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40,291 | pyglove.core.object_utils.common_traits | __str__ | Returns the full (maybe multi-line) representation of this object. | def __str__(self) -> str:
"""Returns the full (maybe multi-line) representation of this object."""
kwargs = dict(self.__str_format_kwargs__)
kwargs.update(thread_local.thread_local_kwargs(_TLS_STR_FORMAT_KWARGS))
return self._maybe_quote(self.format(**kwargs), **kwargs)
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|
40,292 | pyglove.core.symbolic.base | _error_message | Create error message to include path information. | def _error_message(self, message: str) -> str:
"""Create error message to include path information."""
return object_utils.message_on_path(message, self.sym_path)
| (self, message: str) -> str | [
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|
40,293 | pyglove.core.symbolic.object | _init_kwargs | null | def _init_kwargs(self) -> typing.Dict[str, Any]:
kwargs = super()._init_kwargs()
kwargs.update(self._sym_attributes)
return kwargs
| (self) -> Dict[str, Any] | [
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|
40,294 | pyglove.core.object_utils.common_traits | _maybe_quote | Maybe quote the formatted string with markdown. | def _maybe_quote(
self,
s: str,
*,
compact: bool = False,
root_indent: int = 0,
markdown: bool = False,
**kwargs
) -> str:
"""Maybe quote the formatted string with markdown."""
del kwargs
if not markdown or root_indent > 0:
return s
if compact:
return f'`{s}`'
else:
return f'\n```\n{s}\n```\n'
| (self, s: str, *, compact: bool = False, root_indent: int = 0, markdown: bool = False, **kwargs) -> str | [
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|
40,295 | pyglove.core.symbolic.base | _notify_field_updates | Notify field updates. | def _notify_field_updates(
self,
field_updates: List[FieldUpdate],
notify_parents: bool = True) -> None:
"""Notify field updates."""
per_target_updates = dict()
def _get_target_updates(
target: 'Symbolic'
) -> Dict[object_utils.KeyPath, FieldUpdate]:
target_id = id(target)
if target_id not in per_target_updates:
per_target_updates[target_id] = (target, dict())
return per_target_updates[target_id][1]
for update in field_updates:
target = update.target
while target is not None:
target_updates = _get_target_updates(target)
if target._subscribes_field_updates: # pylint: disable=protected-access
relative_path = update.path - target.sym_path
target_updates[relative_path] = update
target = target.sym_parent
# Trigger the notification bottom-up, thus the parent node will always
# be notified after the child nodes.
for target, updates in sorted(per_target_updates.values(),
key=lambda x: x[0].sym_path,
reverse=True):
# Reset content-based cache for the object being notified.
target._set_raw_attr('_sym_puresymbolic', None) # pylint: disable=protected-access
target._set_raw_attr('_sym_missing_values', None) # pylint: disable=protected-access
target._set_raw_attr('_sym_nondefault_values', None) # pylint: disable=protected-access
target._on_change(updates) # pylint: disable=protected-access
# If `notify_parents` is set to False, stop notifications once `self`
# is processed.
if target is self and not notify_parents:
break
| (self, field_updates: List[pyglove.core.symbolic.base.FieldUpdate], notify_parents: bool = True) -> NoneType | [
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|
40,296 | pyglove.core.symbolic.object | _on_bound | Event that is triggered when any value in the subtree are set/updated.
NOTE(daiyip): This is the best place to set derived members from members
registered by the schema. It's called when any value in the sub-tree is
modified, thus making sure derived members are up-to-date.
When derived members are expensive to create/update, you can implement
_init, _on_rebound, _on_subtree_rebound to update derived members only when
they are impacted.
_on_bound is not called on per-field basis, it's called at most once
during a rebind call (though many fields may be updated)
and during __init__.
| def _on_bound(self) -> None:
"""Event that is triggered when any value in the subtree are set/updated.
NOTE(daiyip): This is the best place to set derived members from members
registered by the schema. It's called when any value in the sub-tree is
modified, thus making sure derived members are up-to-date.
When derived members are expensive to create/update, you can implement
_init, _on_rebound, _on_subtree_rebound to update derived members only when
they are impacted.
_on_bound is not called on per-field basis, it's called at most once
during a rebind call (though many fields may be updated)
and during __init__.
"""
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|
40,297 | pyglove.core.symbolic.object | _on_change | Event that is triggered when field values in the subtree are updated.
This event will be called
* On per-field basis when object is modified via attribute.
* In batch when multiple fields are modified via `rebind` method.
When a field in an object tree is updated, all ancestors' `_on_change` event
will be triggered in order, from the nearest one to furthest one.
Args:
field_updates: Updates made to the subtree. Key path is relative to
current object.
Returns:
it will call `_on_bound` and return the return value of `_on_bound`.
| def _on_change(self,
field_updates: Dict[object_utils.KeyPath, base.FieldUpdate]):
"""Event that is triggered when field values in the subtree are updated.
This event will be called
* On per-field basis when object is modified via attribute.
* In batch when multiple fields are modified via `rebind` method.
When a field in an object tree is updated, all ancestors' `_on_change` event
will be triggered in order, from the nearest one to furthest one.
Args:
field_updates: Updates made to the subtree. Key path is relative to
current object.
Returns:
it will call `_on_bound` and return the return value of `_on_bound`.
"""
del field_updates
return self._on_bound()
| (self, field_updates: Dict[pyglove.core.object_utils.value_location.KeyPath, pyglove.core.symbolic.base.FieldUpdate]) | [
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|
40,298 | pyglove.core.symbolic.object | _on_init | Event that is triggered at then end of __init__. | def _on_init(self):
"""Event that is triggered at then end of __init__."""
self._on_bound()
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|
40,299 | pyglove.core.symbolic.object | _on_parent_change | Event that is triggered after the symbolic parent changes. | def _on_parent_change(
self,
old_parent: Optional[base.Symbolic],
new_parent: Optional[base.Symbolic]):
"""Event that is triggered after the symbolic parent changes."""
del old_parent, new_parent
| (self, old_parent: Optional[pyglove.core.symbolic.base.Symbolic], new_parent: Optional[pyglove.core.symbolic.base.Symbolic]) | [
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|
40,300 | pyglove.core.symbolic.object | _on_path_change | Event that is triggered after the symbolic path changes. | def _on_path_change(
self, old_path: object_utils.KeyPath, new_path: object_utils.KeyPath):
"""Event that is triggered after the symbolic path changes."""
del old_path, new_path
| (self, old_path: pyglove.core.object_utils.value_location.KeyPath, new_path: pyglove.core.object_utils.value_location.KeyPath) | [
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|
40,301 | pyglove.core.symbolic.base | _relocate_if_symbolic | Relocate if a symbolic value is to be inserted as member.
NOTE(daiyip): when a symbolic value is inserted into the object tree,
if it already has a parent, we need to make a shallow copy of this object
to avoid multiple parents. Otherwise we need to set its parent and root_path
according to current object.
Args:
key: Key used to insert the value.
value: formal value to be inserted.
Returns:
Formalized value that is ready for insertion as members.
| def _relocate_if_symbolic(self, key: Union[str, int], value: Any) -> Any:
"""Relocate if a symbolic value is to be inserted as member.
NOTE(daiyip): when a symbolic value is inserted into the object tree,
if it already has a parent, we need to make a shallow copy of this object
to avoid multiple parents. Otherwise we need to set its parent and root_path
according to current object.
Args:
key: Key used to insert the value.
value: formal value to be inserted.
Returns:
Formalized value that is ready for insertion as members.
