objectarray
This module contains the ObjectArray class, which is an array-like data structure with an interface similar to PyTorch tensors, but with an ability to store arbitrary type of data (not just numbers).
ObjectArray (Sequence, RecursivePrintable)
¶
An object container with an interface similar to PyTorch tensors.
It is strictly one-dimensional, and supports advanced indexing and slicing operations supported by PyTorch tensors.
An ObjectArray can store None values, strings, numbers, booleans,
lists, sets, dictionaries, PyTorch tensors, and numpy arrays.
When a container (such as a list, dictionary, set, is placed into an ObjectArray, an immutable clone of this container is first created, and then this newly created immutable clone gets stored within the ObjectArray. This behavior is to prevent accidental modification of the stored data.
When a numeric array (such as a PyTorch tensor or a numpy array with a
numeric dtype) is placed into an ObjectArray, the target ObjectArray
first checks if the numeric array is read-only. If the numeric array
is indeed read-only, then the array is put into the ObjectArray as it
is. If the array is not read-only, then a read-only clone of the
original numeric array is first created, and then this clone gets
stored by the ObjectArray. This behavior has the following implications:
(i) even when an ObjectArray is shared by multiple components of the
program, the risk of accidental modification of the stored data through
this shared ObjectArray is significantly reduced as the stored numeric
arrays are read-only;
(ii) although not recommended, one could still forcefully modify the
numeric arrays stored by an ObjectArray by explicitly casting them as
mutable arrays
(in the case of a numpy array, one could forcefully set the WRITEABLE
flag, and, in the case of a ReadOnlyTensor, one could forcefully cast it
as a regular PyTorch tensor);
(iii) if an already read-only array x is placed into an ObjectArray,
but x shares its memory with a mutable array y, then the contents
of the ObjectArray can be affected by modifying y.
The implication (ii) is demonstrated as follows:
objs = ObjectArray(1) # a single-element ObjectArray
# Place a numpy array into objs:
objs[0] = np.array([1, 2, 3], dtype=float)
# At this point, objs[0] is a read-only numpy array.
# objs[0] *= 2 # <- Not allowed
# Possible but NOT recommended:
objs.flags["WRITEABLE"] = True
objs[0] *= 2
The implication (iii) is demonstrated as follows:
objs = ObjectArray(1) # a single-element ObjectArray
# Make a new mutable numpy array
y = np.array([1, 2, 3], dtype=float)
# Make a read-only view to y:
x = y[:]
x.flags["WRITEABLE"] = False
# Place x into objs.
objs[0] = x
# At this point, objs[0] is a read-only numpy array.
# objs[0] *= 2 # <- Not allowed
# During the operation of setting its 0-th item, the ObjectArray
# `objs` did not clone `x` because `x` was already read-only.
# However, the contents of `x` could actually be modified because
# `x` shares its memory with the mutable array `y`.
# Possible but NOT recommended:
y *= 2 # This affects both x and objs!
When a numpy array of dtype object is placed into an ObjectArray, a read-only ObjectArray copy of the original array will first be created, and then, this newly created ObjectArray will be stored by the outer ObjectArray.
An ObjectArray itself has a read-only mode, so that, in addition to its stored data, the ObjectArray itself can be protected against undesired modifications.
An interesting feature of PyTorch: if one slices a tensor A and the result is a new tensor B, and if B is sharing storage memory with A, then A.storage().data_ptr() and B.storage().data_ptr() will return the same pointer. This means, one can compare the storage pointers of A and B and see whether or not the two are sharing memory. ObjectArray was designed to have this exact behavior, so that one can understand if two ObjectArray instances are sharing memory. Note that NumPy does NOT have such a behavior. In more details, a NumPy array C and a NumPy array D could report different pointers even when D was created via a basic slicing operation on C.
Source code in evotorch/tools/objectarray.py
class ObjectArray(Sequence, RecursivePrintable):
"""
An object container with an interface similar to PyTorch tensors.
It is strictly one-dimensional, and supports advanced indexing and
slicing operations supported by PyTorch tensors.
An ObjectArray can store `None` values, strings, numbers, booleans,
lists, sets, dictionaries, PyTorch tensors, and numpy arrays.
