Value above these bounds are clipped in the resulting tensor. None means +inf.

None
max_change Union[float, torch.Tensor]

The ratio of allowed change. In more details, when given as a real number r, modifications are allowed only within [original-(r*abs(original)) ... original+(r*abs(original))]. Modifications beyond this interval are clipped. This argument can also be left as None if no such limitation is needed.

None
in_place bool

Provide this as True if you wish the modification to be done within the original tensor. The default value of this argument is False, which means, the original tensor is not changed, and its modified version is returned as an independent copy.

False

Returns:

Type Description
Tensor

The modified tensor.

Source code in evotorch/tools/misc.py
@torch.no_grad()
def modify_tensor(
    original: torch.Tensor,
    target: torch.Tensor,
    lb: Optional[Union[float, torch.Tensor]] = None,
    ub: Optional[Union[float, torch.Tensor]] = None,
    max_change: Optional[Union[float, torch.Tensor]] = None,
    in_place: bool = False,
) -> torch.Tensor:
    """Return the modified version of the original tensor, with bounds checking.

    Args:
        original: The original tensor.
        target: The target tensor which contains the values to replace the
            old ones in the original tensor.
        lb: The lower bound(s), as a scalar or as an tensor.
            Values below these bounds are clipped in the resulting tensor.
            None means -inf.
        ub: The upper bound(s), as a scalar or as an tensor.
            Value above these bounds are clipped in the resulting tensor.
            None means +inf.
        max_change: The ratio of allowed change.
            In more details, when given as a real number r,
            modifications are allowed only within
            ``[original-(r*abs(original)) ... original+(r*abs(original))]``.
            Modifications beyond this interval are clipped.
            This argument can also be left as None if no such limitation
            is needed.
        in_place: Provide this as True if you wish the modification to be
            done within the original tensor. The default value of this
            argument is False, which means, the original tensor is not
            changed, and its modified version is returned as an independent
            copy.
    Returns:
        The modified tensor.
    """
    if (lb is None) and (ub is None) and (max_change is None):
        # If there is no restriction regarding how the tensor
        # should be modified (no lb, no ub, no max_change),
        # then we simply use the target values
        # themselves for modifying the tensor.

        if in_place:
            original[:] = target
            return original
        else:
            return target
    else:
        # If there are some restriction regarding how the tensor
        # should be modified, then we turn to the following
        # operations

        def convert_to_tensor(x, tensorname: str):
            if isinstance(x, torch.Tensor):
                converted = x
            else:
                converted = torch.as_tensor(x, dtype=original.dtype, device=original.device)
            if converted.ndim == 0 or converted.shape == original.shape:
                return converted
            else:
                raise IndexError(
                    f"Argument {tensorname}: shape mismatch."
                    f" Shape of the original tensor: {original.shape}."
                    f" Shape of {tensorname}: {converted.shape}."
                )

        if lb is None:
            # If lb is None, then it should be taken as -inf
            lb = convert_to_tensor(float("-inf"), "lb")
        else:
            lb = convert_to_tensor(lb, "lb")

        if ub is None:
            # If ub is None, then it should be taken as +inf
            ub = convert_to_tensor(float("inf"), "ub")
        else:
            ub = convert_to_tensor(ub, "ub")

        if max_change is not None:
            # If max_change is provided as something other than None,
            # then we update the lb and ub so that they are tight
            # enough to satisfy the max_change restriction.

            max_change = convert_to_tensor(max_change, "max_change")
            allowed_amounts = torch.abs(original) * max_change
            allowed_lb = original - allowed_amounts
            allowed_ub = original + allowed_amounts
            lb = torch.max(lb, allowed_lb)
            ub = torch.min(ub, allowed_ub)

        ## If in_place is given as True, the clipping (that we are about
        ## to perform), should be in-place.
        # more_config = {}
        # if in_place:
        #    more_config['out'] = original
        #
        ## Return the clipped version of the target values
        # return torch.clamp(target, lb, ub, **more_config)

        result = torch.max(target, lb)
        result = torch.min(result, ub)

        if in_place:
            original[:] = result
            return original
        else:
            return result

numpy_copy(x, dtype=None)

Return a numpy copy of the given iterable.

The newly created numpy array will be mutable, even if the original iterable object is read-only.

Parameters:

Name Type Description Default
x Iterable

Any Iterable whose numpy copy will be returned.

required
dtype Union[str, torch.dtype, numpy.dtype, Type]

The desired dtype. Can be given as a numpy dtype, as a torch dtype, or a native dtype (e.g. int, float), or as a string (e.g. "float32"). If left as None, dtype will be determined according to the data contained by the original iterable object.

None

Returns:

Type Description
ndarray

The numpy copy of the original iterable object.

Source code in evotorch/tools/misc.py
def numpy_copy(x: Iterable, dtype: Optional[DType] = None) -> np.ndarray:
    """
    Return a numpy copy of the given iterable.

    The newly created numpy array will be mutable, even if the
    original iterable object is read-only.

    Args:
        x: Any Iterable whose numpy copy will be returned.
        dtype: The desired dtype. Can be given as a numpy dtype,
            as a torch dtype, or a native dtype (e.g. int, float),
            or as a string (e.g. "float32").
            If left as None, dtype will be determined according
            to the data contained by the original iterable object.
    Returns:
        The numpy copy of the original iterable object.
    """
    from .objectarray import ObjectArray

    needs_casting = dtype is not None

    if isinstance(x, ObjectArray):
        result = x.numpy()
    elif isinstance(x, torch.Tensor):
        result = x.cpu().clone().numpy()
    elif isinstance(x, np.ndarray):
        result = x.copy()
    else:
        needs_casting = False
        result = np.array(x, dtype=dtype)

    if needs_casting:
        result = result.astype(dtype)

    return result

pass_info_if_needed(f, info)

Pass additional arguments into a callable, the info dictionary is unpacked and passed as additional keyword arguments only if the policy is decorated with the pass_info decorator.

Parameters:

Name Type Description Default
f Callable

The callable to be called.

required
info Dict[str, Any]

The info to be passed to the callable.

required

Returns:

Type Description
Callable

The callable with extra arguments

Exceptions:

Type Description
TypeError

If the callable is decorated with the pass_info decorator, but its signature does not match the expected signature.

Source code in evotorch/tools/misc.py
def pass_info_if_needed(f: Callable, info: Dict[str, Any]) -> Callable:
    """
    Pass additional arguments into a callable, the info dictionary is unpacked
    and passed as additional keyword arguments only if the policy is decorated
    with the [pass_info][evotorch.decorators.pass_info] decorator.

    Args:
        f (Callable): The callable to be called.
        info (Dict[str, Any]): The info to be passed to the callable.
    Returns:
        Callable: The callable with extra arguments
    Raises:
        TypeError: If the callable is decorated with the [pass_info][evotorch.decorators.pass_info] decorator,
            but its signature does not match the expected signature.
    """
    if hasattr(f, "__evotorch_pass_info__"):
        try:
            sig = inspect.signature(f)
            sig.bind_partial(**info)
        except TypeError:
            raise TypeError(
                "Callable {f} is decorated with @pass_info, but it doesn't expect some of the extra arguments "
                f"({', '.join(info.keys())}). Hint: maybe you forgot to add **kwargs to the function signature?"
            )
        except Exception:
            pass

        return functools.partial(f, **info)
    else:
        return f

set_default_logger_config(logger_name='evotorch', logger_level=20, show_process=True, show_lineno=False, override=False)

Configure the "EvoTorch" Python logger to print to the console with default format.

The logger will be configured to print to all messages with level INFO or lower to stdout and all messages with level WARNING or higher to stderr.

The default format is:

[2022-11-23 22:28:47] INFO     <75935>   evotorch:      This is a log message
{asctime}             {level}  {process} {logger_name}: {message}
The format can be slightly customized by passing show_process=False to hide Process ID or show_lineno=True to show the filename and line number of the log message instead of the Logger Name.

This function should be called before any other logging is performed, otherwise the default configuration will not be applied. If the logger is already configured, this function will do nothing unless override=True is passed, in which case the logger will be reconfigured.

Parameters:

Name Type Description Default
logger_name str

Name of the logger to configure.

'evotorch'
logger_level int

Level of the logger to configure.

20
show_process bool

Whether to show the process name in the log message.

True
show_lineno bool

Whether to show the filename with the line number in the log message or just the name of the logger.

False
override bool

Whether to override the logger configuration if it has already been configured.

False
Source code in evotorch/tools/misc.py
def set_default_logger_config(
    logger_name: str = "evotorch",
    logger_level: int = logging.INFO,
    show_process: bool = True,
    show_lineno: bool = False,
    override: bool = False,
):
    """
    Configure the "EvoTorch" Python logger to print to the console with default format.

    The logger will be configured to print to all messages with level INFO or lower to stdout and all
    messages with level WARNING or higher to stderr.

    The default format is:
    ```
    [2022-11-23 22:28:47] INFO     <75935>   evotorch:      This is a log message
    {asctime}             {level}  {process} {logger_name}: {message}
    ```
    The format can be slightly customized by passing `show_process=False` to hide Process ID or `show_lineno=True` to
    show the filename and line number of the log message instead of the Logger Name.

    This function should be called before any other logging is performed, otherwise the default configuration will
    not be applied. If the logger is already configured, this function will do nothing unless `override=True` is passed,
    in which case the logger will be reconfigured.