"""
if isinstance(value, Symbolic):
# NOTE(daiyip): make a copy of symbolic object if it belongs to another
# object tree, this prevents it from having multiple parents. See
# List._formalized_value for similar logic.
root_path = object_utils.KeyPath(key, self.sym_path)
if (value.sym_parent is not None and
(value.sym_parent is not self
or root_path != value.sym_path)):
value = value.clone()
if isinstance(value, TopologyAware):
value.sym_setpath(object_utils.KeyPath(key, self.sym_path))
value.sym_setparent(self._sym_parent_for_children())
return value
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|
40,302 | pyglove.core.symbolic.base | _set_item_of_current_tree | Set a field of current tree by key path and return its parent. | def _set_item_of_current_tree(
self, path: object_utils.KeyPath, value: Any) -> Optional[FieldUpdate]:
"""Set a field of current tree by key path and return its parent."""
assert isinstance(path, object_utils.KeyPath), path
if not path:
raise KeyError(
self._error_message(
f'Root key \'$\' cannot be used in '
f'{self.__class__.__name__}.rebind. '
f'Encountered {path!r}'))
parent_node = path.parent.query(self)
if not isinstance(parent_node, Symbolic):
raise KeyError(
f'Cannot rebind key {path.key!r}: {parent_node!r} is not a '
f'symbolic type. (path=\'{path.parent}\')')
if treats_as_sealed(parent_node):
raise WritePermissionError(
f'Cannot rebind key {path.key!r} of '
f'sealed {parent_node.__class__.__name__}: {parent_node!r}. '
f'(path=\'{path.parent}\')')
return parent_node._set_item_without_permission_check(path.key, value) # pylint: disable=protected-access
| (self, path: pyglove.core.object_utils.value_location.KeyPath, value: Any) -> Optional[pyglove.core.symbolic.base.FieldUpdate] | [
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|
40,303 | pyglove.core.symbolic.object | _set_item_without_permission_check | Set item without permission check. | def _set_item_without_permission_check( # pytype: disable=signature-mismatch # overriding-parameter-type-checks
self, key: str, value: Any) -> Optional[base.FieldUpdate]:
"""Set item without permission check."""
return self._sym_attributes._set_item_without_permission_check(key, value) # pylint: disable=protected-access
| (self, key: str, value: Any) -> Optional[pyglove.core.symbolic.base.FieldUpdate] | [
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|
40,304 | pyglove.core.symbolic.base | _set_raw_attr | Set raw property without trigger __setattr__. | def _set_raw_attr(self, name: str, value: Any) -> 'Symbolic':
"""Set raw property without trigger __setattr__."""
# `object.__setattr__` adds a property to the instance without side effects.
object.__setattr__(self, name, value)
return self
| (self, name: str, value: Any) -> pyglove.core.symbolic.base.Symbolic | [
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|
40,305 | pyglove.core.symbolic.object | _sym_clone | Copy flags. | def _sym_clone(self, deep: bool, memo: Any = None) -> 'Object':
"""Copy flags."""
kwargs = dict()
for k, v in self._sym_attributes.sym_items():
if deep or isinstance(v, base.Symbolic):
v = base.clone(v, deep, memo)
kwargs[k] = v
return self.__class__(allow_partial=self._allow_partial,
sealed=self._sealed,
**kwargs) # pytype: disable=not-instantiable
| (self, deep: bool, memo: Optional[Any] = None) -> pyglove.core.symbolic.object.Object | [
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|
40,306 | pyglove.core.symbolic.object | _sym_getattr | Get symbolic field by key. | def _sym_getattr( # pytype: disable=signature-mismatch # overriding-parameter-type-checks
self, key: str) -> Any:
"""Get symbolic field by key."""
return self._sym_attributes.sym_getattr(key)
| (self, key: str) -> Any | [
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|
40,307 | pyglove.core.symbolic.base | _sym_inferred | null | def _sym_inferred(self, key: Union[str, int], **kwargs) -> Any:
v = self.sym_getattr(key)
if isinstance(v, Inferential):
v = v.infer(**kwargs)
return v
| (self, key: Union[str, int], **kwargs) -> Any | [
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|
40,308 | pyglove.core.symbolic.object | _sym_missing | Returns missing values. | def _sym_missing(self) -> Dict[str, Any]:
"""Returns missing values."""
# Invalidate the cache of child attributes' missing values before calling
# `Dict.sym_missing`.
setattr(self._sym_attributes, '_sym_missing_values', None)
return self._sym_attributes.sym_missing(flatten=False)
| (self) -> Dict[str, Any] | [
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|
40,309 | pyglove.core.symbolic.object | _sym_nondefault | Returns non-default values. | def _sym_nondefault(self) -> Dict[str, Any]:
"""Returns non-default values."""
# Invalidate the cache of child attributes' non-default values before
# calling `Dict.sym_nondefault`.
setattr(self._sym_attributes, '_sym_nondefault_values', None)
return self._sym_attributes.sym_nondefault(flatten=False)
| (self) -> Dict[str, Any] | [
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|
40,310 | pyglove.core.symbolic.base | _sym_parent_for_children | Returns the symbolic parent for children. | def _sym_parent_for_children(self) -> Optional['Symbolic']:
"""Returns the symbolic parent for children."""
return self
| (self) -> Optional[pyglove.core.symbolic.base.Symbolic] | [
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|
40,311 | pyglove.core.symbolic.object | _sym_rebind | Rebind current object using object-form members. | def _sym_rebind(
self, path_value_pairs: Dict[object_utils.KeyPath, Any]
) -> List[base.FieldUpdate]:
"""Rebind current object using object-form members."""
if base.treats_as_sealed(self):
raise base.WritePermissionError(
f'Cannot rebind a sealed {self.__class__.__name__}.')
return self._sym_attributes._sym_rebind(path_value_pairs) # pylint: disable=protected-access
| (self, path_value_pairs: Dict[pyglove.core.object_utils.value_location.KeyPath, Any]) -> List[pyglove.core.symbolic.base.FieldUpdate] | [
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|
40,312 | pyglove.core.symbolic.object | _update_children_paths | Update children paths according to root_path of current node. | def _update_children_paths(
self,
old_path: object_utils.KeyPath,
new_path: object_utils.KeyPath) -> None:
"""Update children paths according to root_path of current node."""
self._sym_attributes.sym_setpath(new_path)
self._on_path_change(old_path, new_path)
| (self, old_path: pyglove.core.object_utils.value_location.KeyPath, new_path: pyglove.core.object_utils.value_location.KeyPath) -> NoneType | [
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|
40,313 | pyglove.core.symbolic.base | clone | Clones current object symbolically.
Args:
deep: If True, perform deep copy (equivalent to copy.deepcopy). Otherwise
shallow copy (equivalent to copy.copy).
memo: Memo object for deep clone.
override: An optional dict of key path to new values to override cloned
value.
Returns:
A copy of self.
| def clone(
self,
deep: bool = False,
memo: Optional[Any] = None,
override: Optional[Dict[str, Any]] = None
) -> 'Symbolic':
"""Clones current object symbolically.
Args:
deep: If True, perform deep copy (equivalent to copy.deepcopy). Otherwise
shallow copy (equivalent to copy.copy).
memo: Memo object for deep clone.
override: An optional dict of key path to new values to override cloned
value.
Returns:
A copy of self.
"""
return self.sym_clone(deep, memo, override)
| (self, deep: bool = False, memo: Optional[Any] = None, override: Optional[Dict[str, Any]] = None) -> pyglove.core.symbolic.base.Symbolic | [
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|
40,314 | pyglove.core.symbolic.object | format | Formats this object. | def format(self,
compact: bool = False,
verbose: bool = False,
root_indent: int = 0,
**kwargs) -> str:
"""Formats this object."""
return self._sym_attributes.format(
compact,
verbose,
root_indent,
cls_name=self.__class__.__name__,
key_as_attribute=True,
bracket_type=object_utils.BracketType.ROUND,
**kwargs)
| (self, compact: bool = False, verbose: bool = False, root_indent: int = 0, **kwargs) -> str | [
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|
40,315 | pyglove.core.symbolic.base | inspect | Inspects current object by printing out selected values.
Example::
@pg.members([
('x', pg.typing.Int(0)),
('y', pg.typing.Str())
])
class A(pg.Object):
pass
value = {
'a1': A(x=0, y=0),
'a2': [A(x=1, y=1), A(x=1, y=2)],
'a3': {
'p': A(x=2, y=1),
'q': A(x=2, y=2)
}
}
# Inspect without constraint,
# which is equivalent as `print(value.format(hide_default_values=True))`
# Shall print:
# {
# a1 = A(y=0)
# a2 = [
# 0: A(x=1, y=1)
# 1: A(x=1, y=2)
# a3 = {
# p = A(x=2, y=1)
# q = A(x=2, y=2)
# }
# }
value.inspect(hide_default_values=True)
# Inspect by path regex.
# Shall print:
# {'a3.p': A(x=2, y=1)}
value.inspect(r'.*p')
# Inspect by value.
# Shall print:
# {
# 'a3.p.x': 2,
# 'a3.q.x': 2,
# 'a3.q.y': 2,
# }
value.inspect(where=lambda v: v==2)
# Inspect by path, value and parent.
# Shall print:
# {
# 'a2[1].y': 2
# }
value.inspect(
r'.*y', where=lambda v, p: v > 1 and isinstance(p, A) and p.x == 1))
# Inspect by custom_selector.
# Shall print:
# {
# 'a2[0].x': 1,
# 'a2[0].y': 1,
# 'a3.q.x': 2,
# 'a3.q.y': 2
# }
value.inspect(
custom_selector=lambda k, v, p: (
len(k) == 3 and isinstance(p, A) and p.x == v))
Args:
path_regex: Optional regex expression to constrain path.
where: Optional callable to constrain value and parent when path matches
`path_regex` or `path_regex` is not provided. The signature is:
`(value) -> should_select`, or `(value, parent) -> should_select`.
custom_selector: Optional callable object as custom selector. When
`custom_selector` is provided, `path_regex` and `where` must be None.