When a container (such as a list, dictionary, set, is placed into an
ObjectArray, an immutable clone of this container is first created, and
then this newly created immutable clone gets stored within the
ObjectArray. This behavior is to prevent accidental modification of the
stored data.
When a numeric array (such as a PyTorch tensor or a numpy array with a
numeric dtype) is placed into an ObjectArray, the target ObjectArray
first checks if the numeric array is read-only. If the numeric array
is indeed read-only, then the array is put into the ObjectArray as it
is. If the array is not read-only, then a read-only clone of the
original numeric array is first created, and then this clone gets
stored by the ObjectArray. This behavior has the following implications:
(i) even when an ObjectArray is shared by multiple components of the
program, the risk of accidental modification of the stored data through
this shared ObjectArray is significantly reduced as the stored numeric
arrays are read-only;
(ii) although not recommended, one could still forcefully modify the
numeric arrays stored by an ObjectArray by explicitly casting them as
mutable arrays
(in the case of a numpy array, one could forcefully set the WRITEABLE
flag, and, in the case of a ReadOnlyTensor, one could forcefully cast it
as a regular PyTorch tensor);
(iii) if an already read-only array `x` is placed into an ObjectArray,
but `x` shares its memory with a mutable array `y`, then the contents
of the ObjectArray can be affected by modifying `y`.
The implication (ii) is demonstrated as follows:
```python
objs = ObjectArray(1) # a single-element ObjectArray
# Place a numpy array into objs:
objs[0] = np.array([1, 2, 3], dtype=float)
# At this point, objs[0] is a read-only numpy array.
# objs[0] *= 2 # <- Not allowed
# Possible but NOT recommended:
objs.flags["WRITEABLE"] = True
objs[0] *= 2
```
The implication (iii) is demonstrated as follows:
```python
objs = ObjectArray(1) # a single-element ObjectArray
# Make a new mutable numpy array
y = np.array([1, 2, 3], dtype=float)
# Make a read-only view to y:
x = y[:]
x.flags["WRITEABLE"] = False
# Place x into objs.
objs[0] = x
# At this point, objs[0] is a read-only numpy array.
# objs[0] *= 2 # <- Not allowed
# During the operation of setting its 0-th item, the ObjectArray
# `objs` did not clone `x` because `x` was already read-only.
# However, the contents of `x` could actually be modified because
# `x` shares its memory with the mutable array `y`.
# Possible but NOT recommended:
y *= 2 # This affects both x and objs!
```
When a numpy array of dtype object is placed into an ObjectArray,
a read-only ObjectArray copy of the original array will first be
created, and then, this newly created ObjectArray will be stored
by the outer ObjectArray.
An ObjectArray itself has a read-only mode, so that, in addition to its
stored data, the ObjectArray itself can be protected against undesired
modifications.
An interesting feature of PyTorch: if one slices a tensor A and the
result is a new tensor B, and if B is sharing storage memory with A,
then A.storage().data_ptr() and B.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 self._indices.storage().data_ptr() != source._indices.storage().data_ptr():
self._objects = clone(self._objects)
self._device = torch.device("cpu")
self._read_only = False
@property
def shape(self) -> Size:
"""Shape of the ObjectArray, as a PyTorch Size tuple."""
return self._indices.shape
def size(self) -> Size:
"""
Get the size of the ObjectArray, as a PyTorch Size tuple.
Returns:
The size (i.e. the shape) of the ObjectArray.
"""
return self._indices.size()
@property
def ndim(self) -> int:
"""
Number of dimensions handled by the ObjectArray.
This is equivalent to getting the length of the size tuple.
"""
return self._indices.ndim
def dim(self) -> int:
"""
Get the number of dimensions handled by the ObjectArray.
This is equivalent to getting the length of the size tuple.
Returns:
The number of dimensions, as an integer.
"""
return self._indices.dim()
def numel(self) -> int:
"""
Number of elements stored by the ObjectArray.
Returns:
The number of elements, as an integer.
"""
return self._indices.numel()
def repeat(self, *sizes) -> "ObjectArray":
"""
Repeat the contents of this ObjectArray.
For example, if we have an ObjectArray `objs` which stores
`["hello", "world"]`, the following line:
objs.repeat(3)
will result in an ObjectArray which stores:
`["hello", "world", "hello", "world", "hello", "world"]`
Args:
sizes: Although this argument is named `sizes` to be compatible
with PyTorch, what is expected here is a single positional
argument, as a single integer, or as a single-element
tuple.