    Args:
        logger_name: Name of the logger to configure.
        logger_level: Level of the logger to configure.
        show_process: Whether to show the process name in the log message.
        show_lineno: Whether to show the filename with the line number in the log message or just the name of the logger.
        override: Whether to override the logger configuration if it has already been configured.
    """
    logger = logging.getLogger(logger_name)

    if not override and logger.hasHandlers():
        # warn user that the logger is already configured
        logger.warning(
            "The logger is already configured. "
            "The default configuration will not be applied. "
            "Call `set_default_logger_config` with `override=True` to override the current configuration."
        )
        return
    elif override:
        # remove all handlers
        for handler in logger.handlers:
            logger.removeHandler(handler)

    logger.setLevel(logger_level)
    logger.propagate = False

    formatter = logging.Formatter(
        "[{asctime}] "
        + "{levelname:<8s} "
        + ("<{process:5d}> " if show_process else "")
        + ("{filename}:{lineno}: " if show_lineno else "{name}: ")
        + "{message}",
        datefmt="%Y-%m-%d %H:%M:%S",
        style="{",
    )

    _stdout_handler = logging.StreamHandler(sys.stdout)
    _stdout_handler.addFilter(lambda log_record: log_record.levelno < logging.WARNING)
    _stdout_handler.setFormatter(formatter)
    logger.addHandler(_stdout_handler)

    _stderr_handler = logging.StreamHandler(sys.stderr)
    _stderr_handler.addFilter(lambda log_record: log_record.levelno >= logging.WARNING)
    _stderr_handler.setFormatter(formatter)
    logger.addHandler(_stderr_handler)

split_workload(workload, num_actors)

Split a workload among actors.

By "workload" what is meant is the total amount of a work, this amount being expressed by an integer. For example, if the "work" is the evaluation of a population, the "workload" would usually be the population size.

Parameters:

Name Type Description Default
workload int

Total amount of work, as an integer.

required
num_actors int

Number of actors (i.e. remote workers) among which the workload will be distributed.

required

Returns:

Type Description
list

A list of integers. The i-th item of the returned list expresses the suggested workload for the i-th actor.

Source code in evotorch/tools/misc.py
def split_workload(workload: int, num_actors: int) -> list:
    """
    Split a workload among actors.

    By "workload" what is meant is the total amount of a work,
    this amount being expressed by an integer.
    For example, if the "work" is the evaluation of a population,
    the "workload" would usually be the population size.

    Args:
        workload: Total amount of work, as an integer.
        num_actors: Number of actors (i.e. remote workers) among
            which the workload will be distributed.
    Returns:
        A list of integers. The i-th item of the returned list
        expresses the suggested workload for the i-th actor.
    """
    base_workload = workload // num_actors
    extra_workload = workload % num_actors
    result = [base_workload] * num_actors
    for i in range(extra_workload):
        result[i] += 1
    return result

stdev_from_radius(radius, solution_length)

Get elementwise standard deviation from a given radius.

Sometimes, for a distribution-based search algorithm, the user might choose to configure the initial coverage area of the search distribution not via standard deviation, but via a radius value, as was done in the study of Toklu et al. (2020). This function takes the desired radius value and the solution length of the problem at hand, and returns the elementwise standard deviation value. Let us name this returned standard deviation value as s. When a new Gaussian distribution is constructed such that its initial standard deviation is [s, s, s, ...] (the length of this vector being equal to the solution length), this constructed distribution's radius corresponds with the desired radius.

Here, the "radius" of a Gaussian distribution is defined as the norm of the standard deviation vector. In the case of a standard normal distribution, this radius formulation serves as a simplified approximation to E[||Normal(0, I)||] (for which a closer approximation is used in the study of Hansen & Ostermeier (2001)).

Reference:

Toklu, N.E., Liskowski, P., Srivastava, R.K. (2020).
ClipUp: A Simple and Powerful Optimizer
for Distribution-based Policy Evolution.
Parallel Problem Solving from Nature (PPSN 2020).

Nikolaus Hansen, Andreas Ostermeier (2001).
Completely Derandomized Self-Adaptation in Evolution Strategies.

Parameters:

Name Type Description Default
radius float

The radius whose elementwise standard deviation counterpart will be returned.

required
solution_length int

Length of a solution for the problem at hand.

required

Returns:

Type Description
float

An elementwise standard deviation value s, such that a Gaussian distribution constructed with the standard deviation [s, s, s, ...] has the desired radius.

Source code in evotorch/tools/misc.py
def stdev_from_radius(radius: float, solution_length: int) -> float:
    """
    Get elementwise standard deviation from a given radius.

    Sometimes, for a distribution-based search algorithm, the user might
    choose to configure the initial coverage area of the search distribution
    not via standard deviation, but via a radius value, as was done in the
    study of Toklu et al. (2020).
    This function takes the desired radius value and the solution length of
    the problem at hand, and returns the elementwise standard deviation value.
    Let us name this returned standard deviation value as `s`.
    When a new Gaussian distribution is constructed such that its initial
    standard deviation is `[s, s, s, ...]` (the length of this vector being
    equal to the solution length), this constructed distribution's radius
    corresponds with the desired radius.

    Here, the "radius" of a Gaussian distribution is defined as the norm
    of the standard deviation vector. In the case of a standard normal
    distribution, this radius formulation serves as a simplified approximation
    to `E[||Normal(0, I)||]` (for which a closer approximation is used in
    the study of Hansen & Ostermeier (2001)).

    Reference:

        Toklu, N.E., Liskowski, P., Srivastava, R.K. (2020).
        ClipUp: A Simple and Powerful Optimizer
        for Distribution-based Policy Evolution.
        Parallel Problem Solving from Nature (PPSN 2020).

        Nikolaus Hansen, Andreas Ostermeier (2001).
        Completely Derandomized Self-Adaptation in Evolution Strategies.

    Args:
        radius: The radius whose elementwise standard deviation counterpart
            will be returned.
        solution_length: Length of a solution for the problem at hand.
    Returns:
        An elementwise standard deviation value `s`, such that a Gaussian
        distribution constructed with the standard deviation `[s, s, s, ...]`
        has the desired radius.
    """
    radius = float(radius)
    solution_length = int(solution_length)
    return math.sqrt((radius**2) / solution_length)

storage_ptr(x)

Get the pointer to the underlying storage of a tensor of an ObjectArray.

Calling storage_ptr(x) is equivalent to x.untyped_storage().data_ptr().

Parameters:

Name Type Description Default
x Iterable

A regular PyTorch tensor, or a ReadOnlyTensor, or an ObjectArray.

required

Returns:

Type Description
int

The address of the underlying storage.

Source code in evotorch/tools/misc.py
def storage_ptr(x: Iterable) -> int:
    """
    Get the pointer to the underlying storage of a tensor of an ObjectArray.

    Calling `storage_ptr(x)` is equivalent to `x.untyped_storage().data_ptr()`.

    Args:
        x: A regular PyTorch tensor, or a ReadOnlyTensor, or an ObjectArray.
    Returns:
        The address of the underlying storage.
    """
    return _storage_ptr(x)

to_numpy_dtype(dtype)

Convert the given string or the given PyTorch dtype to a numpy dtype. If the argument is already a numpy dtype, then the argument is returned as it is.

Returns:

Type Description
dtype

The dtype, converted to a numpy dtype.

Source code in evotorch/tools/misc.py
def to_numpy_dtype(dtype: DType) -> np.dtype:
    """
    Convert the given string or the given PyTorch dtype to a numpy dtype.
    If the argument is already a numpy dtype, then the argument is returned
    as it is.

    Returns:
        The dtype, converted to a numpy dtype.
    """
    if isinstance(dtype, torch.dtype):
        return torch.tensor([], dtype=dtype).numpy().dtype
    elif is_dtype_object(dtype):
        return np.dtype(object)
    elif isinstance(dtype, np.dtype):
        return dtype
    else:
        return np.dtype(dtype)

to_stdev_init(*, solution_length, stdev_init=None, radius_init=None)

Ask for both standard deviation and radius, return the standard deviation.

It is very common among the distribution-based search algorithms to ask for both standard deviation and for radius for initializing the coverage area of the search distribution. During their initialization phases, these algorithms must check which one the user provided (radius or standard deviation), and return the result as the standard deviation so that a Gaussian distribution can easily be constructed.

This function serves as a helper function for such search algorithms by performing these actions:

  • If the user provided a standard deviation and not a radius, then this provided standard deviation is simply returned.
  • If the user provided a radius and not a standard deviation, then this provided radius is converted to its standard deviation counterpart, and then returned.
  • If both standard deviation and radius are missing, or they are both given at the same time, then an error is raised.

Parameters:

Name Type Description Default
solution_length int

Length of a solution for the problem at hand.

required
stdev_init Union[float, Iterable[float], torch.Tensor]

Standard deviation. If one wishes to provide a radius instead, then stdev_init is expected as None.

None
radius_init Union[float, Iterable[float], torch.Tensor]

Radius. If one wishes to provide a standard deviation instead, then radius_init is expected as None.

None

Returns:

Type Description
Union[float, Iterable[float], torch.Tensor]

The standard deviation for the search distribution to be constructed.

Source code in evotorch/tools/misc.py
def to_stdev_init(
    *,
    solution_length: int,
    stdev_init: Optional[RealOrVector] = None,
    radius_init: Optional[RealOrVector] = None,
) -> RealOrVector:
    """
    Ask for both standard deviation and radius, return the standard deviation.

    It is very common among the distribution-based search algorithms to ask
    for both standard deviation and for radius for initializing the coverage
    area of the search distribution. During their initialization phases,
    these algorithms must check which one the user provided (radius or
    standard deviation), and return the result as the standard deviation
    so that a Gaussian distribution can easily be constructed.

    This function serves as a helper function for such search algorithms
    by performing these actions:

    - If the user provided a standard deviation and not a radius, then this
      provided standard deviation is simply returned.
    - If the user provided a radius and not a standard deviation, then this
      provided radius is converted to its standard deviation counterpart,
      and then returned.
    - If both standard deviation and radius are missing, or they are both
      given at the same time, then an error is raised.

    Args:
        solution_length: Length of a solution for the problem at hand.
        stdev_init: Standard deviation. If one wishes to provide a radius
            instead, then `stdev_init` is expected as None.
        radius_init: Radius. If one wishes to provide a standard deviation
            instead, then `radius_init` is expected as None.
    Returns:
        The standard deviation for the search distribution to be constructed.
    """
    if (stdev_init is not None) and (radius_init is None):
        return stdev_init
    elif (stdev_init is None) and (radius_init is not None):
        return stdev_from_radius(radius_init, solution_length)
    elif (stdev_init is None) and (radius_init is None):
        raise ValueError(
            "Received both `stdev_init` and `radius_init` as None."
            " Please provide a value either for `stdev_init` or for `radius_init`."
        )
    else:
        raise ValueError(
            "Found both `stdev_init` and `radius_init` with values other than None."
            " Please provide a value either for `stdev_init` or for `radius_init`, but not for both."
        )

to_torch_dtype(dtype)

Convert the given string or the given numpy dtype to a PyTorch dtype. If the argument is already a PyTorch dtype, then the argument is returned as it is.