The signature of `custom_selector` is:
`(key_path, value) -> should_select`
or `(key_path, value, parent) -> should_select`.
file: Output file stream. This can be any object with a `write(str)`
method.
**kwargs: Wildcard keyword arguments to pass to `format`.
| def inspect(
self,
path_regex: Optional[str] = None,
where: Optional[Union[Callable[[Any], bool],
Callable[[Any, Any], bool]]] = None,
custom_selector: Optional[Union[
Callable[[object_utils.KeyPath, Any], bool],
Callable[[object_utils.KeyPath, Any, Any], bool]]] = None,
file=sys.stdout, # pylint: disable=redefined-builtin
**kwargs) -> None:
"""Inspects current object by printing out selected values.
Example::
@pg.members([
('x', pg.typing.Int(0)),
('y', pg.typing.Str())
])
class A(pg.Object):
pass
value = {
'a1': A(x=0, y=0),
'a2': [A(x=1, y=1), A(x=1, y=2)],
'a3': {
'p': A(x=2, y=1),
'q': A(x=2, y=2)
}
}
# Inspect without constraint,
# which is equivalent as `print(value.format(hide_default_values=True))`
# Shall print:
# {
# a1 = A(y=0)
# a2 = [
# 0: A(x=1, y=1)
# 1: A(x=1, y=2)
# a3 = {
# p = A(x=2, y=1)
# q = A(x=2, y=2)
# }
# }
value.inspect(hide_default_values=True)
# Inspect by path regex.
# Shall print:
# {'a3.p': A(x=2, y=1)}
value.inspect(r'.*p')
# Inspect by value.
# Shall print:
# {
# 'a3.p.x': 2,
# 'a3.q.x': 2,
# 'a3.q.y': 2,
# }
value.inspect(where=lambda v: v==2)
# Inspect by path, value and parent.
# Shall print:
# {
# 'a2[1].y': 2
# }
value.inspect(
r'.*y', where=lambda v, p: v > 1 and isinstance(p, A) and p.x == 1))
# Inspect by custom_selector.
# Shall print:
# {
# 'a2[0].x': 1,
# 'a2[0].y': 1,
# 'a3.q.x': 2,
# 'a3.q.y': 2
# }
value.inspect(
custom_selector=lambda k, v, p: (
len(k) == 3 and isinstance(p, A) and p.x == v))
Args:
path_regex: Optional regex expression to constrain path.
where: Optional callable to constrain value and parent when path matches
`path_regex` or `path_regex` is not provided. The signature is:
`(value) -> should_select`, or `(value, parent) -> should_select`.
custom_selector: Optional callable object as custom selector. When
`custom_selector` is provided, `path_regex` and `where` must be None.
The signature of `custom_selector` is:
`(key_path, value) -> should_select`
or `(key_path, value, parent) -> should_select`.
file: Output file stream. This can be any object with a `write(str)`
method.
**kwargs: Wildcard keyword arguments to pass to `format`.
"""
if path_regex is None and where is None and custom_selector is None:
v = self
else:
v = query(self, path_regex, where, False, custom_selector)
object_utils.print(v, file=file, **kwargs)
| (self, path_regex: Optional[str] = None, where: Union[Callable[[Any], bool], Callable[[Any, Any], bool], NoneType] = None, custom_selector: Union[Callable[[pyglove.core.object_utils.value_location.KeyPath, Any], bool], Callable[[pyglove.core.object_utils.value_location.KeyPath, Any, Any], bool], NoneType] = None, file=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, **kwargs) -> NoneType | [
0.11083894968032837,
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|
40,316 | pyglove.core.symbolic.base | missing_values | Alias for `sym_missing`. | def missing_values(self, flatten: bool = True) -> Dict[str, Any]:
"""Alias for `sym_missing`."""
return self.sym_missing(flatten)
| (self, flatten: bool = True) -> Dict[str, Any] | [
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|
40,317 | pyglove.core.symbolic.base | non_default_values | Alias for `sym_nondefault`. | def non_default_values(
self, flatten: bool = True) -> Dict[Union[int, str], Any]:
"""Alias for `sym_nondefault`."""
return self.sym_nondefault(flatten)
| (self, flatten: bool = True) -> Dict[Union[int, str], Any] | [
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|
40,318 | pyglove.core.symbolic.base | rebind | Alias for `sym_rebind`.
Alias for `sym_rebind`. `rebind` is the recommended way for mutating
symbolic objects in PyGlove:
* It allows mutations not only on immediate child nodes, but on the
entire sub-tree.
* It allows mutations by rules via passing a callable object as the
value for `path_value_pairs`.
* It batches the updates from multiple sub-nodes, which triggers the
`_on_change` or `_on_bound` event once for recomputing the parent
object's internal states.
* It respects the "sealed" flag of the object or the `pg.seal`
context manager to trigger permission error.
Example::
#
# Rebind on pg.Object subclasses.
#
@pg.members([
('x', pg.typing.Dict([
('y', pg.typing.Int(default=0))
])),
('z', pg.typing.Int(default=1))
])
class A(pg.Object):
pass
a = A()
# Rebind using path-value pairs.
a.rebind({
'x.y': 1,
'z': 0
})
# Rebind using **kwargs.
a.rebind(x={y: 1}, z=0)
# Rebind using rebinders.
# Rebind based on path.
a.rebind(lambda k, v: 1 if k == 'x.y' else v)
# Rebind based on key.
a.rebind(lambda k, v: 1 if k and k.key == 'y' else v)
# Rebind based on value.
a.rebind(lambda k, v: 0 if v == 1 else v)
# Rebind baesd on value and parent.
a.rebind(lambda k, v, p: (0 if isinstance(p, A) and isinstance(v, int)
else v))
# Rebind on pg.Dict.
#
d = pg.Dict(value_spec=pg.typing.Dict([
('a', pg.typing.Dict([
('b', pg.typing.Int()),
])),
('c', pg.typing.Float())
])
# Rebind using **kwargs.
d.rebind(a={b: 1}, c=1.0)
# Rebind using key path to value dict.
d.rebind({
'a.b': 2,
'c': 2.0
})
# NOT OKAY: **kwargs and dict/rebinder cannot be used at the same time.
d.rebind({'a.b': 2}, c=2)
# Rebind with rebinder by path (on subtree).
d.rebind(lambda k, v: 1 if k.key == 'b' else v)
# Rebind with rebinder by value (on subtree).
d.rebind(lambda k, v: 0 if isinstance(v, int) else v)
#
# Rebind on pg.List.
#
l = pg.List([{
'a': 'foo',
'b': 0,
}
],
value_spec = pg.typing.List(pg.typing.Dict([
('a', pg.typing.Str()),
('b', pg.typing.Int())
]), max_size=10))
# Rebind using integer as list index: update semantics on list[0].
l.rebind({
0: {
'a': 'bar',
'b': 1
}
})
# Rebind: trigger append semantics when index is larger than list length.
l.rebind({
999: {
'a': 'fun',
'b': 2
}
})
# Rebind using key path.
l.rebind({
'[0].a': 'bar2'
'[1].b': 3
})
# Rebind using function (rebinder).
# Change all integers to 0 in sub-tree.
l.rebind(lambda k, v: v if not isinstance(v, int) else 0)
Args:
path_value_pairs: A dictionary of key/or key path to new field value, or
a function that generate updates based on the key path, value and
parent of each node under current object. We use terminology 'rebinder'
for this type of functions. The signature of a rebinder is:
`(key_path: pg.KeyPath, value: Any)` or
`(key_path: pg.KeyPath, value: Any, parent: pg.Symbolic)`
raise_on_no_change: If True, raises ``ValueError`` when there are no
values to change. This is useful when rebinder is used, which may or
may not generate any updates.
notify_parents: If True (default), parents will be notified upon change.