The given integer (which can be the argument itself, or
the integer within the given single-element tuple),
specifies how many times the stored sequence will be
repeated.
Returns:
A new ObjectArray which repeats the original one's values
"""
if len(sizes) != 1:
type_name = type(self).__name__
raise ValueError(
f"The `repeat(...)` method of {type_name} expects exactly one positional argument."
f" This is because {type_name} supports only 1-dimensional storage."
f" The received positional arguments are: {sizes}."
)
if isinstance(sizes, tuple):
if len(sizes) == 1:
sizes = sizes[0]
else:
type_name = type(self).__name__
raise ValueError(
f"The `repeat(...)` method of {type_name} can accept a size tuple with only one element."
f" This is because {type_name} supports only 1-dimensional storage."
f" The received size tuple is: {sizes}."
)
num_repetitions = int(sizes[0])
self_length = len(self)
result = ObjectArray(num_repetitions * self_length)
source_index = 0
for result_index in range(len(result)):
result[result_index] = self[source_index]
source_index = (source_index + 1) % self_length
return result
@property
def device(self) -> Device:
"""
The device which stores the elements of the ObjectArray.
In the case of ObjectArray, this property always returns
the CPU device.
Returns:
The CPU device, as a torch.device object.
"""
return self._device
@property
def dtype(self) -> DType:
"""
The dtype of the elements stored by the ObjectArray.
In the case of ObjectArray, the dtype is always `object`.
"""
return object
def __getitem__(self, i: Any) -> Any:
if is_integer(i):
index = int(self._indices[i])
return self._objects[index]
else:
indices = self._indices[i]
same_ptr = indices.storage().data_ptr() == self._indices.storage().data_ptr()
result = ObjectArray(len(indices))
if same_ptr:
result._indices[:] = indices
result._objects = self._objects
else:
result._objects = []
for index in indices:
result._objects.append(self._objects[int(index)])
result._read_only = self._read_only
return result
def __setitem__(self, i: Any, x: Any):
self.set_item(i, x)
def set_item(self, i: Any, x: Any, *, memo: Optional[dict] = None):
"""
Set the i-th item of the ObjectArray as x.
Args:
i: An index or a slice.
x: The object that will be put into the ObjectArray.
memo: Optionally a dictionary which maps from the ids of the
already placed objects to their clones within ObjectArray.
In most scenarios, when this method is called from outside,
this can be left as None.
"""
from .immutable import as_immutable
if memo is None:
memo = {}
memo[id(self)] = self
if self._read_only:
raise ValueError("This ObjectArray is read-only, therefore, modification is not allowed.")
if is_integer(i):
index = int(self._indices[i])
self._objects[index] = as_immutable(x, memo=memo)
else:
indices = self._indices[i]
if not isinstance(x, Iterable):
raise TypeError(f"Expected an iterable, but got {repr(x)}")
if indices.ndim != 1:
raise ValueError(
"Received indices that would change the dimensionality of the ObjectArray."
" However, an ObjectArray can only be 1-dimensional."
)
slice_refers_to_whole_array = (len(indices) == len(self._indices)) and torch.all(indices == self._indices)
if slice_refers_to_whole_array:
memo[id(x)] = self
if not hasattr(x, "__len__"):
x = list(x)
if len(x) != len(indices):
raise TypeError(
f"The slicing operation refers to {len(indices)} elements."
f" However, the given objects sequence has {len(x)} elements."
)
for q, obj in enumerate(x):
index = int(indices[q])
self._objects[index] = as_immutable(obj, memo=memo)
def __len__(self) -> int:
return len(self._indices)
def __iter__(self):
for i in range(len(self)):
yield self[i]
def clone(self, *, preserve_read_only: bool = False, memo: Optional[dict] = None) -> Iterable:
"""
Get a deep copy of the ObjectArray.
Args:
preserve_read_only: Whether or not to preserve the read-only
attribute. Note that the default value is False, which
means that the newly made clone will NOT be read-only
even if the original ObjectArray is.
memo: Optionally a dictionary which maps from the ids of the
already cloned objects to their clones.
In most scenarios, when this method is called from outside,
this can be left as None.
Returns:
The clone of the original ObjectArray.