Returns:

Type Description
dtype

The dtype, converted to a PyTorch dtype.

Source code in evotorch/tools/misc.py
def to_torch_dtype(dtype: DType) -> torch.dtype:
    """
    Convert the given string or the given numpy dtype to a PyTorch dtype.
    If the argument is already a PyTorch dtype, then the argument is returned
    as it is.

    Returns:
        The dtype, converted to a PyTorch dtype.
    """
    if isinstance(dtype, str) and hasattr(torch, dtype):
        attrib_within_torch = getattr(torch, dtype)
    else:
        attrib_within_torch = None

    if isinstance(attrib_within_torch, torch.dtype):
        return attrib_within_torch
    elif isinstance(dtype, torch.dtype):
        return dtype
    elif dtype is Any or dtype is object:
        raise TypeError(f"Cannot make a numeric tensor with dtype {repr(dtype)}")
    else:
        return torch.from_numpy(np.array([], dtype=dtype)).dtype

objectarray

This module contains the ObjectArray class, which is an array-like data structure with an interface similar to PyTorch tensors, but with an ability to store arbitrary type of data (not just numbers).

ObjectArray (Sequence, RecursivePrintable)

An object container with an interface similar to PyTorch tensors.

It is strictly one-dimensional, and supports advanced indexing and slicing operations supported by PyTorch tensors.

An ObjectArray can store None values, strings, numbers, booleans, lists, sets, dictionaries, PyTorch tensors, and numpy arrays.

When a container (such as a list, dictionary, set, is placed into an ObjectArray, an immutable clone of this container is first created, and then this newly created immutable clone gets stored within the ObjectArray. This behavior is to prevent accidental modification of the stored data.

When a numeric array (such as a PyTorch tensor or a numpy array with a numeric dtype) is placed into an ObjectArray, the target ObjectArray first checks if the numeric array is read-only. If the numeric array is indeed read-only, then the array is put into the ObjectArray as it is. If the array is not read-only, then a read-only clone of the original numeric array is first created, and then this clone gets stored by the ObjectArray. This behavior has the following implications: (i) even when an ObjectArray is shared by multiple components of the program, the risk of accidental modification of the stored data through this shared ObjectArray is significantly reduced as the stored numeric arrays are read-only; (ii) although not recommended, one could still forcefully modify the numeric arrays stored by an ObjectArray by explicitly casting them as mutable arrays (in the case of a numpy array, one could forcefully set the WRITEABLE flag, and, in the case of a ReadOnlyTensor, one could forcefully cast it as a regular PyTorch tensor); (iii) if an already read-only array x is placed into an ObjectArray, but x shares its memory with a mutable array y, then the contents of the ObjectArray can be affected by modifying y. The implication (ii) is demonstrated as follows:

objs = ObjectArray(1)  # a single-element ObjectArray

# Place a numpy array into objs:
objs[0] = np.array([1, 2, 3], dtype=float)

# At this point, objs[0] is a read-only numpy array.
# objs[0] *= 2   # <- Not allowed

# Possible but NOT recommended:
objs.flags["WRITEABLE"] = True
objs[0] *= 2

The implication (iii) is demonstrated as follows:

objs = ObjectArray(1)  # a single-element ObjectArray

# Make a new mutable numpy array
y = np.array([1, 2, 3], dtype=float)

# Make a read-only view to y:
x = y[:]
x.flags["WRITEABLE"] = False

# Place x into objs.
objs[0] = x

# At this point, objs[0] is a read-only numpy array.
# objs[0] *= 2   # <- Not allowed

# During the operation of setting its 0-th item, the ObjectArray
# `objs` did not clone `x` because `x` was already read-only.
# However, the contents of `x` could actually be modified because
# `x` shares its memory with the mutable array `y`.

# Possible but NOT recommended:
y *= 2  # This affects both x and objs!

When a numpy array of dtype object is placed into an ObjectArray, a read-only ObjectArray copy of the original array will first be created, and then, this newly created ObjectArray will be stored by the outer ObjectArray.

An ObjectArray itself has a read-only mode, so that, in addition to its stored data, the ObjectArray itself can be protected against undesired modifications.

An interesting feature of PyTorch: if one slices a tensor A and the result is a new tensor B, and if B is sharing storage memory with A, then A.untyped_storage().data_ptr() and B.untyped_storage().data_ptr() will return the same pointer. This means, one can compare the storage pointers of A and B and see whether or not the two are sharing memory. ObjectArray was designed to have this exact behavior, so that one can understand if two ObjectArray instances are sharing memory. Note that NumPy does NOT have such a behavior. In more details, a NumPy array C and a NumPy array D could report different pointers even when D was created via a basic slicing operation on C.

Source code in evotorch/tools/objectarray.py
class ObjectArray(Sequence, RecursivePrintable):
    """
    An object container with an interface similar to PyTorch tensors.

    It is strictly one-dimensional, and supports advanced indexing and
    slicing operations supported by PyTorch tensors.

    An ObjectArray can store `None` values, strings, numbers, booleans,
    lists, sets, dictionaries, PyTorch tensors, and numpy arrays.

    When a container (such as a list, dictionary, set, is placed into an
    ObjectArray, an immutable clone of this container is first created, and
    then this newly created immutable clone gets stored within the
    ObjectArray. This behavior is to prevent accidental modification of the
    stored data.

    When a numeric array (such as a PyTorch tensor or a numpy array with a
    numeric dtype) is placed into an ObjectArray, the target ObjectArray
    first checks if the numeric array is read-only. If the numeric array
    is indeed read-only, then the array is put into the ObjectArray as it
    is. If the array is not read-only, then a read-only clone of the
    original numeric array is first created, and then this clone gets
    stored by the ObjectArray. This behavior has the following implications:
    (i) even when an ObjectArray is shared by multiple components of the
    program, the risk of accidental modification of the stored data through
    this shared ObjectArray is significantly reduced as the stored numeric
    arrays are read-only;
    (ii) although not recommended, one could still forcefully modify the
    numeric arrays stored by an ObjectArray by explicitly casting them as
    mutable arrays
    (in the case of a numpy array, one could forcefully set the WRITEABLE
    flag, and, in the case of a ReadOnlyTensor, one could forcefully cast it
    as a regular PyTorch tensor);
    (iii) if an already read-only array `x` is placed into an ObjectArray,
    but `x` shares its memory with a mutable array `y`, then the contents
    of the ObjectArray can be affected by modifying `y`.
    The implication (ii) is demonstrated as follows:

    ```python
    objs = ObjectArray(1)  # a single-element ObjectArray

    # Place a numpy array into objs:
    objs[0] = np.array([1, 2, 3], dtype=float)

    # At this point, objs[0] is a read-only numpy array.
    # objs[0] *= 2   # <- Not allowed

    # Possible but NOT recommended:
    objs.flags["WRITEABLE"] = True
    objs[0] *= 2
    ```

    The implication (iii) is demonstrated as follows:

    ```python
    objs = ObjectArray(1)  # a single-element ObjectArray

    # Make a new mutable numpy array
    y = np.array([1, 2, 3], dtype=float)

    # Make a read-only view to y:
    x = y[:]
    x.flags["WRITEABLE"] = False

    # Place x into objs.
    objs[0] = x

    # At this point, objs[0] is a read-only numpy array.
    # objs[0] *= 2   # <- Not allowed

    # During the operation of setting its 0-th item, the ObjectArray
    # `objs` did not clone `x` because `x` was already read-only.
    # However, the contents of `x` could actually be modified because
    # `x` shares its memory with the mutable array `y`.

    # Possible but NOT recommended:
    y *= 2  # This affects both x and objs!
    ```

    When a numpy array of dtype object is placed into an ObjectArray,
    a read-only ObjectArray copy of the original array will first be
    created, and then, this newly created ObjectArray will be stored
    by the outer ObjectArray.

    An ObjectArray itself has a read-only mode, so that, in addition to its
    stored data, the ObjectArray itself can be protected against undesired
    modifications.

    An interesting feature of PyTorch: if one slices a tensor A and the
    result is a new tensor B, and if B is sharing storage memory with A,
    then A.untyped_storage().data_ptr() and B.untyped_storage().data_ptr()
    will return the same pointer. This means, one can compare the storage
    pointers of A and B and see whether or not the two are sharing memory.
    ObjectArray was designed to have this exact behavior, so that one
    can understand if two ObjectArray instances are sharing memory.
    Note that NumPy does NOT have such a behavior. In more details,
    a NumPy array C and a NumPy array D could report different pointers
    even when D was created via a basic slicing operation on C.
    """

    def __init__(
        self,
        size: Optional[Size] = None,
        *,
        slice_of: Optional[tuple] = None,
    ):
        """
        `__init__(...)`: Instantiate a new ObjectArray.

        Args:
            size: Length of the ObjectArray. If this argument is present and
                is an integer `n`, then the resulting ObjectArray will be
                of length `n`, and will be filled with `None` values.
                This argument cannot be used together with the keyword
                argument `slice_of`.
            slice_of: Optionally a tuple in the form
                `(original_object_tensor, slice_info)`.
                When this argument is present, then the resulting ObjectArray
                will be a slice of the given `original_object_tensor` (which
                is expected as an ObjectArray instance). `slice_info` is
                either a `slice` instance, or a sequence of integers.
                The resulting ObjectArray might be a view of
                `original_object_tensor` (i.e. it might share its memory with
                `original_object_tensor`).
                This keyword argument cannot be used together with the
                argument `size`.
        """
        if size is not None and slice_of is not None:
            raise ValueError("Expected either `size` argument or `slice_of` argument, but got both.")
        elif size is None and slice_of is None:
            raise ValueError("Expected either `size` argument or `slice_of` argument, but got none.")
        elif size is not None:
            if not is_sequence(size):
                length = size
            elif isinstance(size, (np.ndarray, torch.Tensor)) and (size.ndim > 1):
                raise ValueError(f"Invalid size: {size}")
            else:
                [length] = size
            length = int(length)
            self._indices = torch.arange(length, dtype=torch.int64)
            self._objects = [None] * length
        elif slice_of is not None:
            source: ObjectArray

            source, slicing = slice_of

            if not isinstance(source, ObjectArray):
                raise TypeError(
                    f"`slice_of`: The first element was expected as an ObjectArray."
                    f" But it is of type {repr(type(source))}"
                )

            if isinstance(slicing, tuple) or is_integer(slicing):
                raise TypeError(f"Invalid slice: {slicing}")

            self._indices = source._indices[slicing]
            self._objects = source._objects

            if storage_ptr(self._indices) != storage_ptr(source._indices):
                self._objects = clone(self._objects)

        self._device = torch.device("cpu")
        self._read_only = False

    @property
    def shape(self) -> Size:
        """Shape of the ObjectArray, as a PyTorch Size tuple."""
        return self._indices.shape

    def size(self) -> Size:
        """
        Get the size of the ObjectArray, as a PyTorch Size tuple.