Otherwisee only the current object and the impacted children will
be notified. A most common use case for setting this flag to False
is when users want to rebind a child within the parent `_on_bound`
method.
skip_notification: If True, there will be no ``_on_change`` event
triggered from current `rebind`. If None, the default value will be
inferred from the :func:`pyglove.notify_on_change` context manager.
Use it only when you are certain that current rebind does not
invalidate internal states of its object tree.
**kwargs: For ``pg.Dict`` and ``pg.Object`` subclasses, user can use
keyword arguments (in format of `<field_name>=<field_value>`) to
directly modify immediate child nodes.
Returns:
Self.
Raises:
WritePermissionError: If object is sealed.
KeyError: If update location specified by key or key path is not aligned
with the schema of the object tree.
TypeError: If updated field value type does not conform to field spec.
ValueError: If updated field value is not acceptable according to field
spec, or nothing is updated and `raise_on_no_change` is set to
True.
| def rebind(
self,
path_value_pairs: Optional[Union[
Dict[
Union[object_utils.KeyPath, str, int],
Any],
Callable]] = None, # pylint: disable=g-bare-generic
*,
raise_on_no_change: bool = True,
notify_parents: bool = True,
skip_notification: Optional[bool] = None,
**kwargs) -> 'Symbolic':
"""Alias for `sym_rebind`.
Alias for `sym_rebind`. `rebind` is the recommended way for mutating
symbolic objects in PyGlove:
* It allows mutations not only on immediate child nodes, but on the
entire sub-tree.
* It allows mutations by rules via passing a callable object as the
value for `path_value_pairs`.
* It batches the updates from multiple sub-nodes, which triggers the
`_on_change` or `_on_bound` event once for recomputing the parent
object's internal states.
* It respects the "sealed" flag of the object or the `pg.seal`
context manager to trigger permission error.
Example::
#
# Rebind on pg.Object subclasses.
#
@pg.members([
('x', pg.typing.Dict([
('y', pg.typing.Int(default=0))
])),
('z', pg.typing.Int(default=1))
])
class A(pg.Object):
pass
a = A()
# Rebind using path-value pairs.
a.rebind({
'x.y': 1,
'z': 0
})
# Rebind using **kwargs.
a.rebind(x={y: 1}, z=0)
# Rebind using rebinders.
# Rebind based on path.
a.rebind(lambda k, v: 1 if k == 'x.y' else v)
# Rebind based on key.
a.rebind(lambda k, v: 1 if k and k.key == 'y' else v)
# Rebind based on value.
a.rebind(lambda k, v: 0 if v == 1 else v)
# Rebind baesd on value and parent.
a.rebind(lambda k, v, p: (0 if isinstance(p, A) and isinstance(v, int)
else v))
# Rebind on pg.Dict.
#
d = pg.Dict(value_spec=pg.typing.Dict([
('a', pg.typing.Dict([
('b', pg.typing.Int()),
])),
('c', pg.typing.Float())
])
# Rebind using **kwargs.
d.rebind(a={b: 1}, c=1.0)
# Rebind using key path to value dict.
d.rebind({
'a.b': 2,
'c': 2.0
})
# NOT OKAY: **kwargs and dict/rebinder cannot be used at the same time.
d.rebind({'a.b': 2}, c=2)
# Rebind with rebinder by path (on subtree).
d.rebind(lambda k, v: 1 if k.key == 'b' else v)
# Rebind with rebinder by value (on subtree).
d.rebind(lambda k, v: 0 if isinstance(v, int) else v)
#
# Rebind on pg.List.
#
l = pg.List([{
'a': 'foo',
'b': 0,
}
],
value_spec = pg.typing.List(pg.typing.Dict([
('a', pg.typing.Str()),
('b', pg.typing.Int())
]), max_size=10))
# Rebind using integer as list index: update semantics on list[0].
l.rebind({
0: {
'a': 'bar',
'b': 1
}
})
# Rebind: trigger append semantics when index is larger than list length.
l.rebind({
999: {
'a': 'fun',
'b': 2
}
})
# Rebind using key path.
l.rebind({
'[0].a': 'bar2'
'[1].b': 3
})
# Rebind using function (rebinder).
# Change all integers to 0 in sub-tree.
l.rebind(lambda k, v: v if not isinstance(v, int) else 0)
Args:
path_value_pairs: A dictionary of key/or key path to new field value, or
a function that generate updates based on the key path, value and
parent of each node under current object. We use terminology 'rebinder'
for this type of functions. The signature of a rebinder is:
`(key_path: pg.KeyPath, value: Any)` or
`(key_path: pg.KeyPath, value: Any, parent: pg.Symbolic)`
raise_on_no_change: If True, raises ``ValueError`` when there are no
values to change. This is useful when rebinder is used, which may or
may not generate any updates.
notify_parents: If True (default), parents will be notified upon change.
Otherwisee only the current object and the impacted children will
be notified. A most common use case for setting this flag to False
is when users want to rebind a child within the parent `_on_bound`
method.
skip_notification: If True, there will be no ``_on_change`` event
triggered from current `rebind`. If None, the default value will be
inferred from the :func:`pyglove.notify_on_change` context manager.
Use it only when you are certain that current rebind does not
invalidate internal states of its object tree.
**kwargs: For ``pg.Dict`` and ``pg.Object`` subclasses, user can use
keyword arguments (in format of `<field_name>=<field_value>`) to
directly modify immediate child nodes.
Returns:
Self.
Raises:
WritePermissionError: If object is sealed.
KeyError: If update location specified by key or key path is not aligned
with the schema of the object tree.
TypeError: If updated field value type does not conform to field spec.
ValueError: If updated field value is not acceptable according to field
spec, or nothing is updated and `raise_on_no_change` is set to
True.
"""
return self.sym_rebind(
path_value_pairs,
raise_on_no_change=raise_on_no_change,
notify_parents=notify_parents,
skip_notification=skip_notification,
**kwargs)
| (self, path_value_pairs: Union[Dict[Union[pyglove.core.object_utils.value_location.KeyPath, str, int], Any], Callable, NoneType] = None, *, raise_on_no_change: bool = True, notify_parents: bool = True, skip_notification: Optional[bool] = None, **kwargs) -> pyglove.core.symbolic.base.Symbolic | [
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|
40,319 | pyglove.core.symbolic.base | save | Saves current object using the global save handler. | def save(self, *args, **kwargs) -> Any:
"""Saves current object using the global save handler."""
return save(self, *args, **kwargs)
| (self, *args, **kwargs) -> Any | [
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|
40,320 | pyglove.core.symbolic.object | seal | Seal or unseal current object from further modification. | def seal(self, sealed: bool = True) -> 'Object':
"""Seal or unseal current object from further modification."""
self._sym_attributes.seal(sealed)
super().seal(sealed)
return self
| (self, sealed: bool = True) -> pyglove.core.symbolic.object.Object | [
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|
40,321 | pyglove.core.symbolic.base | set_accessor_writable | Sets accessor writable. | def set_accessor_writable(self, writable: bool = True) -> 'Symbolic':
"""Sets accessor writable."""
return self._set_raw_attr('_accessor_writable', writable)
| (self, writable: bool = True) -> pyglove.core.symbolic.base.Symbolic | [
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|
40,322 | pyglove.core.symbolic.base | sym_ancestor | Returns the nearest ancestor of specific classes. | def sym_ancestor(
self,
where: Optional[Callable[[Any], bool]] = None,
) -> Optional['Symbolic']:
"""Returns the nearest ancestor of specific classes."""
ancestor = self.sym_parent
where = where or (lambda x: True)
while ancestor is not None and not where(ancestor):
ancestor = ancestor.sym_parent
return ancestor
| (self, where: Optional[Callable[[Any], bool]] = None) -> Optional[pyglove.core.symbolic.base.Symbolic] | [
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|
40,323 | pyglove.core.symbolic.object | sym_attr_field | Returns the field definition for a symbolic attribute. | def sym_attr_field(
self, key: Union[str, int]
) -> Optional[pg_typing.Field]:
"""Returns the field definition for a symbolic attribute."""
return self._sym_attributes.sym_attr_field(key)
| (self, key: Union[str, int]) -> Optional[pyglove.core.typing.class_schema.Field] | [
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|
40,324 | pyglove.core.symbolic.base | sym_clone | Clones current object symbolically. | def sym_clone(self,
deep: bool = False,
memo: Optional[Any] = None,
override: Optional[Dict[str, Any]] = None):
"""Clones current object symbolically."""
assert deep or not memo
new_value = self._sym_clone(deep, memo)
if override:
new_value.sym_rebind(override, raise_on_no_change=False)
if flags.is_tracking_origin():
new_value.sym_setorigin(self, 'deepclone' if deep else 'clone')
return new_value
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|
40,325 | pyglove.core.symbolic.base | sym_contains | Returns True if the object contains sub-nodes of given value or type. | def sym_contains(
self,
value: Any = None,
type: Union[None, Type[Any], Tuple[Type[Any]]] = None # pylint: disable=redefined-builtin
) -> bool:
"""Returns True if the object contains sub-nodes of given value or type."""
return contains(self, value, type)
| (self, value: Optional[Any] = None, type: Union[NoneType, Type[Any], Tuple[Type[Any]]] = None) -> bool | [
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|
40,326 | pyglove.core.symbolic.base | sym_descendants | Returns all descendants of specific classes.