"""
from .cloning import deep_clone
if memo is None:
memo = {}
self_id = id(self)
if self_id in memo:
return memo[self_id]
if not preserve_read_only:
return self.numpy(memo=memo)
else:
result = ObjectArray(len(self))
memo[self_id] = result
for i, item in enumerate(self):
result[i] = deep_clone(item, otherwise_deepcopy=True, memo=memo)
return result
def __copy__(self) -> "ObjectArray":
return self.clone(preserve_read_only=True)
def __deepcopy__(self, memo: Optional[dict]) -> "ObjectArray":
if memo is None:
memo = {}
return self.clone(preserve_read_only=True, memo=memo)
def __setstate__(self, state: dict):
self.__dict__.update(state)
# After pickling and unpickling, numpy arrays become mutable.
# Since we are dealing with immutable containers here, we need to forcefully make all numpy arrays read-only.
for v in self:
if isinstance(v, np.ndarray):
v.flags["WRITEABLE"] = False
# def __getstate__(self) -> dict:
# from .cloning import deep_clone
# self_id = id(self)
# memo = {self_id: self}
# cloned_dict = deep_clone(self.__dict__, otherwise_deepcopy=True, memo=memo)
# return cloned_dict
def get_read_only_view(self) -> "ObjectArray":
"""
Get a read-only view of this ObjectArray.
"""
result = self[:]
result._read_only = True
return result
@property
def is_read_only(self) -> bool:
"""
True if this ObjectArray is read-only; False otherwise.
"""
return self._read_only
def storage(self) -> ObjectArrayStorage:
return ObjectArrayStorage(self)
def 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:
The numpy array that will be converted to `ObjectArray`.
Returns:
The ObjectArray counterpart of the given numpy array.
"""
if isinstance(ndarray, np.ndarray):
if ndarray.dtype == np.dtype(object):
n = len(ndarray)
result = ObjectArray(n)
for i, element in enumerate(ndarray):
result[i] = element
return result
else:
raise ValueError(
f"The dtype of the given array was expected as `object`."
f" However, the dtype was encountered as {ndarray.dtype}."
)
else:
raise TypeError(f"Expected a `numpy.ndarray` instance, but received an object of type {type(ndarray)}.")
device: Union[str, torch.device]
property
readonly
¶
The device which stores the elements of the ObjectArray. In the case of ObjectArray, this property always returns the CPU device.
Returns:
| Type | Description |
|---|---|
Union[str, torch.device] |
The CPU device, as a torch.device object. |
dtype: Union[str, torch.dtype, numpy.dtype, Type]
property
readonly
¶
The dtype of the elements stored by the ObjectArray.
In the case of ObjectArray, the dtype is always object.
is_read_only: bool
property
readonly
¶
True if this ObjectArray is read-only; False otherwise.
ndim: int
property
readonly
¶
Number of dimensions handled by the ObjectArray. This is equivalent to getting the length of the size tuple.
shape: Union[int, torch.Size]
property
readonly
¶
Shape of the ObjectArray, as a PyTorch Size tuple.
__init__(self, size=None, *, slice_of=None)
special
¶
__init__(...): Instantiate a new ObjectArray.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
size |
Union[int, torch.Size] |
Length of the ObjectArray. If this argument is present and
is an integer |
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 self._indices.storage().data_ptr() != source._indices.storage().data_ptr():
self._objects = clone(self._objects)
self._device = torch.device("cpu")
self._read_only = False
clone(self, *, preserve_read_only=False, memo=None)
¶
Get a deep copy of the ObjectArray.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
preserve_read_only |
bool |
Whether or not to preserve the read-only attribute. Note that the default value is False, which means that the newly made clone will NOT be read-only even if the original ObjectArray is. |
False |
memo |
Optional[dict] |
Optionally a dictionary which maps from the ids of the already cloned objects to their clones. In most scenarios, when this method is called from outside, this can be left as None. |
None |
Returns:
| Type | Description |
|---|---|
Iterable |
The clone of the original ObjectArray. |
Source code in evotorch/tools/objectarray.py
def clone(self, *, preserve_read_only: bool = False, memo: Optional[dict] = None) -> Iterable:
"""
Get a deep copy of the ObjectArray.