        Returns:
            The size (i.e. the shape) of the ObjectArray.
        """
        return self._indices.size()

    @property
    def ndim(self) -> int:
        """
        Number of dimensions handled by the ObjectArray.
        This is equivalent to getting the length of the size tuple.
        """
        return self._indices.ndim

    def dim(self) -> int:
        """
        Get the number of dimensions handled by the ObjectArray.
        This is equivalent to getting the length of the size tuple.

        Returns:
            The number of dimensions, as an integer.
        """
        return self._indices.dim()

    def numel(self) -> int:
        """
        Number of elements stored by the ObjectArray.

        Returns:
            The number of elements, as an integer.
        """
        return self._indices.numel()

    def repeat(self, *sizes) -> "ObjectArray":
        """
        Repeat the contents of this ObjectArray.

        For example, if we have an ObjectArray `objs` which stores
        `["hello", "world"]`, the following line:

            objs.repeat(3)

        will result in an ObjectArray which stores:

            `["hello", "world", "hello", "world", "hello", "world"]`

        Args:
            sizes: Although this argument is named `sizes` to be compatible
                with PyTorch, what is expected here is a single positional
                argument, as a single integer, or as a single-element
                tuple.
                The given integer (which can be the argument itself, or
                the integer within the given single-element tuple),
                specifies how many times the stored sequence will be
                repeated.
        Returns:
            A new ObjectArray which repeats the original one's values
        """

        if len(sizes) != 1:
            type_name = type(self).__name__
            raise ValueError(
                f"The `repeat(...)` method of {type_name} expects exactly one positional argument."
                f" This is because {type_name} supports only 1-dimensional storage."
                f" The received positional arguments are: {sizes}."
            )
        if isinstance(sizes, tuple):
            if len(sizes) == 1:
                sizes = sizes[0]
            else:
                type_name = type(self).__name__
                raise ValueError(
                    f"The `repeat(...)` method of {type_name} can accept a size tuple with only one element."
                    f" This is because {type_name} supports only 1-dimensional storage."
                    f" The received size tuple is: {sizes}."
                )
        num_repetitions = int(sizes[0])
        self_length = len(self)
        result = ObjectArray(num_repetitions * self_length)

        source_index = 0
        for result_index in range(len(result)):
            result[result_index] = self[source_index]
            source_index = (source_index + 1) % self_length

        return result

    @property
    def device(self) -> Device:
        """
        The device which stores the elements of the ObjectArray.
        In the case of ObjectArray, this property always returns
        the CPU device.

        Returns:
            The CPU device, as a torch.device object.
        """
        return self._device

    @property
    def dtype(self) -> DType:
        """
        The dtype of the elements stored by the ObjectArray.
        In the case of ObjectArray, the dtype is always `object`.
        """
        return object

    def __getitem__(self, i: Any) -> Any:
        if is_integer(i):
            index = int(self._indices[i])
            return self._objects[index]
        else:
            indices = self._indices[i]

            same_ptr = storage_ptr(indices) == storage_ptr(self._indices)

            result = ObjectArray(len(indices))

            if same_ptr:
                result._indices[:] = indices
                result._objects = self._objects
            else:
                result._objects = []
                for index in indices:
                    result._objects.append(self._objects[int(index)])

            result._read_only = self._read_only

            return result

    def __setitem__(self, i: Any, x: Any):
        self.set_item(i, x)

    def set_item(self, i: Any, x: Any, *, memo: Optional[dict] = None):
        """
        Set the i-th item of the ObjectArray as x.

        Args:
            i: An index or a slice.
            x: The object that will be put into the ObjectArray.
            memo: Optionally a dictionary which maps from the ids of the
                already placed objects to their clones within ObjectArray.
                In most scenarios, when this method is called from outside,
                this can be left as None.
        """
        from .immutable import as_immutable

        if memo is None:
            memo = {}

        memo[id(self)] = self

        if self._read_only:
            raise ValueError("This ObjectArray is read-only, therefore, modification is not allowed.")

        if is_integer(i):
            index = int(self._indices[i])
            self._objects[index] = as_immutable(x, memo=memo)
        else:
            indices = self._indices[i]
            if not isinstance(x, Iterable):
                raise TypeError(f"Expected an iterable, but got {repr(x)}")

            if indices.ndim != 1:
                raise ValueError(
                    "Received indices that would change the dimensionality of the ObjectArray."
                    " However, an ObjectArray can only be 1-dimensional."
                )

            slice_refers_to_whole_array = (len(indices) == len(self._indices)) and torch.all(indices == self._indices)
            if slice_refers_to_whole_array:
                memo[id(x)] = self

            if not hasattr(x, "__len__"):
                x = list(x)

            if len(x) != len(indices):
                raise TypeError(
                    f"The slicing operation refers to {len(indices)} elements."
                    f" However, the given objects sequence has {len(x)} elements."
                )

            for q, obj in enumerate(x):
                index = int(indices[q])
                self._objects[index] = as_immutable(obj, memo=memo)

    def __len__(self) -> int:
        return len(self._indices)

    def __iter__(self):
        for i in range(len(self)):
            yield self[i]

    def clone(self, *, preserve_read_only: bool = False, memo: Optional[dict] = None) -> Iterable:
        """
        Get a deep copy of the ObjectArray.

        Args:
            preserve_read_only: Whether or not to preserve the read-only
                attribute. Note that the default value is False, which
                means that the newly made clone will NOT be read-only
                even if the original ObjectArray is.
            memo: Optionally a dictionary which maps from the ids of the
                already cloned objects to their clones.
                In most scenarios, when this method is called from outside,
                this can be left as None.
        Returns:
            The clone of the original ObjectArray.
        """
        from .cloning import deep_clone

        if memo is None:
            memo = {}

        self_id = id(self)
        if self_id in memo:
            return memo[self_id]

        if not preserve_read_only:
            return self.numpy(memo=memo)
        else:
            result = ObjectArray(len(self))
            memo[self_id] = result

            for i, item in enumerate(self):
                result[i] = deep_clone(item, otherwise_deepcopy=True, memo=memo)

            return result

    def __copy__(self) -> "ObjectArray":
        return self.clone(preserve_read_only=True)

    def __deepcopy__(self, memo: Optional[dict]) -> "ObjectArray":
        if memo is None:
            memo = {}
        return self.clone(preserve_read_only=True, memo=memo)

    def __setstate__(self, state: dict):
        self.__dict__.update(state)

        # After pickling and unpickling, numpy arrays become mutable.
        # Since we are dealing with immutable containers here, we need to forcefully make all numpy arrays read-only.
        for v in self:
            if isinstance(v, np.ndarray):
                v.flags["WRITEABLE"] = False

    # def __getstate__(self) -> dict:
    #     from .cloning import deep_clone
    #     self_id = id(self)
    #     memo = {self_id: self}
    #     cloned_dict = deep_clone(self.__dict__, otherwise_deepcopy=True, memo=memo)
    #     return cloned_dict

    def get_read_only_view(self) -> "ObjectArray":
        """
        Get a read-only view of this ObjectArray.
        """
        result = self[:]
        result._read_only = True
        return result

    @property
    def is_read_only(self) -> bool:
        """
        True if this ObjectArray is read-only; False otherwise.
        """
        return self._read_only

    def storage(self) -> ObjectArrayStorage:
        return ObjectArrayStorage(self)

    def untyped_storage(self) -> ObjectArrayStorage:
        return ObjectArrayStorage(self)

    def numpy(self, *, memo: Optional[dict] = None) -> np.ndarray:
        """
        Convert this ObjectArray to a numpy array.

        The resulting numpy array will have its dtype set as `object`.
        This new array itself and its contents will be mutable (those
        mutable objects being the copies of their immutable sources).

        Returns:
            The numpy counterpart of this ObjectArray.
        """
        from .immutable import mutable_copy

        if memo is None:
            memo = {}

        n = len(self)
        result = np.empty(n, dtype=object)

        memo[id(self)] = result

        for i, item in enumerate(self):
            result[i] = mutable_copy(item, memo=memo)

        return result

    @staticmethod
    def from_numpy(ndarray: np.ndarray) -> "ObjectArray":
        """
        Convert a numpy array of dtype `object` to an `ObjectArray`.

        Args:
            ndarray: The numpy array that will be converted to `ObjectArray`.
        Returns:
            The ObjectArray counterpart of the given numpy array.
        """
        if isinstance(ndarray, np.ndarray):
            if ndarray.dtype == np.dtype(object):
                n = len(ndarray)
                result = ObjectArray(n)
                for i, element in enumerate(ndarray):
                    result[i] = element
                return result
            else:
                raise ValueError(
                    f"The dtype of the given array was expected as `object`."
                    f" However, the dtype was encountered as {ndarray.dtype}."
                )
        else:
            raise TypeError(f"Expected a `numpy.ndarray` instance, but received an object of type {type(ndarray)}.")

device: Union[str, torch.device] property readonly

The device which stores the elements of the ObjectArray. In the case of ObjectArray, this property always returns the CPU device.