Args:
where: Optional callable object as the filter of descendants to return.
option: Descendant query options, indicating whether all matched,
immediate matched or only the matched leaf nodes will be returned.
include_self: If True, `self` will be included in the query, otherwise
only strict descendants are included.
Returns:
A list of objects that match the descendant_cls.
| def sym_descendants(
self,
where: Optional[Callable[[Any], bool]] = None,
option: DescendantQueryOption = DescendantQueryOption.ALL,
include_self: bool = False) -> List[Any]:
"""Returns all descendants of specific classes.
Args:
where: Optional callable object as the filter of descendants to return.
option: Descendant query options, indicating whether all matched,
immediate matched or only the matched leaf nodes will be returned.
include_self: If True, `self` will be included in the query, otherwise
only strict descendants are included.
Returns:
A list of objects that match the descendant_cls.
"""
descendants = []
where = where or (lambda x: True)
def visit(k, v, p):
del k, p
if not where(v):
return TraverseAction.ENTER
if not include_self and self is v:
return TraverseAction.ENTER
if option == DescendantQueryOption.IMMEDIATE:
descendants.append(v)
return TraverseAction.CONTINUE
# Dealing with option = ALL or LEAF.
leaf_descendants = []
if isinstance(v, Symbolic):
leaf_descendants = v.sym_descendants(where, option)
if option is DescendantQueryOption.ALL or not leaf_descendants:
descendants.append(v)
descendants.extend(leaf_descendants)
return TraverseAction.CONTINUE
traverse(self, visit)
return descendants
| (self, where: Optional[Callable[[Any], bool]] = None, option: pyglove.core.symbolic.base.DescendantQueryOption = <DescendantQueryOption.ALL: 0>, include_self: bool = False) -> List[Any] | [
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|
40,327 | pyglove.core.symbolic.object | sym_eq | Tests symbolic equality. | def sym_eq(self, other: Any) -> bool:
"""Tests symbolic equality."""
return self is other or (
type(self) is type(other) and base.eq(
self._sym_attributes, other._sym_attributes)) # pylint: disable=protected-access
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|
40,328 | pyglove.core.symbolic.base | sym_get | Returns a sub-node by path.
NOTE: there is no `sym_set`, use `sym_rebind`.
Args:
path: A KeyPath object or equivalence.
default: Default value if path does not exists. If absent, `KeyError` will
be thrown.
Returns:
Value of symbolic attribute specified by path if found, otherwise the
default value if it's specified.
Raises:
KeyError if `path` does not exist and `default` is not specified.
| def sym_get(
self,
path: Union[object_utils.KeyPath, str, int],
default: Any = RAISE_IF_NOT_FOUND) -> Any:
"""Returns a sub-node by path.
NOTE: there is no `sym_set`, use `sym_rebind`.
Args:
path: A KeyPath object or equivalence.
default: Default value if path does not exists. If absent, `KeyError` will
be thrown.
Returns:
Value of symbolic attribute specified by path if found, otherwise the
default value if it's specified.
Raises:
KeyError if `path` does not exist and `default` is not specified.
"""
path = object_utils.KeyPath.from_value(path)
if default is RAISE_IF_NOT_FOUND:
return path.query(self)
else:
return path.get(self, default)
| (self, path: Union[pyglove.core.object_utils.value_location.KeyPath, str, int], default: Any = (MISSING_VALUE,)) -> Any | [
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|
40,329 | pyglove.core.symbolic.base | sym_getattr | Gets a symbolic attribute.
Args:
key: Key of symbolic attribute.
default: Default value if attribute does not exist. If absent,
Returns:
Value of symbolic attribute if found, otherwise the default value
if it's specified.
Raises:
AttributeError if `key` does not exist and `default` is not provided.
| def sym_getattr(
self, key: Union[str, int], default: Any = RAISE_IF_NOT_FOUND
) -> Any:
"""Gets a symbolic attribute.
Args:
key: Key of symbolic attribute.
default: Default value if attribute does not exist. If absent,
Returns:
Value of symbolic attribute if found, otherwise the default value
if it's specified.
Raises:
AttributeError if `key` does not exist and `default` is not provided.
"""
if not self.sym_hasattr(key):
if default is RAISE_IF_NOT_FOUND:
raise AttributeError(
self._error_message(
f'{self.__class__!r} object has no symbolic attribute {key!r}.'
)
)
return default
return self._sym_getattr(key)
| (self, key: Union[str, int], default: Any = (MISSING_VALUE,)) -> Any | [
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|
40,330 | pyglove.core.symbolic.base | sym_gt | Returns if this object is symbolically greater than another object. | def sym_gt(self, other: Any) -> bool:
"""Returns if this object is symbolically greater than another object."""
return gt(self, other)
| (self, other: Any) -> bool | [
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|
40,331 | pyglove.core.symbolic.base | sym_has | Returns True if a path exists in the sub-tree.
Args:
path: A KeyPath object or equivalence.
Returns:
True if the path exists in current sub-tree, otherwise False.
| def sym_has(self, path: Union[object_utils.KeyPath, str, int]) -> bool:
"""Returns True if a path exists in the sub-tree.
Args:
path: A KeyPath object or equivalence.
Returns:
True if the path exists in current sub-tree, otherwise False.
"""
return object_utils.KeyPath.from_value(path).exists(self)
| (self, path: Union[pyglove.core.object_utils.value_location.KeyPath, str, int]) -> bool | [
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|
40,332 | pyglove.core.symbolic.object | sym_hasattr | Tests if a symbolic attribute exists. | def sym_hasattr(self, key: Union[str, int]) -> bool:
"""Tests if a symbolic attribute exists."""
if key == '_sym_attributes':
raise ValueError(
f'{self.__class__.__name__}.__init__ should call `super().__init__`.')
return (
isinstance(key, str)
and not key.startswith('_')
and key in self._sym_attributes
)
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|
40,333 | pyglove.core.symbolic.object | sym_hash | Symbolically hashing. | def sym_hash(self) -> int:
"""Symbolically hashing."""
return base.sym_hash((self.__class__, base.sym_hash(self._sym_attributes)))
| (self) -> int | [
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|
40,334 | pyglove.core.symbolic.base | sym_inferrable | Returns True if the attribute under key can be inferred. | def sym_inferrable(self, key: Union[str, int], **kwargs) -> bool:
"""Returns True if the attribute under key can be inferred."""
return (
self.sym_inferred(key, pg_typing.MISSING_VALUE, **kwargs)
!= pg_typing.MISSING_VALUE
)
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|
40,335 | pyglove.core.symbolic.base | sym_inferred | Returns the inferred value of the attribute under key. | def sym_inferred(
self,
key: Union[str, int],
default: Any = RAISE_IF_NOT_FOUND,
**kwargs,
) -> Any:
"""Returns the inferred value of the attribute under key."""
if default is RAISE_IF_NOT_FOUND:
return self._sym_inferred(key, **kwargs)
else:
try:
return self._sym_inferred(key, **kwargs)
except Exception: # pylint: disable=broad-exception-caught
return default
| (self, key: Union[str, int], default: Any = (MISSING_VALUE,), **kwargs) -> Any | [
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|
40,336 | pyglove.core.symbolic.object | sym_items | Iterates the (key, value) pairs of symbolic attributes. | def sym_items(self):
"""Iterates the (key, value) pairs of symbolic attributes."""
