Index
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 readonly 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 readonly attribute is preserved and one where a mutable clone is created.
Upon being copied or deepcopied (using the standard Python functions),
the newly made clones are also readonly. 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 readonly numpy array will not be readonly anymore).
Subclasses of evotorch.immutable.ImmutableContainer
inherit from
ReadOnlyClonable
.
Source code in evotorch/tools/cloning.py
class ReadOnlyClonable(Clonable):
"""
Clonability base class for readonly 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 readonly attribute is preserved and one where a mutable
clone is created.
Upon being copied or deepcopied (using the standard Python functions),
the newly made clones are also readonly. 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 readonly numpy array will not be readonly 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 readonly 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 readonly
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 readonly 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 readonly 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 readonly 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 readonly
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 deepcopied 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
deepcopied, 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 readonly and/or immutable data, this function will
NOT modify the readonly behavior. For example, the deepclone of a
ReadOnlyTensor is still a ReadOnlyTensor, and the deepclone of a
readonly numpy array is still a readonly 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 deepcloning operation,
the encountered tensors and/or arrays might be the components of the root
object, and changing their readonly 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 deepcloned. 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

required 
otherwise_deepcopy 
bool 
Setting this as True means that, when an
unrecognized object is encountered, that object will be
deepcopied. To handle shared and cyclicreferencing objects,
the 
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 deepcopied 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
deepcopied, 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 readonly and/or immutable data, this function will
NOT modify the readonly behavior. For example, the deepclone of a
ReadOnlyTensor is still a ReadOnlyTensor, and the deepclone of a
readonly numpy array is still a readonly 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 deepcloning operation,
the encountered tensors and/or arrays might be the components of the root
object, and changing their readonly 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 deepcloned. 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
deepcopied. To handle shared and cyclicreferencing 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 listlike objects or dictlike 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 listlike
objects or dictlike 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 dictlike
or listlike 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 dictlike object."
f" However, previous function(s) in this hook had returned listlike 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 listlike object."
f" However, previous function(s) in this hook had returned dictlike 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 dictlike object, or a listlike 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" dictionarylike objects. Instead, accumulated"
f" an object of type {type(result)}."
f" Hint: are the functions registered in this hook"
f" returning nondictionary 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 dictlike 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 dictlike or listlike 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 dictlike
or listlike 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 dictlike object."
f" However, previous function(s) in this hook had returned listlike 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 listlike object."
f" However, previous function(s) in this hook had returned dictlike 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 dictlike object, or a listlike 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 
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 
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)
¶
misc
¶
Miscellaneous utility functions
DTypeAndDevice (tuple)
¶
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 
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 
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 
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 readonly containers (e.g. ReadOnlyTensor,
ImmutableContainer, etc.), the created clones preserve their readonly
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 readonly containers (e.g. ReadOnlyTensor,
ImmutableContainer, etc.), the created clones preserve their readonly
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 
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 
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 
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 
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 
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 
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 1dimensional."
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 1dimensional")
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.
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 
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 
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 1dimensional 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 
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 
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
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
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 
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 
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 
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 
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 
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 
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)
¶
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 
None 
out 
Optional[torch.Tensor] 
Optionally, the existing tensor whose values will be changed
so that they represent an identity matrix.
If an 
None 
dtype 
Union[str, torch.dtype, numpy.dtype, Type] 
Optionally a string (e.g. "float32") or a PyTorch dtype
(e.g. torch.float32).
If 
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 
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 
() 
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 
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

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 singleelement
tuple containing an integer (because `ObjectArray` can only
be onedimensional).
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 
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 
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 
None 
dtype 
Union[str, torch.dtype, numpy.dtype, Type] 
Optionally a string (e.g. "float32") or a PyTorch dtype
(e.g. torch.float32).
If 
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 
None 
generator 
Any 
Pseudorandom number generator to be used when sampling
the values. Can be a 
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: Pseudorandom 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 
None 
dtype 
Union[str, torch.dtype, numpy.dtype, Type] 
Optionally a string (e.g. "float32") or a PyTorch dtype
(e.g. torch.float32).
If 
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 
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 
None 
dtype 
Union[str, torch.dtype, numpy.dtype, Type] 
Optionally a string (e.g. "float32") or a PyTorch dtype
(e.g. torch.float32).
If 
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 
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 ... n1]
.
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.

required 
out 
Optional[torch.Tensor] 
Optionally, the tensor to be filled by the random integers.
If an 
None 
dtype 
Union[str, torch.dtype, numpy.dtype, Type] 
Optionally a string (e.g. "int64") or a PyTorch dtype
(e.g. torch.int64).
If 
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 
None 
generator 
Any 
Pseudorandom number generator to be used when sampling
the values. Can be a 
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 ... n1]`.
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: Pseudorandom 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 readonly behavior.
The following example creates a 2dimensional 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 
required 
dtype 
Union[str, torch.dtype, numpy.dtype, Type] 
Optionally a string (e.g. "float32"), or a PyTorch dtype
(e.g. torch.float32), or 
None 
device 
Union[str, torch.device] 
The device in which the tensor will be stored.
If 
None 
read_only 
bool 
Whether or not the created tensor will be readonly. 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 readonly behavior.
The following example creates a 2dimensional 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 readonly.
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 
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 
None 
out 
Optional[torch.Tensor] 
Optionally, the tensor to be filled by uniformly distributed
values. If an 
None 
dtype 
Union[str, torch.dtype, numpy.dtype, Type] 
Optionally a string (e.g. "float32") or a PyTorch dtype
(e.g. torch.float32).
If 
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 
None 
generator 
Any 
Pseudorandom number generator to be used when sampling
the values. Can be a 
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: Pseudorandom 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 
None 
dtype 
Union[str, torch.dtype, numpy.dtype, Type] 
Optionally a string (e.g. "float32") or a PyTorch dtype
(e.g. torch.float32).
If 
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 
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:
The output becomes something like this:
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