Returns:

Type Description
Union[str, torch.device]

The CPU device, as a torch.device object.

dtype: Union[str, torch.dtype, numpy.dtype, Type] property readonly

The dtype of the elements stored by the ObjectArray. In the case of ObjectArray, the dtype is always object.

is_read_only: bool property readonly

True if this ObjectArray is read-only; False otherwise.

ndim: int property readonly

Number of dimensions handled by the ObjectArray. This is equivalent to getting the length of the size tuple.

shape: Union[int, torch.Size] property readonly

Shape of the ObjectArray, as a PyTorch Size tuple.

__init__(self, size=None, *, slice_of=None) special

__init__(...): Instantiate a new ObjectArray.

Parameters:

Name Type Description Default
size Union[int, torch.Size]

Length of the ObjectArray. If this argument is present and is an integer n, then the resulting ObjectArray will be of length n, and will be filled with None values. This argument cannot be used together with the keyword argument slice_of.

None
slice_of Optional[tuple]

Optionally a tuple in the form (original_object_tensor, slice_info). When this argument is present, then the resulting ObjectArray will be a slice of the given original_object_tensor (which is expected as an ObjectArray instance). slice_info is either a slice instance, or a sequence of integers. The resulting ObjectArray might be a view of original_object_tensor (i.e. it might share its memory with original_object_tensor). This keyword argument cannot be used together with the argument size.

None
Source code in evotorch/tools/objectarray.py
def __init__(
    self,
    size: Optional[Size] = None,
    *,
    slice_of: Optional[tuple] = None,
):
    """
    `__init__(...)`: Instantiate a new ObjectArray.

    Args:
        size: Length of the ObjectArray. If this argument is present and
            is an integer `n`, then the resulting ObjectArray will be
            of length `n`, and will be filled with `None` values.
            This argument cannot be used together with the keyword
            argument `slice_of`.
        slice_of: Optionally a tuple in the form
            `(original_object_tensor, slice_info)`.
            When this argument is present, then the resulting ObjectArray
            will be a slice of the given `original_object_tensor` (which
            is expected as an ObjectArray instance). `slice_info` is
            either a `slice` instance, or a sequence of integers.
            The resulting ObjectArray might be a view of
            `original_object_tensor` (i.e. it might share its memory with
            `original_object_tensor`).
            This keyword argument cannot be used together with the
            argument `size`.
    """
    if size is not None and slice_of is not None:
        raise ValueError("Expected either `size` argument or `slice_of` argument, but got both.")
    elif size is None and slice_of is None:
        raise ValueError("Expected either `size` argument or `slice_of` argument, but got none.")
    elif size is not None:
        if not is_sequence(size):
            length = size
        elif isinstance(size, (np.ndarray, torch.Tensor)) and (size.ndim > 1):
            raise ValueError(f"Invalid size: {size}")
        else:
            [length] = size
        length = int(length)
        self._indices = torch.arange(length, dtype=torch.int64)
        self._objects = [None] * length
    elif slice_of is not None:
        source: ObjectArray

        source, slicing = slice_of

        if not isinstance(source, ObjectArray):
            raise TypeError(
                f"`slice_of`: The first element was expected as an ObjectArray."
                f" But it is of type {repr(type(source))}"
            )

        if isinstance(slicing, tuple) or is_integer(slicing):
            raise TypeError(f"Invalid slice: {slicing}")

        self._indices = source._indices[slicing]
        self._objects = source._objects

        if storage_ptr(self._indices) != storage_ptr(source._indices):
            self._objects = clone(self._objects)

    self._device = torch.device("cpu")
    self._read_only = False

clone(self, *, preserve_read_only=False, memo=None)

Get a deep copy of the ObjectArray.

Parameters:

Name Type Description Default
preserve_read_only bool

Whether or not to preserve the read-only attribute. Note that the default value is False, which means that the newly made clone will NOT be read-only even if the original ObjectArray is.

False
memo Optional[dict]

Optionally a dictionary which maps from the ids of the already cloned objects to their clones. In most scenarios, when this method is called from outside, this can be left as None.

None

Returns:

Type Description
Iterable

The clone of the original ObjectArray.

Source code in evotorch/tools/objectarray.py
def clone(self, *, preserve_read_only: bool = False, memo: Optional[dict] = None) -> Iterable:
    """
    Get a deep copy of the ObjectArray.

    Args:
        preserve_read_only: Whether or not to preserve the read-only
            attribute. Note that the default value is False, which
            means that the newly made clone will NOT be read-only
            even if the original ObjectArray is.
        memo: Optionally a dictionary which maps from the ids of the
            already cloned objects to their clones.
            In most scenarios, when this method is called from outside,
            this can be left as None.
    Returns:
        The clone of the original ObjectArray.
    """
    from .cloning import deep_clone

    if memo is None:
        memo = {}

    self_id = id(self)
    if self_id in memo:
        return memo[self_id]

    if not preserve_read_only:
        return self.numpy(memo=memo)
    else:
        result = ObjectArray(len(self))
        memo[self_id] = result

        for i, item in enumerate(self):
            result[i] = deep_clone(item, otherwise_deepcopy=True, memo=memo)

        return result

dim(self)

Get the number of dimensions handled by the ObjectArray. This is equivalent to getting the length of the size tuple.

Returns:

Type Description
int

The number of dimensions, as an integer.

Source code in evotorch/tools/objectarray.py
def dim(self) -> int:
    """
    Get the number of dimensions handled by the ObjectArray.
    This is equivalent to getting the length of the size tuple.

    Returns:
        The number of dimensions, as an integer.
    """
    return self._indices.dim()

from_numpy(ndarray) staticmethod

Convert a numpy array of dtype object to an ObjectArray.

Parameters:

Name Type Description Default
ndarray ndarray

The numpy array that will be converted to ObjectArray.

required

Returns:

Type Description
ObjectArray

The ObjectArray counterpart of the given numpy array.

Source code in evotorch/tools/objectarray.py
@staticmethod
def from_numpy(ndarray: np.ndarray) -> "ObjectArray":
    """
    Convert a numpy array of dtype `object` to an `ObjectArray`.

    Args:
        ndarray: The numpy array that will be converted to `ObjectArray`.
    Returns:
        The ObjectArray counterpart of the given numpy array.
    """
    if isinstance(ndarray, np.ndarray):
        if ndarray.dtype == np.dtype(object):
            n = len(ndarray)
            result = ObjectArray(n)
            for i, element in enumerate(ndarray):
                result[i] = element
            return result
        else:
            raise ValueError(
                f"The dtype of the given array was expected as `object`."
                f" However, the dtype was encountered as {ndarray.dtype}."
            )
    else:
        raise TypeError(f"Expected a `numpy.ndarray` instance, but received an object of type {type(ndarray)}.")

get_read_only_view(self)

Get a read-only view of this ObjectArray.

Source code in evotorch/tools/objectarray.py
def get_read_only_view(self) -> "ObjectArray":
    """
    Get a read-only view of this ObjectArray.
    """
    result = self[:]
    result._read_only = True
    return result

numel(self)

Number of elements stored by the ObjectArray.

Returns:

Type Description
int

The number of elements, as an integer.

Source code in evotorch/tools/objectarray.py
def numel(self) -> int:
    """
    Number of elements stored by the ObjectArray.

    Returns:
        The number of elements, as an integer.
    """
    return self._indices.numel()

numpy(self, *, memo=None)

Convert this ObjectArray to a numpy array.

The resulting numpy array will have its dtype set as object. This new array itself and its contents will be mutable (those mutable objects being the copies of their immutable sources).

Returns:

Type Description
ndarray

The numpy counterpart of this ObjectArray.

Source code in evotorch/tools/objectarray.py
def numpy(self, *, memo: Optional[dict] = None) -> np.ndarray:
    """
    Convert this ObjectArray to a numpy array.

    The resulting numpy array will have its dtype set as `object`.
    This new array itself and its contents will be mutable (those
    mutable objects being the copies of their immutable sources).

    Returns:
        The numpy counterpart of this ObjectArray.
    """
    from .immutable import mutable_copy

    if memo is None:
        memo = {}

    n = len(self)
    result = np.empty(n, dtype=object)

    memo[id(self)] = result

    for i, item in enumerate(self):
        result[i] = mutable_copy(item, memo=memo)

    return result

repeat(self, *sizes)

Repeat the contents of this ObjectArray.

For example, if we have an ObjectArray objs which stores ["hello", "world"], the following line:

objs.repeat(3)

will result in an ObjectArray which stores:

`["hello", "world", "hello", "world", "hello", "world"]`

Parameters:

Name Type Description Default
sizes

Although this argument is named sizes to be compatible with PyTorch, what is expected here is a single positional argument, as a single integer, or as a single-element tuple. The given integer (which can be the argument itself, or the integer within the given single-element tuple), specifies how many times the stored sequence will be repeated.

()

Returns:

Type Description
ObjectArray

A new ObjectArray which repeats the original one's values

Source code in evotorch/tools/objectarray.py
def repeat(self, *sizes) -> "ObjectArray":
    """
    Repeat the contents of this ObjectArray.

    For example, if we have an ObjectArray `objs` which stores
    `["hello", "world"]`, the following line:

        objs.repeat(3)

    will result in an ObjectArray which stores:

        `["hello", "world", "hello", "world", "hello", "world"]`

    Args:
        sizes: Although this argument is named `sizes` to be compatible
            with PyTorch, what is expected here is a single positional
            argument, as a single integer, or as a single-element
            tuple.
            The given integer (which can be the argument itself, or
            the integer within the given single-element tuple),
            specifies how many times the stored sequence will be
            repeated.
    Returns:
        A new ObjectArray which repeats the original one's values
    """

    if len(sizes) != 1:
        type_name = type(self).__name__
        raise ValueError(
            f"The `repeat(...)` method of {type_name} expects exactly one positional argument."
            f" This is because {type_name} supports only 1-dimensional storage."
            f" The received positional arguments are: {sizes}."
        )
    if isinstance(sizes, tuple):
        if len(sizes) == 1:
            sizes = sizes[0]
        else:
            type_name = type(self).__name__
            raise ValueError(
                f"The `repeat(...)` method of {type_name} can accept a size tuple with only one element."
                f" This is because {type_name} supports only 1-dimensional storage."
                f" The received size tuple is: {sizes}."
            )
    num_repetitions = int(sizes[0])
    self_length = len(self)
    result = ObjectArray(num_repetitions * self_length)

    source_index = 0
    for result_index in range(len(result)):
        result[result_index] = self[source_index]
        source_index = (source_index + 1) % self_length

    return result

set_item(self, i, x, *, memo=None)

Set the i-th item of the ObjectArray as x.