return self._sym_attributes.sym_items()
| (self) | [
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|
40,337 | pyglove.core.symbolic.object | sym_jsonify | Converts current object to a dict of plain Python objects. | def sym_jsonify(self, **kwargs) -> object_utils.JSONValueType:
"""Converts current object to a dict of plain Python objects."""
return object_utils.merge([
{
object_utils.JSONConvertible.TYPE_NAME_KEY: (
self.__class__.__serialization_key__
)
},
self._sym_attributes.to_json(**kwargs),
])
| (self, **kwargs) -> Union[int, float, bool, str, List[Any], Dict[str, Any]] | [
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|
40,338 | pyglove.core.symbolic.object | sym_keys | Iterates the keys of symbolic attributes. | def sym_keys(self) -> Iterator[str]:
"""Iterates the keys of symbolic attributes."""
return self._sym_attributes.sym_keys()
| (self) -> Iterator[str] | [
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]
|
40,339 | pyglove.core.symbolic.object | sym_lt | Tests symbolic less-than. | def sym_lt(self, other: Any) -> bool:
"""Tests symbolic less-than."""
if type(self) is not type(other):
return base.lt(self, other)
return base.lt(self._sym_attributes, other._sym_attributes) # pylint: disable=protected-access
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|
40,340 | pyglove.core.symbolic.base | sym_missing | Returns missing values. | def sym_missing(self, flatten: bool = True) -> Dict[str, Any]:
"""Returns missing values."""
missing = getattr(self, '_sym_missing_values')
if missing is None:
missing = self._sym_missing()
self._set_raw_attr('_sym_missing_values', missing)
if flatten:
missing = object_utils.flatten(missing)
return missing
| (self, flatten: bool = True) -> Dict[str, Any] | [
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|
40,341 | pyglove.core.symbolic.base | sym_ne | Returns if this object does not equal to another object symbolically. | def sym_ne(self, other: Any) -> bool:
"""Returns if this object does not equal to another object symbolically."""
return ne(self, other)
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|
40,342 | pyglove.core.symbolic.base | sym_nondefault | Returns missing values. | def sym_nondefault(self, flatten: bool = True) -> Dict[Union[int, str], Any]:
"""Returns missing values."""
nondefault = getattr(self, '_sym_nondefault_values')
if nondefault is None:
nondefault = self._sym_nondefault()
self._set_raw_attr('_sym_nondefault_values', nondefault)
if flatten:
nondefault = object_utils.flatten(nondefault)
return nondefault
| (self, flatten: bool = True) -> Dict[Union[int, str], Any] | [
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|
40,343 | pyglove.core.symbolic.base | sym_rebind | Mutates the sub-nodes of current object. Please see `rebind`. | def sym_rebind(
self,
path_value_pairs: Optional[Union[
Dict[
Union[object_utils.KeyPath, str, int],
Any],
Callable]] = None, # pylint: disable=g-bare-generic
*,
raise_on_no_change: bool = True,
notify_parents: bool = True,
skip_notification: Optional[bool] = None,
**kwargs,
) -> 'Symbolic':
"""Mutates the sub-nodes of current object. Please see `rebind`."""
assert Symbolic.DictType is not None
if callable(path_value_pairs):
path_value_pairs = get_rebind_dict(path_value_pairs, self)
elif path_value_pairs is None:
path_value_pairs = {}
elif isinstance(path_value_pairs, Symbolic.DictType):
# Rebind work on symbolic form, thus we get their symbol instead of
# their evaluated value when building the rebind dict.
sd = typing.cast(Symbolic.DictType, path_value_pairs)
path_value_pairs = {k: v for k, v in sd.sym_items()}
if not isinstance(path_value_pairs, dict):
raise ValueError(
self._error_message(
f'Argument \'path_value_pairs\' should be a dict. '
f'Encountered {path_value_pairs}'))
path_value_pairs.update(kwargs)
path_value_pairs = {object_utils.KeyPath.from_value(k): v
for k, v in path_value_pairs.items()}
if not path_value_pairs and raise_on_no_change:
raise ValueError(self._error_message('There are no values to rebind.'))
updates = self._sym_rebind(path_value_pairs)
if skip_notification is None:
skip_notification = not flags.is_change_notification_enabled()
if not skip_notification:
self._notify_field_updates(updates, notify_parents=notify_parents)
return self
| (self, path_value_pairs: Union[Dict[Union[pyglove.core.object_utils.value_location.KeyPath, str, int], Any], Callable, NoneType] = None, *, raise_on_no_change: bool = True, notify_parents: bool = True, skip_notification: Optional[bool] = None, **kwargs) -> pyglove.core.symbolic.base.Symbolic | [
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|
40,344 | pyglove.core.symbolic.base | sym_seal | Seals or unseals current object from further modification. | def sym_seal(self, is_seal: bool = True) -> 'Symbolic':
"""Seals or unseals current object from further modification."""
return self._set_raw_attr('_sealed', is_seal)
| (self, is_seal: bool = True) -> pyglove.core.symbolic.base.Symbolic | [
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|
40,345 | pyglove.core.symbolic.base | sym_setorigin | Sets the symbolic origin of current object.
Args:
source: Source value for current object.
tag: A descriptive tag of the origin. Built-in tags are:
`__init__`, `clone`, `deepclone`, `return`. Users can manually
call `sym_setorigin` with custom tag value.
stacktrace: If True, enable stack trace for the origin. If None, enable
stack trace if `pg.tracek_origin()` is called. Otherwise stack trace is
disabled.
stacklimit: An optional integer to limit the stack depth. If None, it's
determined by the value passed to `pg.set_origin_stacktrace_limit`,
which is 10 by default.
stacktop: A negative or zero-value integer indicating the stack top among
the stack frames that we want to present to user, by default it's
1-level up from the stack within current `sym_setorigin` call.
Example::
def foo():
return bar()
def bar():
s = MyObject()
t = s.build()
t.sym_setorigin(s, 'builder',
stacktrace=True, stacklimit=5, stacktop=-1)
This example sets the origin of `t` using `s` as its source with tag
'builder'. We also record the callstack where the `sym_setorigin` is
called, so users can call `t.sym_origin.stacktrace` to get the call stack
later. The `stacktop` -1 indicates that we do not need the stack frame
within ``sym_setorigin``, so users will see the stack top within the
function `bar`. We also set the max number of stack frames to display to 5,
not including the stack frame inside ``sym_setorigin``.
| def sym_setorigin(
self,
source: Any,
tag: str,
stacktrace: Optional[bool] = None,
stacklimit: Optional[int] = None,
stacktop: int = -1):
"""Sets the symbolic origin of current object.
Args:
source: Source value for current object.
tag: A descriptive tag of the origin. Built-in tags are:
`__init__`, `clone`, `deepclone`, `return`. Users can manually
call `sym_setorigin` with custom tag value.
stacktrace: If True, enable stack trace for the origin. If None, enable
stack trace if `pg.tracek_origin()` is called. Otherwise stack trace is
disabled.
stacklimit: An optional integer to limit the stack depth. If None, it's
determined by the value passed to `pg.set_origin_stacktrace_limit`,
which is 10 by default.
stacktop: A negative or zero-value integer indicating the stack top among
the stack frames that we want to present to user, by default it's
1-level up from the stack within current `sym_setorigin` call.
Example::
def foo():
return bar()
def bar():
s = MyObject()
t = s.build()
t.sym_setorigin(s, 'builder',
stacktrace=True, stacklimit=5, stacktop=-1)
This example sets the origin of `t` using `s` as its source with tag
'builder'. We also record the callstack where the `sym_setorigin` is
called, so users can call `t.sym_origin.stacktrace` to get the call stack
later. The `stacktop` -1 indicates that we do not need the stack frame
within ``sym_setorigin``, so users will see the stack top within the
function `bar`. We also set the max number of stack frames to display to 5,
not including the stack frame inside ``sym_setorigin``.
"""
if self.sym_origin is not None:
current_source = self.sym_origin.source
if current_source is not None and current_source is not source:
raise ValueError(
f'Cannot set the origin with a different source value. '
f'Origin source: {current_source!r}, New source: {source!r}.')