Args:
preserve_read_only: Whether or not to preserve the read-only
attribute. Note that the default value is False, which
means that the newly made clone will NOT be read-only
even if the original ObjectArray is.
memo: Optionally a dictionary which maps from the ids of the
already cloned objects to their clones.
In most scenarios, when this method is called from outside,
this can be left as None.
Returns:
The clone of the original ObjectArray.
"""
from .cloning import deep_clone
if memo is None:
memo = {}
self_id = id(self)
if self_id in memo:
return memo[self_id]
if not preserve_read_only:
return self.numpy(memo=memo)
else:
result = ObjectArray(len(self))
memo[self_id] = result
for i, item in enumerate(self):
result[i] = deep_clone(item, otherwise_deepcopy=True, memo=memo)
return result
dim(self)
¶
Get the number of dimensions handled by the ObjectArray. This is equivalent to getting the length of the size tuple.
Returns:
| Type | Description |
|---|---|
int |
The number of dimensions, as an integer. |
from_numpy(ndarray)
staticmethod
¶
Convert a numpy array of dtype object to an ObjectArray.
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:
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 single-element
tuple.
The given integer (which can be the argument itself, or
the integer within the given single-element tuple),
specifies how many times the stored sequence will be
repeated.
Returns:
A new ObjectArray which repeats the original one's values
"""
if len(sizes) != 1:
type_name = type(self).__name__
raise ValueError(
f"The `repeat(...)` method of {type_name} expects exactly one positional argument."
f" This is because {type_name} supports only 1-dimensional storage."
f" The received positional arguments are: {sizes}."
)
if isinstance(sizes, tuple):
if len(sizes) == 1:
sizes = sizes[0]
else:
type_name = type(self).__name__
raise ValueError(
f"The `repeat(...)` method of {type_name} can accept a size tuple with only one element."
f" This is because {type_name} supports only 1-dimensional storage."
f" The received size tuple is: {sizes}."
)
num_repetitions = int(sizes[0])
self_length = len(self)
result = ObjectArray(num_repetitions * self_length)
source_index = 0
for result_index in range(len(result)):
result[result_index] = self[source_index]
source_index = (source_index + 1) % self_length
return result
set_item(self, i, x, *, memo=None)
¶
Set the i-th item of the ObjectArray as x.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
i |
Any |
An index or a slice. |
required |
x |
Any |
The object that will be put into the ObjectArray. |
required |
memo |
Optional[dict] |
Optionally a dictionary which maps from the ids of the already placed objects to their clones within ObjectArray. In most scenarios, when this method is called from outside, this can be left as None. |
None |
Source code in evotorch/tools/objectarray.py
def set_item(self, i: Any, x: Any, *, memo: Optional[dict] = None):
"""
Set the i-th item of the ObjectArray as x.
Args:
i: An index or a slice.
x: The object that will be put into the ObjectArray.
memo: Optionally a dictionary which maps from the ids of the
already placed objects to their clones within ObjectArray.
In most scenarios, when this method is called from outside,
this can be left as None.
"""
from .immutable import as_immutable
if memo is None:
memo = {}
memo[id(self)] = self
if self._read_only:
raise ValueError("This ObjectArray is read-only, therefore, modification is not allowed.")
if is_integer(i):
index = int(self._indices[i])
self._objects[index] = as_immutable(x, memo=memo)
else:
indices = self._indices[i]
if not isinstance(x, Iterable):
raise TypeError(f"Expected an iterable, but got {repr(x)}")
if indices.ndim != 1:
raise ValueError(
"Received indices that would change the dimensionality of the ObjectArray."
" However, an ObjectArray can only be 1-dimensional."
)
slice_refers_to_whole_array = (len(indices) == len(self._indices)) and torch.all(indices == self._indices)
if slice_refers_to_whole_array:
memo[id(x)] = self
if not hasattr(x, "__len__"):
x = list(x)
if len(x) != len(indices):
raise TypeError(
f"The slicing operation refers to {len(indices)} elements."
f" However, the given objects sequence has {len(x)} elements."
)
for q, obj in enumerate(x):
index = int(indices[q])
self._objects[index] = as_immutable(obj, memo=memo)
size(self)
¶
Get the size of the ObjectArray, as a PyTorch Size tuple.
Returns:
| Type | Description |
|---|---|
Union[int, torch.Size] |
The size (i.e. the shape) of the ObjectArray. |