Parameters:

Name Type Description Default
i Any

An index or a slice.

required
x Any

The object that will be put into the ObjectArray.

required
memo Optional[dict]

Optionally a dictionary which maps from the ids of the already placed objects to their clones within ObjectArray. In most scenarios, when this method is called from outside, this can be left as None.

None
Source code in evotorch/tools/objectarray.py
def set_item(self, i: Any, x: Any, *, memo: Optional[dict] = None):
    """
    Set the i-th item of the ObjectArray as x.

    Args:
        i: An index or a slice.
        x: The object that will be put into the ObjectArray.
        memo: Optionally a dictionary which maps from the ids of the
            already placed objects to their clones within ObjectArray.
            In most scenarios, when this method is called from outside,
            this can be left as None.
    """
    from .immutable import as_immutable

    if memo is None:
        memo = {}

    memo[id(self)] = self

    if self._read_only:
        raise ValueError("This ObjectArray is read-only, therefore, modification is not allowed.")

    if is_integer(i):
        index = int(self._indices[i])
        self._objects[index] = as_immutable(x, memo=memo)
    else:
        indices = self._indices[i]
        if not isinstance(x, Iterable):
            raise TypeError(f"Expected an iterable, but got {repr(x)}")

        if indices.ndim != 1:
            raise ValueError(
                "Received indices that would change the dimensionality of the ObjectArray."
                " However, an ObjectArray can only be 1-dimensional."
            )

        slice_refers_to_whole_array = (len(indices) == len(self._indices)) and torch.all(indices == self._indices)
        if slice_refers_to_whole_array:
            memo[id(x)] = self

        if not hasattr(x, "__len__"):
            x = list(x)

        if len(x) != len(indices):
            raise TypeError(
                f"The slicing operation refers to {len(indices)} elements."
                f" However, the given objects sequence has {len(x)} elements."
            )

        for q, obj in enumerate(x):
            index = int(indices[q])
            self._objects[index] = as_immutable(obj, memo=memo)

size(self)

Get the size of the ObjectArray, as a PyTorch Size tuple.

Returns:

Type Description
Union[int, torch.Size]

The size (i.e. the shape) of the ObjectArray.

Source code in evotorch/tools/objectarray.py
def size(self) -> Size:
    """
    Get the size of the ObjectArray, as a PyTorch Size tuple.

    Returns:
        The size (i.e. the shape) of the ObjectArray.
    """
    return self._indices.size()

ranking

This module contains ranking functions which work with PyTorch tensors.

centered(fitnesses, *, higher_is_better=True)

Apply linearly spaced 0-centered ranking on a PyTorch tensor. The lowest weight is -0.5, and the highest weight is 0.5. This is the same ranking method that was used in:

Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, Ilya Sutskever (2017).
Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Parameters:

Name Type Description Default
fitnesses Tensor

A PyTorch tensor which contains real numbers which we want to rank.

required
higher_is_better bool

Whether or not the higher values will be assigned higher ranks. Changing this to False means that lower values are interpreted as better, and therefore lower values will have higher ranks.

True

Returns:

Type Description
Tensor

The ranks, in the same device, with the same dtype with the original tensor.

Source code in evotorch/tools/ranking.py
def centered(fitnesses: torch.Tensor, *, higher_is_better: bool = True) -> torch.Tensor:
    """
    Apply linearly spaced 0-centered ranking on a PyTorch tensor.
    The lowest weight is -0.5, and the highest weight is 0.5.
    This is the same ranking method that was used in:

        Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, Ilya Sutskever (2017).
        Evolution Strategies as a Scalable Alternative to Reinforcement Learning

    Args:
        fitnesses: A PyTorch tensor which contains real numbers which we want
             to rank.
        higher_is_better: Whether or not the higher values will be assigned
             higher ranks. Changing this to False means that lower values
             are interpreted as better, and therefore lower values will have
             higher ranks.
    Returns:
        The ranks, in the same device, with the same dtype with the original
        tensor.
    """
    device = fitnesses.device
    dtype = fitnesses.dtype
    with torch.no_grad():
        x = fitnesses.reshape(-1)
        n = len(x)
        indices = x.argsort(descending=(not higher_is_better))
        weights = (torch.arange(n, dtype=dtype, device=device) / (n - 1)) - 0.5
        ranks = torch.empty_like(x)
        ranks[indices] = weights
        return ranks.reshape(*(fitnesses.shape))

linear(fitnesses, *, higher_is_better=True)

Apply linearly spaced ranking on a PyTorch tensor. The lowest weight is 0, and the highest weight is 1.

Parameters:

Name Type Description Default
fitnesses Tensor

A PyTorch tensor which contains real numbers which we want to rank.

required
higher_is_better bool

Whether or not the higher values will be assigned higher ranks. Changing this to False means that lower values are interpreted as better, and therefore lower values will have higher ranks.

True

Returns:

Type Description
Tensor

The ranks, in the same device, with the same dtype with the original tensor.

Source code in evotorch/tools/ranking.py
def linear(fitnesses: torch.Tensor, *, higher_is_better: bool = True) -> torch.Tensor:
    """
    Apply linearly spaced ranking on a PyTorch tensor.
    The lowest weight is 0, and the highest weight is 1.

    Args:
        fitnesses: A PyTorch tensor which contains real numbers which we want
             to rank.
        higher_is_better: Whether or not the higher values will be assigned
             higher ranks. Changing this to False means that lower values
             are interpreted as better, and therefore lower values will have
             higher ranks.
    Returns:
        The ranks, in the same device, with the same dtype with the original
        tensor.
    """
    device = fitnesses.device
    dtype = fitnesses.dtype
    with torch.no_grad():
        x = fitnesses.reshape(-1)
        n = len(x)
        indices = x.argsort(descending=(not higher_is_better))
        weights = torch.arange(n, dtype=dtype, device=device) / (n - 1)
        ranks = torch.empty_like(x)
        ranks[indices] = weights
        return ranks.reshape(*(fitnesses.shape))

nes(fitnesses, *, higher_is_better=True)

Apply the ranking mechanism proposed in:

Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., & Schmidhuber, J. (2014).
Natural evolution strategies. The Journal of Machine Learning Research, 15(1), 949-980.

Parameters:

Name Type Description Default
fitnesses Tensor

A PyTorch tensor which contains real numbers which we want to rank.

required
higher_is_better bool

Whether or not the higher values will be assigned higher ranks. Changing this to False means that lower values are interpreted as better, and therefore lower values will have higher ranks.

True

Returns:

Type Description
Tensor

The ranks, in the same device, with the same dtype with the original tensor.

Source code in evotorch/tools/ranking.py
def nes(fitnesses: torch.Tensor, *, higher_is_better: bool = True) -> torch.Tensor:
    """
    Apply the ranking mechanism proposed in:

        Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., & Schmidhuber, J. (2014).
        Natural evolution strategies. The Journal of Machine Learning Research, 15(1), 949-980.

    Args:
        fitnesses: A PyTorch tensor which contains real numbers which we want
             to rank.
        higher_is_better: Whether or not the higher values will be assigned
             higher ranks. Changing this to False means that lower values
             are interpreted as better, and therefore lower values will have
             higher ranks.
    Returns:
        The ranks, in the same device, with the same dtype with the original
        tensor.
    """
    device = fitnesses.device
    dtype = fitnesses.dtype

    with torch.no_grad():
        x = fitnesses.reshape(-1)
        n = len(x)

        incr_indices = torch.arange(n, dtype=dtype, device=device)
        N = torch.tensor(n, dtype=dtype, device=device)

        weights = torch.max(
            torch.tensor(0, dtype=dtype, device=device), torch.log((N / 2.0) + 1.0) - torch.log(N - incr_indices)
        )

        indices = torch.argsort(x, descending=(not higher_is_better))
        ranks = torch.empty(n, dtype=indices.dtype, device=device)
        ranks[indices] = torch.arange(n, dtype=indices.dtype, device=device)

        utils = weights[ranks]
        utils /= torch.sum(utils)
        utils -= 1 / N

        return utils.reshape(*(fitnesses.shape))

normalized(fitnesses, *, higher_is_better=True)

Normalize the fitnesses and return the result as ranks.

The normalization is done in such a way that the mean becomes 0.0 and the standard deviation becomes 1.0.

According to the value of higher_is_better, it will be ensured that better solutions will have numerically higher rank. In more details, if higher_is_better is set as False, then the fitnesses will be multiplied by -1.0 in addition to being subject to normalization.

Parameters:

Name Type Description Default
fitnesses Tensor

A PyTorch tensor which contains real numbers which we want to rank.

required
higher_is_better bool

Whether or not the higher values will be assigned higher ranks. Changing this to False means that lower values are interpreted as better, and therefore lower values will have higher ranks.

True

Returns:

Type Description
Tensor

The ranks, in the same device, with the same dtype with the original tensor.

Source code in evotorch/tools/ranking.py
def normalized(fitnesses: torch.Tensor, *, higher_is_better: bool = True) -> torch.Tensor:
    """
    Normalize the fitnesses and return the result as ranks.

    The normalization is done in such a way that the mean becomes 0.0 and
    the standard deviation becomes 1.0.

    According to the value of `higher_is_better`, it will be ensured that
    better solutions will have numerically higher rank.
    In more details, if `higher_is_better` is set as False, then the
    fitnesses will be multiplied by -1.0 in addition to being subject
    to normalization.

    Args:
        fitnesses: A PyTorch tensor which contains real numbers which we want
             to rank.
        higher_is_better: Whether or not the higher values will be assigned
             higher ranks. Changing this to False means that lower values
             are interpreted as better, and therefore lower values will have
             higher ranks.
    Returns:
        The ranks, in the same device, with the same dtype with the original
        tensor.
    """
    if not higher_is_better:
        fitnesses = -fitnesses

    fitness_mean = torch.mean(fitnesses)
    fitness_stdev = torch.std(fitnesses)

    fitnesses = fitnesses - fitness_mean
    fitnesses = fitnesses / fitness_stdev

    return fitnesses

rank(fitnesses, ranking_method, *, higher_is_better)

Get the ranks of the given sequence of numbers.