# NOTE(daiyip): We decrement the stacktop by 1 as the physical stack top
# is within Origin.
self._set_raw_attr(
'_sym_origin',
Origin(source, tag, stacktrace, stacklimit, stacktop - 1))
| (self, source: Any, tag: str, stacktrace: Optional[bool] = None, stacklimit: Optional[int] = None, stacktop: int = -1) | [
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|
40,346 | pyglove.core.symbolic.object | sym_setparent | Sets the parent of current node in the symbolic tree. | def sym_setparent(self, parent: base.Symbolic):
"""Sets the parent of current node in the symbolic tree."""
old_parent = self.sym_parent
super().sym_setparent(parent)
if old_parent is not parent:
self._on_parent_change(old_parent, parent)
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|
40,347 | pyglove.core.symbolic.base | sym_setpath | Sets the path of current node in its symbolic tree. | def sym_setpath(
self, path: Optional[Union[str, object_utils.KeyPath]]) -> None:
"""Sets the path of current node in its symbolic tree."""
if self.sym_path != path:
old_path = self.sym_path
self._set_raw_attr('_sym_path', path)
self._update_children_paths(old_path, path)
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|
40,348 | pyglove.core.symbolic.object | sym_values | Iterates the values of symbolic attributes. | def sym_values(self):
"""Iterates the values of symbolic attributes."""
return self._sym_attributes.sym_values()
| (self) | [
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|
40,349 | pyglove.core.symbolic.base | to_json | Alias for `sym_jsonify`. | def to_json(self, **kwargs) -> object_utils.JSONValueType:
"""Alias for `sym_jsonify`."""
return self.sym_jsonify(**kwargs)
| (self, **kwargs) -> Union[int, float, bool, str, List[Any], Dict[str, Any]] | [
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|
40,350 | pyglove.core.symbolic.base | to_json_str | Serializes current object into a JSON string. | def to_json_str(self, json_indent: Optional[int] = None, **kwargs) -> str:
"""Serializes current object into a JSON string."""
return json.dumps(self.sym_jsonify(**kwargs), indent=json_indent)
| (self, json_indent: Optional[int] = None, **kwargs) -> str | [
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|
40,351 | pyglove.core.typing.custom_typing | CustomTyping | Interface of custom value type.
Instances of subclasses of CustomTyping can be assigned to fields of
any ValueSpec, and take over `apply` via `custom_apply` method.
As a result, CustomTyping makes the schema system extensible without modifying
existing value specs. For example, value generators can extend CustomTyping
and be assignable to any fields.
| class CustomTyping(metaclass=abc.ABCMeta):
"""Interface of custom value type.
Instances of subclasses of CustomTyping can be assigned to fields of
any ValueSpec, and take over `apply` via `custom_apply` method.
As a result, CustomTyping makes the schema system extensible without modifying
existing value specs. For example, value generators can extend CustomTyping
and be assignable to any fields.
"""
@abc.abstractmethod
def custom_apply(
self,
path: object_utils.KeyPath,
value_spec: class_schema.ValueSpec,
allow_partial: bool,
child_transform: Optional[Callable[
[object_utils.KeyPath, class_schema.Field, Any], Any]] = None
) -> Tuple[bool, Any]:
"""Custom apply on a value based on its original value spec.
Args:
path: KeyPath of current object under its object tree.
value_spec: Original value spec for this field.
allow_partial: Whether allow partial object to be created.
child_transform: Function to transform child node values into their final
values. Transform function is called on leaf nodes first, then on their
parents, recursively.
Returns:
A tuple (proceed_with_standard_apply, value_to_proceed).
If proceed_with_standard_apply is set to False, value_to_proceed
will be used as final value.
Raises:
Error when the value is not compatible with the value spec.
"""
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|
40,352 | pyglove.core.typing.custom_typing | custom_apply | Custom apply on a value based on its original value spec.
Args:
path: KeyPath of current object under its object tree.
value_spec: Original value spec for this field.
allow_partial: Whether allow partial object to be created.
child_transform: Function to transform child node values into their final
values. Transform function is called on leaf nodes first, then on their
parents, recursively.
Returns:
A tuple (proceed_with_standard_apply, value_to_proceed).
If proceed_with_standard_apply is set to False, value_to_proceed
will be used as final value.
Raises:
Error when the value is not compatible with the value spec.
| @abc.abstractmethod
def custom_apply(
self,
path: object_utils.KeyPath,
value_spec: class_schema.ValueSpec,
allow_partial: bool,
child_transform: Optional[Callable[
[object_utils.KeyPath, class_schema.Field, Any], Any]] = None
) -> Tuple[bool, Any]:
"""Custom apply on a value based on its original value spec.
Args:
path: KeyPath of current object under its object tree.
value_spec: Original value spec for this field.
allow_partial: Whether allow partial object to be created.
child_transform: Function to transform child node values into their final
values. Transform function is called on leaf nodes first, then on their
parents, recursively.
Returns:
A tuple (proceed_with_standard_apply, value_to_proceed).
If proceed_with_standard_apply is set to False, value_to_proceed
will be used as final value.
Raises:
Error when the value is not compatible with the value spec.
"""
| (self, path: pyglove.core.object_utils.value_location.KeyPath, value_spec: pyglove.core.typing.class_schema.ValueSpec, allow_partial: bool, child_transform: Optional[Callable[[pyglove.core.object_utils.value_location.KeyPath, pyglove.core.typing.class_schema.Field, Any], Any]] = None) -> Tuple[bool, Any] | [
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|
40,353 | pyglove.core.geno.base | DNA | The genome of a symbolic object relative to its search space.
DNA is a hierarchical structure - each DNA node has a value, and a list of
child DNA nodes. The root node represents the genome that encodes an entire
object relative to its space. The value of a DNA node could be None, an
integer, a float number or a string, dependening on its specification
(:class:`pg.DNASpec`). A valid DNA has a form of the following.
+--------------------------------------+-----------------+-----------------------------+
| Hyper value type | Possible values | Child nodes |
| (DNASpec type) | | |
+======================================+=================+=============================+
|:class:`pg.hyper.ObjectTemplate` | None |DNA of child decision points |
|(:class:`pg.geno.Space`) |(elements > 1) |(Choices/Float) in the |
| | |template. |
+--------------------------------------+-----------------+-----------------------------+
| |None |Children of elements[0] |
| |(elements == 1 | |
| |and elements[0]. | |
| |num_choices > 1) | |
+--------------------------------------+-----------------+-----------------------------+
| |int |Children of: |
| |(elements == 1 |elements[0][0] |
| |and elements[0]. | |
| |num_choices ==1) | |
+--------------------------------------+-----------------+-----------------------------+
| |float |Empty |
| |(elements == 1 | |
| |and elements[0] | |
| |is geno.Float) | |
+--------------------------------------+-----------------+-----------------------------+
|:func:`pg.oneof` |int |Children of Space |
|(:class:`pg.geno.Choices`) |(candidate index |for the chosen candidate |
| |as choice) | |
+--------------------------------------+-----------------+-----------------------------+
|:func:`pg.manyof` |None |DNA of each chosen candidate |
|(:class:`pg.geno.Choices) |(num_choices > 1 | |
+--------------------------------------+-----------------+-----------------------------+
| |int |Children of chosen candidate |
| |(num_choices==1) | |
+--------------------------------------+-----------------+-----------------------------+
|:func:`pg.floatv` |float |Empty |
|(:class:`pg.geno.Float` ) | | |
+--------------------------------------+-----------------+-----------------------------+
|:class:`pg.hyper.CustomHyper` |string |User defined. |
|(:class:`pg.geno.CustomDecisionPoint`)|(serialized | |
| | object) | |
+--------------------------------------+-----------------+-----------------------------+
DNA can also be represented in a compact form - a tree of numbers/strs,
formally defined as::
<dna> := empty | <decision>
<decision>: = <single-decision>
| <multi-decisions>
| <conditional-choice>
| <custom-decision>
<single-decision> := <categorical-decision>
| <float-decision>
| <custom-decision>
<categorical-decision> := int
<float-decision> := float
<custom-decision> := str
<multiple-decisions> := [<decision>, <decision>, ...]
<conditional-choice> := (<categorical-decision>,
<categorical-decision>,
...
<decision>)
Thus DNA can be constructed by nested structures of list, tuple and numbers.
The numeric value for DNA can be integer (as index of choice) or float (the
value itself will used as decoded value).