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 inplace.
# 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 readonly.
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 readonly.
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:
[20221123 22:28:47] INFO <75935> evotorch: This is a log message
{asctime} {level} {process} {logger_name}: {message}
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:
```
[20221123 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 ith item of the returned list expresses the suggested workload for the ith 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 ith item of the returned list
expresses the suggested workload for the ith 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 distributionbased 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 Distributionbased Policy Evolution.
Parallel Problem Solving from Nature (PPSN 2020).
Nikolaus Hansen, Andreas Ostermeier (2001).
Completely Derandomized SelfAdaptation 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 
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 distributionbased 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 Distributionbased Policy Evolution.
Parallel Problem Solving from Nature (PPSN 2020).
Nikolaus Hansen, Andreas Ostermeier (2001).
Completely Derandomized SelfAdaptation 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 distributionbased 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 
None 
radius_init 
Union[float, Iterable[float], torch.Tensor] 
Radius. If one wishes to provide a standard deviation
instead, then 
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 distributionbased 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 arraylike 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 onedimensional, 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 readonly. If the numeric array
is indeed readonly, then the array is put into the ObjectArray as it
is. If the array is not readonly, then a readonly 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 readonly;
(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 readonly 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 singleelement ObjectArray
# Place a numpy array into objs:
objs[0] = np.array([1, 2, 3], dtype=float)
# At this point, objs[0] is a readonly 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 singleelement ObjectArray
# Make a new mutable numpy array
y = np.array([1, 2, 3], dtype=float)
# Make a readonly view to y:
x = y[:]
x.flags["WRITEABLE"] = False
# Place x into objs.
objs[0] = x
# At this point, objs[0] is a readonly numpy array.
# objs[0] *= 2 # < Not allowed
# During the operation of setting its 0th item, the ObjectArray
# `objs` did not clone `x` because `x` was already readonly.
# 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 readonly 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 readonly 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 onedimensional, 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 readonly. If the numeric array
is indeed readonly, then the array is put into the ObjectArray as it
is. If the array is not readonly, then a readonly 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 readonly;
(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 readonly 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 singleelement ObjectArray
# Place a numpy array into objs:
objs[0] = np.array([1, 2, 3], dtype=float)
# At this point, objs[0] is a readonly 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 singleelement ObjectArray
# Make a new mutable numpy array
y = np.array([1, 2, 3], dtype=float)
# Make a readonly view to y:
x = y[:]
x.flags["WRITEABLE"] = False
# Place x into objs.
objs[0] = x
# At this point, objs[0] is a readonly numpy array.
# objs[0] *= 2 # < Not allowed
# During the operation of setting its 0th item, the ObjectArray
# `objs` did not clone `x` because `x` was already readonly.
# 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 readonly 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 readonly 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 singleelement
tuple.
The given integer (which can be the argument itself, or
the integer within the given singleelement 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 1dimensional 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 1dimensional 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 ith 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 readonly, 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 1dimensional."
)
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 readonly
attribute. Note that the default value is False, which
means that the newly made clone will NOT be readonly
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 readonly.
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 readonly view of this ObjectArray.
"""
result = self[:]
result._read_only = True
return result
@property
def is_read_only(self) > bool:
"""
True if this ObjectArray is readonly; 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 readonly; 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 
None 
slice_of 
Optional[tuple] 
Optionally a tuple in the form

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 readonly attribute. Note that the default value is False, which means that the newly made clone will NOT be readonly 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 readonly
attribute. Note that the default value is False, which
means that the newly made clone will NOT be readonly
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. 
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 
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)
¶
numel(self)
¶
Number of elements stored by the ObjectArray.
Returns:
Type  Description 

int 
The number of elements, as an integer. 
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 
() 
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 singleelement
tuple.
The given integer (which can be the argument itself, or
the integer within the given singleelement 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 1dimensional 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 1dimensional 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 ith 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 ith 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 readonly, 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 1dimensional."
)
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. 
ranking
¶
This module contains ranking functions which work with PyTorch tensors.
centered(fitnesses, *, higher_is_better=True)
¶
Apply linearly spaced 0centered 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 0centered 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), 949980.
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), 949980.
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 0centered 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 
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 0centered 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 readonly.
This is a subclass of torch.Tensor
which explicitly disallows
operations that would cause inplace modifications.
Since ReadOnlyTensor if a subclass of torch.Tensor
, most
nondestructive 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 readonly.
This is a subclass of `torch.Tensor` which explicitly disallows
operations that would cause inplace modifications.
Since ReadOnlyTensor if a subclass of `torch.Tensor`, most
nondestructive 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 inplace"
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 inplace"
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
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 
required 
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 
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 readonly 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 
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 
None 
Returns:
Type  Description 

Iterable 
The readonly 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 readonly 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 reused.
Returns:
The readonly 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 readonly 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 readonly 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 readonly 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 readonly 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 readonly 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 readonly 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 readytouse 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 readytouse 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 vectorizationfriendly.
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:
which gives us an empty bag (i.e. a bag in which all preallocated 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:
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:
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:
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