Better solutions will have numerically higher ranks.

Parameters:

Name Type Description Default
fitnesses Iterable[float]

A sequence of numbers to be ranked.

required
ranking_method str

The ranking method to be used. Can be "centered", which means 0-centered linear ranking from -0.5 to 0.5. Can be "linear", which means a linear ranking from 0 to 1. Can be "nes", which means the ranking method used by Natural Evolution Strategies. Can be "normalized", which means that the ranks will be the normalized counterparts of the fitnesses. Can be "raw", which means that the fitnesses themselves (or, if higher_is_better is False, their inverted counterparts, inversion meaning the operation of multiplying by -1 in this context) will be the ranks.

required
higher_is_better bool

Whether or not the higher values will be assigned higher ranks. Changing this to False means that lower values are interpreted as better, and therefore lower values will have higher ranks.

required
Source code in evotorch/tools/ranking.py
def rank(fitnesses: Iterable[float], ranking_method: str, *, higher_is_better: bool):
    """
    Get the ranks of the given sequence of numbers.

    Better solutions will have numerically higher ranks.

    Args:
        fitnesses: A sequence of numbers to be ranked.
        ranking_method: The ranking method to be used.
            Can be "centered", which means 0-centered linear ranking
                from -0.5 to 0.5.
            Can be "linear", which means a linear ranking from 0 to 1.
            Can be "nes", which means the ranking method used by
                Natural Evolution Strategies.
            Can be "normalized", which means that the ranks will be
                the normalized counterparts of the fitnesses.
            Can be "raw", which means that the fitnesses themselves
                (or, if `higher_is_better` is False, their inverted
                counterparts, inversion meaning the operation of
                multiplying by -1 in this context) will be the ranks.
        higher_is_better: Whether or not the higher values will be assigned
             higher ranks. Changing this to False means that lower values
             are interpreted as better, and therefore lower values will have
             higher ranks.
    """
    fitnesses = torch.as_tensor(fitnesses)
    rank_func = rankers[ranking_method]
    return rank_func(fitnesses, higher_is_better=higher_is_better)

raw(fitnesses, *, higher_is_better=True)

Return the fitnesses themselves as ranks.

If higher_is_better is given as False, then the fitnesses will first be multiplied by -1 and then the result will be returned as ranks.

Parameters:

Name Type Description Default
fitnesses Tensor

A PyTorch tensor which contains real numbers which we want to rank.

required
higher_is_better bool

Whether or not the higher values will be assigned higher ranks. Changing this to False means that lower values are interpreted as better, and therefore lower values will have higher ranks.

True

Returns:

Type Description
Tensor

The ranks, in the same device, with the same dtype with the original tensor.

Source code in evotorch/tools/ranking.py
def raw(fitnesses: torch.Tensor, *, higher_is_better: bool = True) -> torch.Tensor:
    """
    Return the fitnesses themselves as ranks.

    If `higher_is_better` is given as False, then the fitnesses will first
    be multiplied by -1 and then the result will be returned as ranks.

    Args:
        fitnesses: A PyTorch tensor which contains real numbers which we want
             to rank.
        higher_is_better: Whether or not the higher values will be assigned
             higher ranks. Changing this to False means that lower values
             are interpreted as better, and therefore lower values will have
             higher ranks.
    Returns:
        The ranks, in the same device, with the same dtype with the original
        tensor.
    """
    if not higher_is_better:
        fitnesses = -fitnesses
    return fitnesses

readonlytensor

ReadOnlyTensor (Tensor)

A special type of tensor which is read-only.

This is a subclass of torch.Tensor which explicitly disallows operations that would cause in-place modifications.

Since ReadOnlyTensor if a subclass of torch.Tensor, most non-destructive PyTorch operations are on this tensor are supported.

Cloning a ReadOnlyTensor using the clone() method or Python's deepcopy(...) function results in a regular PyTorch tensor.

Reshaping or slicing operations might return a ReadOnlyTensor if the result ends up being a view of the original ReadOnlyTensor; otherwise, the returned tensor is a regular torch.Tensor.

Source code in evotorch/tools/readonlytensor.py
class ReadOnlyTensor(torch.Tensor):
    """
    A special type of tensor which is read-only.

    This is a subclass of `torch.Tensor` which explicitly disallows
    operations that would cause in-place modifications.

    Since ReadOnlyTensor if a subclass of `torch.Tensor`, most
    non-destructive PyTorch operations are on this tensor are supported.

    Cloning a ReadOnlyTensor using the `clone()` method or Python's
    `deepcopy(...)` function results in a regular PyTorch tensor.

    Reshaping or slicing operations might return a ReadOnlyTensor if the
    result ends up being a view of the original ReadOnlyTensor; otherwise,
    the returned tensor is a regular `torch.Tensor`.
    """

    def __getattribute__(self, attribute_name: str) -> Any:
        if (
            isinstance(attribute_name, str)
            and attribute_name.endswith("_")
            and (not ((attribute_name.startswith("__")) and (attribute_name.endswith("__"))))
        ):
            raise AttributeError(
                f"A ReadOnlyTensor explicitly disables all members whose names end with '_'."
                f" Cannot access member {repr(attribute_name)}."
            )
        else:
            return super().__getattribute__(attribute_name)

    def __cannot_modify(self, *ignore, **ignore_too):
        raise TypeError("The contents of a ReadOnlyTensor cannot be modified")

    __setitem__ = __cannot_modify
    __iadd__ = __cannot_modify
    __iand__ = __cannot_modify
    __idiv__ = __cannot_modify
    __ifloordiv__ = __cannot_modify
    __ilshift__ = __cannot_modify
    __imatmul__ = __cannot_modify
    __imod__ = __cannot_modify
    __imul__ = __cannot_modify
    __ior__ = __cannot_modify
    __ipow__ = __cannot_modify
    __irshift__ = __cannot_modify
    __isub__ = __cannot_modify
    __itruediv__ = __cannot_modify
    __ixor__ = __cannot_modify

    if _torch_older_than_1_12:
        # Define __str__ and __repr__ for when using PyTorch 1.11 or older.
        # With PyTorch 1.12, overriding __str__ and __repr__ are not necessary.
        def __to_string(self) -> str:
            s = super().__repr__()
            if "\n" not in s:
                return f"ReadOnlyTensor({super().__repr__()})"
            else:
                indenter = " " * 4
                s = (indenter + s.replace("\n", "\n" + indenter)).rstrip()
                return f"ReadOnlyTensor(\n{s}\n)"

        __str__ = __to_string
        __repr__ = __to_string

    def clone(self, *, preserve_read_only: bool = False) -> torch.Tensor:
        result = super().clone()
        if not preserve_read_only:
            result = result.as_subclass(torch.Tensor)
        return result

    def __mutable_if_independent(self, other: torch.Tensor) -> torch.Tensor:
        from .misc import storage_ptr

        self_ptr = storage_ptr(self)
        other_ptr = storage_ptr(other)
        if self_ptr != other_ptr:
            other = other.as_subclass(torch.Tensor)
        return other

    def __getitem__(self, index_or_slice) -> torch.Tensor:
        result = super().__getitem__(index_or_slice)
        return self.__mutable_if_independent(result)

    def reshape(self, *args, **kwargs) -> torch.Tensor:
        result = super().reshape(*args, **kwargs)
        return self.__mutable_if_independent(result)

    def numpy(self) -> np.ndarray:
        arr: np.ndarray = torch.Tensor.numpy(self)
        arr.flags["WRITEABLE"] = False
        return arr

    def __array__(self, *args, **kwargs) -> np.ndarray:
        arr: np.ndarray = super().__array__(*args, **kwargs)
        arr.flags["WRITEABLE"] = False
        return arr

    def __copy__(self):
        return self.clone(preserve_read_only=True)

    def __deepcopy__(self, memo):
        return self.clone(preserve_read_only=True)

    @classmethod
    def __torch_function__(cls, func: Callable, types: Iterable, args: tuple = (), kwargs: Optional[Mapping] = None):
        if (kwargs is not None) and ("out" in kwargs):
            if isinstance(kwargs["out"], ReadOnlyTensor):
                raise TypeError(
                    f"The `out` keyword argument passed to {func} is a ReadOnlyTensor."
                    f" A ReadOnlyTensor explicitly fails when referenced via the `out` keyword argument of any torch"
                    f" function."
                    f" This restriction is for making sure that the torch operations which could normally do in-place"
                    f" modifications do not operate on ReadOnlyTensor instances."
                )
        return super().__torch_function__(func, types, args, kwargs)

__torch_function__(func, types, args=(), kwargs=None) classmethod special

This torch_function implementation wraps subclasses such that methods called on subclasses return a subclass instance instead of a torch.Tensor instance.

One corollary to this is that you need coverage for torch.Tensor methods if implementing torch_function for subclasses.

We recommend always calling super().__torch_function__ as the base case when doing the above.

While not mandatory, we recommend making __torch_function__ a classmethod.

Source code in evotorch/tools/readonlytensor.py
@classmethod
def __torch_function__(cls, func: Callable, types: Iterable, args: tuple = (), kwargs: Optional[Mapping] = None):
    if (kwargs is not None) and ("out" in kwargs):
        if isinstance(kwargs["out"], ReadOnlyTensor):
            raise TypeError(
                f"The `out` keyword argument passed to {func} is a ReadOnlyTensor."
                f" A ReadOnlyTensor explicitly fails when referenced via the `out` keyword argument of any torch"
                f" function."
                f" This restriction is for making sure that the torch operations which could normally do in-place"
                f" modifications do not operate on ReadOnlyTensor instances."
            )
    return super().__torch_function__(func, types, args, kwargs)

clone(self, *, preserve_read_only=False)

clone(*, memory_format=torch.preserve_format) -> Tensor

See :func:torch.clone

Source code in evotorch/tools/readonlytensor.py
def clone(self, *, preserve_read_only: bool = False) -> torch.Tensor:
    result = super().clone()
    if not preserve_read_only:
        result = result.as_subclass(torch.Tensor)
    return result

numpy(self)

numpy(*, force=False) -> numpy.ndarray

Returns the tensor as a NumPy :class:ndarray.

If :attr:force is False (the default), the conversion is performed only if the tensor is on the CPU, does not require grad, does not have its conjugate bit set, and is a dtype and layout that NumPy supports. The returned ndarray and the tensor will share their storage, so changes to the tensor will be reflected in the ndarray and vice versa.

If :attr:force is True this is equivalent to calling t.detach().cpu().resolve_conj().resolve_neg().numpy(). If the tensor isn't on the CPU or the conjugate or negative bit is set, the tensor won't share its storage with the returned ndarray. Setting :attr:force to True can be a useful shorthand.

Parameters:

Name Type Description Default
force bool

if True, the ndarray may be a copy of the tensor instead of always sharing memory, defaults to False.

required
Source code in evotorch/tools/readonlytensor.py
def numpy(self) -> np.ndarray:
    arr: np.ndarray = torch.Tensor.numpy(self)
    arr.flags["WRITEABLE"] = False
    return arr

reshape(self, *args, **kwargs)

reshape(*shape) -> Tensor

Returns a tensor with the same data and number of elements as :attr:self but with the specified shape. This method returns a view if :attr:shape is compatible with the current shape. See :meth:torch.Tensor.view on when it is possible to return a view.

See :func:torch.reshape

Parameters:

Name Type Description Default
shape tuple of ints or int...

the desired shape

required
Source code in evotorch/tools/readonlytensor.py
def reshape(self, *args, **kwargs) -> torch.Tensor:
    result = super().reshape(*args, **kwargs)
    return self.__mutable_if_independent(result)

as_read_only_tensor(x, *, dtype=None, device=None)

Convert the given object to a ReadOnlyTensor.

The provided object can be a scalar, or an Iterable of numeric data, or an ObjectArray.

This function can be thought as the read-only counterpart of PyTorch's torch.as_tensor(...) function.

Parameters:

Name Type Description Default
x Any

The object to be converted to a ReadOnlyTensor.

required
dtype Optional[torch.dtype]

The dtype of the new ReadOnlyTensor (e.g. torch.float32). If this argument is not specified, dtype will be inferred from x. For example, if x is a PyTorch tensor or a numpy array, its existing dtype will be kept.

None
device Union[str, torch.device]

The device in which the ReadOnlyTensor will be stored (e.g. "cpu"). If this argument is not specified, the device which is storing the original x will be re-used.

None

Returns:

Type Description
Iterable

The read-only counterpart of the provided object.

Source code in evotorch/tools/readonlytensor.py
def as_read_only_tensor(
    x: Any, *, dtype: Optional[torch.dtype] = None, device: Optional[Union[str, torch.device]] = None
) -> Iterable:
    """
    Convert the given object to a ReadOnlyTensor.

    The provided object can be a scalar, or an Iterable of numeric data,
    or an ObjectArray.

    This function can be thought as the read-only counterpart of PyTorch's
    `torch.as_tensor(...)` function.

    Args:
        x: The object to be converted to a ReadOnlyTensor.
        dtype: The dtype of the new ReadOnlyTensor (e.g. torch.float32).
            If this argument is not specified, dtype will be inferred from `x`.
            For example, if `x` is a PyTorch tensor or a numpy array, its
            existing dtype will be kept.
        device: The device in which the ReadOnlyTensor will be stored
            (e.g. "cpu").
            If this argument is not specified, the device which is storing
            the original `x` will be re-used.
    Returns:
        The read-only counterpart of the provided object.
    """
    from .objectarray import ObjectArray

    kwargs = _device_and_dtype_kwargs(dtype=dtype, device=device)
    if isinstance(x, ObjectArray):
        if len(kwargs) != 0:
            raise ValueError(
                f"read_only_tensor(...): when making a read-only tensor from an ObjectArray,"
                f" the arguments `dtype` and `device` were not expected."
                f" However, the received keyword arguments are: {kwargs}."
            )
        return x.get_read_only_view()
    else:
        return torch.as_tensor(x, **kwargs).as_subclass(ReadOnlyTensor)

read_only_tensor(x, *, dtype=None, device=None)

Make a ReadOnlyTensor from the given object.

The provided object can be a scalar, or an Iterable of numeric data, or an ObjectArray.

This function can be thought as the read-only counterpart of PyTorch's torch.tensor(...) function.

Parameters:

Name Type Description Default
x Any

The object from which the new ReadOnlyTensor will be made.

required
dtype Optional[torch.dtype]

The dtype of the new ReadOnlyTensor (e.g. torch.float32).

None
device Union[str, torch.device]

The device in which the ReadOnlyTensor will be stored (e.g. "cpu").

None

Returns:

Type Description
Iterable

The new read-only tensor.

Source code in evotorch/tools/readonlytensor.py
def read_only_tensor(
    x: Any, *, dtype: Optional[torch.dtype] = None, device: Optional[Union[str, torch.device]] = None
) -> Iterable:
    """
    Make a ReadOnlyTensor from the given object.

    The provided object can be a scalar, or an Iterable of numeric data,
    or an ObjectArray.

    This function can be thought as the read-only counterpart of PyTorch's
    `torch.tensor(...)` function.

    Args:
        x: The object from which the new ReadOnlyTensor will be made.
        dtype: The dtype of the new ReadOnlyTensor (e.g. torch.float32).
        device: The device in which the ReadOnlyTensor will be stored
            (e.g. "cpu").
    Returns:
        The new read-only tensor.
    """
    from .objectarray import ObjectArray

    kwargs = _device_and_dtype_kwargs(dtype=dtype, device=device)
    if isinstance(x, ObjectArray):
        if len(kwargs) != 0:
            raise ValueError(
                f"read_only_tensor(...): when making a read-only tensor from an ObjectArray,"
                f" the arguments `dtype` and `device` were not expected."
                f" However, the received keyword arguments are: {kwargs}."
            )
        return x.get_read_only_view()
    else:
        return torch.as_tensor(x, **kwargs).as_subclass(ReadOnlyTensor)

recursiveprintable

RecursivePrintable

A base class for making a class printable.

This base class considers custom container types which can recursively contain themselves (even in a cyclic manner). Classes inheriting from RecursivePrintable will gain a new ready-to-use method named to_string(...). This to_string(...) method, upon being called, checks if the current class is an Iterable or a Mapping, and prints the representation accordingly, with a recursion limit to avoid RecursionError. The methods __str__(...) and __repr__(...) are also defined as aliases of this to_string method.

Source code in evotorch/tools/recursiveprintable.py
class RecursivePrintable:
    """
    A base class for making a class printable.

    This base class considers custom container types which can recursively
    contain themselves (even in a cyclic manner). Classes inheriting from
    `RecursivePrintable` will gain a new ready-to-use method named
    `to_string(...)`. This `to_string(...)` method, upon being called,
    checks if the current class is an Iterable or a Mapping, and prints
    the representation accordingly, with a recursion limit to avoid
    `RecursionError`. The methods `__str__(...)` and `__repr__(...)`
    are also defined as aliases of this `to_string` method.
    """

    def to_string(self, *, max_depth: int = 10) -> str:
        if max_depth <= 0:
            return "<...>"

        def item_repr(x: Any) -> str:
            if isinstance(x, RecursivePrintable):
                return x.to_string(max_depth=(max_depth - 1))
            else:
                return repr(x)

        result = []

        def puts(*x: Any):
            for item_of_x in x:
                result.append(str(item_of_x))

        clsname = type(self).__name__
        first_one = True

        if isinstance(self, Mapping):
            puts(clsname, "({")
            for k, v in self.items():
                if first_one:
                    first_one = False
                else:
                    puts(", ")
                puts(item_repr(k), ": ", item_repr(v))
            puts("})")
        elif isinstance(self, Iterable):
            puts(clsname, "([")
            for v in self:
                if first_one:
                    first_one = False
                else:
                    puts(", ")
                puts(item_repr(v))
            puts("])")
        else:
            raise NotImplementedError

        return "".join(result)

    def __str__(self) -> str:
        return self.to_string()

    def __repr__(self) -> str:
        return self.to_string()

structures

This namespace contains data structures whose underlying storages are contiguous and therefore vectorization-friendly.

CBag (Structure)

An integer bag from which one can do sampling without replacement.

Let us imagine that we wish to create a bag whose maximum length (i.e. whose maximum number of contained elements) is 5. For this, we can do:

bag = CBag(max_length=5)

which gives us an empty bag (i.e. a bag in which all pre-allocated slots are empty):

 _________________________________________________
|         |         |         |         |         |
| <empty> | <empty> | <empty> | <empty> | <empty> |
|_________|_________|_________|_________|_________|

Given that the maximum length for this bag is 5, the default set of acceptable values for this bag is 0, 1, 2, 3, 4. Let us put three values into our bag:

bag.push_(torch.tensor(1))
bag.push_(torch.tensor(3))
bag.push_(torch.tensor(4))

After these push operations, our bag can be visualized like this:

 _________________________________________________
|         |         |         |         |         |
|   1     |   3     |   4     | <empty> | <empty> |
|_________|_________|_________|_________|_________|

Let us now sample an element from this bag:

sampled1 = bag.pop_()

Because this is the first time we are sampling from this bag, the elements will be first shuffled. Let us assume that the shuffling resulted in:

 _________________________________________________
|         |         |         |         |         |
|   3     |   1     |   4     | <empty> | <empty> |
|_________|_________|_________|_________|_________|

Given this shuffed state, our call to pop_(...) will pop the leftmost element (3 in this case). Therefore, the value of sampled1 will be 3 (as a scalar PyTorch tensor), and the state of the bag after the pop operation will be:

 _________________________________________________
|         |         |         |         |         |
|   1     |   4     | <empty> | <empty> | <empty> |
|_________|_________|_________|_________|_________|

Let us keep sampling until the bag is empty:

sampled2 = bag.pop_()
sampled3 = bag.pop_()

The value of sampled2 becomes 1, and the value of sampled3 becomes 4.

This class can also represent a contiguous batch of bags. As an example, le