Examples::
# Empty DNA. This may be generated by an empty template.
DNA()
# A DNA of a nested choice of depth 3.
DNA(0, 0, 0)
# A DNA of three choices at the same level,
# positioned at 0, 1, 2, each choice has value 0.
DNA([0, 0, 0])
# A DNA of two choices (one two-level conditional,
# one non-conditional), position 0 is with choice path: 0 -> [0, 1],
# while [0, 1] means it's a multi-choice, decodable by Sublist or Subset.
# position 1 is a single non-conditional choice: 0.
DNA([(0, [0, 1]), 0])
# A DNA with custom decision point whose encoding
# is defined by the user.
DNA('abc')
| class DNA(symbolic.Object):
"""The genome of a symbolic object relative to its search space.
DNA is a hierarchical structure - each DNA node has a value, and a list of
child DNA nodes. The root node represents the genome that encodes an entire
object relative to its space. The value of a DNA node could be None, an
integer, a float number or a string, dependening on its specification
(:class:`pg.DNASpec`). A valid DNA has a form of the following.
+--------------------------------------+-----------------+-----------------------------+
| Hyper value type | Possible values | Child nodes |
| (DNASpec type) | | |
+======================================+=================+=============================+
|:class:`pg.hyper.ObjectTemplate` | None |DNA of child decision points |
|(:class:`pg.geno.Space`) |(elements > 1) |(Choices/Float) in the |
| | |template. |
+--------------------------------------+-----------------+-----------------------------+
| |None |Children of elements[0] |
| |(elements == 1 | |
| |and elements[0]. | |
| |num_choices > 1) | |
+--------------------------------------+-----------------+-----------------------------+
| |int |Children of: |
| |(elements == 1 |elements[0][0] |
| |and elements[0]. | |
| |num_choices ==1) | |
+--------------------------------------+-----------------+-----------------------------+
| |float |Empty |
| |(elements == 1 | |
| |and elements[0] | |
| |is geno.Float) | |
+--------------------------------------+-----------------+-----------------------------+
|:func:`pg.oneof` |int |Children of Space |
|(:class:`pg.geno.Choices`) |(candidate index |for the chosen candidate |
| |as choice) | |
+--------------------------------------+-----------------+-----------------------------+
|:func:`pg.manyof` |None |DNA of each chosen candidate |
|(:class:`pg.geno.Choices) |(num_choices > 1 | |
+--------------------------------------+-----------------+-----------------------------+
| |int |Children of chosen candidate |
| |(num_choices==1) | |
+--------------------------------------+-----------------+-----------------------------+
|:func:`pg.floatv` |float |Empty |
|(:class:`pg.geno.Float` ) | | |
+--------------------------------------+-----------------+-----------------------------+
|:class:`pg.hyper.CustomHyper` |string |User defined. |
|(:class:`pg.geno.CustomDecisionPoint`)|(serialized | |
| | object) | |
+--------------------------------------+-----------------+-----------------------------+
DNA can also be represented in a compact form - a tree of numbers/strs,
formally defined as::
<dna> := empty | <decision>
<decision>: = <single-decision>
| <multi-decisions>
| <conditional-choice>
| <custom-decision>
<single-decision> := <categorical-decision>
| <float-decision>
| <custom-decision>
<categorical-decision> := int
<float-decision> := float
<custom-decision> := str
<multiple-decisions> := [<decision>, <decision>, ...]
<conditional-choice> := (<categorical-decision>,
<categorical-decision>,
...
<decision>)
Thus DNA can be constructed by nested structures of list, tuple and numbers.
The numeric value for DNA can be integer (as index of choice) or float (the
value itself will used as decoded value).
Examples::
# Empty DNA. This may be generated by an empty template.
DNA()
# A DNA of a nested choice of depth 3.
DNA(0, 0, 0)
# A DNA of three choices at the same level,
# positioned at 0, 1, 2, each choice has value 0.
DNA([0, 0, 0])
# A DNA of two choices (one two-level conditional,
# one non-conditional), position 0 is with choice path: 0 -> [0, 1],
# while [0, 1] means it's a multi-choice, decodable by Sublist or Subset.
# position 1 is a single non-conditional choice: 0.
DNA([(0, [0, 1]), 0])
# A DNA with custom decision point whose encoding
# is defined by the user.
DNA('abc')
"""
# pylint: enable=line-too-long
# Use compact format for __str__ output.
__str_format_kwargs__ = dict(compact=True)
# Allow assignment on symbolic attributes.
allow_symbolic_assignment = True
@object_utils.explicit_method_override
def __init__(
self,
value: Union[None, int, float, str, List[Any], Tuple[Any]] = None,
# Set MISSING_VALUE to use default from pg_typing.
children: Optional[List['DNA']] = None,
spec: Optional[DNASpec] = None,
metadata: Optional[Dict[str, Any]] = None,
*,
allow_partial: bool = False,
**kwargs):
"""Constructor.
Args:
value: Value for current node.
children: Child DNA(s).
spec: DNA spec that constraint current node.
metadata: Optional dict as controller metadata for the DNA.
allow_partial: If True, allow the object to be partial.
**kwargs: keyword arguments that will be passed through to
symbolic.Object.
"""
value, children, metadata = self._parse_value_and_children(
value, children, metadata, spec)
super().__init__(
value=value,
children=children,
metadata=metadata or symbolic.Dict(),
allow_partial=allow_partial,
**kwargs)
self._decision_by_id_cache = None
self._named_decisions = None
self._userdata = AttributeDict()
self._cloneable_metadata_keys = set()
self._cloneable_userdata_keys = set()
self._spec = None
if spec:
self.use_spec(spec)
def _on_bound(self):
"""Event that is triggered when any symbolic member changes."""
super()._on_bound()
self._decision_by_id_cache = None
self._named_decisions = None
def _parse_value_and_children(
self,
value: Union[
int, # As a single chosen index.
float, # As a single chosen value.
str, # As a custom genome.
List[Any], # As multi-choice. (coexisting)
Tuple[Any], # As a conditional choice.
None],
children: Optional[List['DNA']],
metadata: Optional[Dict[str, Any]],
dna_spec: Optional[DNASpec]
) -> Tuple[Union[int, None, float], Optional[List['DNA']]]:
"""Parse value (leaf) and children from maybe compositional value."""
if isinstance(value, (list, tuple)):
# The value is compositional, therefore we need to parse the decision
# for current node and construct the children.
if children is not None:
| (value: Union[NoneType, int, float, str, List[Any], Tuple[Any]] = None, children: Optional[List[ForwardRef('DNA')]] = None, spec: Optional[pyglove.core.geno.base.DNASpec] = None, metadata: Optional[Dict[str, Any]] = None, *, allow_partial: bool = False, **kwargs) | [
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|
40,355 | pyglove.core.geno.base | __cmp__ | DNA comparison. | def __cmp__(self, other: 'DNA') -> int:
"""DNA comparison."""
if other is None:
return 1
def compare_dna_value(x, y):
if x == y:
return 0
if x is None:
return -1
if y is None:
return 1
if isinstance(x, (int, float)) and isinstance(y, str):
return -1
if isinstance(x, str) and isinstance(y, (int, float)):
return 1
return -1 if x < y else 1
result = compare_dna_value(self.value, other.value)
if result != 0:
return result
if len(self.children) != len(other.children):
raise ValueError(
f'The two input DNA have different number of children. '
f'(Left={self!r}, Right={other!r})')
for i, c in enumerate(self.children):
result = c.__cmp__(other.children[i])
if result != 0:
return result
return 0
| (self, other: pyglove.core.geno.base.DNA) -> int | [
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|
40,356 | pyglove.core.geno.base | __contains__ | Returns whether child DNA(s) contains a value. | def __contains__(self, dna_or_value: Union[int, 'DNA']) -> bool:
"""Returns whether child DNA(s) contains a value."""
for child in self.children:
if isinstance(dna_or_value, (int, float, str)):
if child.value == dna_or_value:
return True
elif isinstance(dna_or_value, DNA):
if child == dna_or_value:
return True
else:
raise ValueError(
f'DNA.__contains__ does not accept '
f'{object_utils.quote_if_str(dna_or_value)!r}.')
return False
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|
40,359 | pyglove.core.geno.base | __eq__ | null | def __eq__(self, other: 'DNA') -> bool:
if not isinstance(other, DNA):
return False
return not self.__cmp__(other)
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|
40,362 | pyglove.core.geno.base | __getitem__ | Get an immediate child DNA or DNA in the sub-tree.
Args:
key: The key for retrieving the sub-DNA or sub-DNA list. The key should
be one of:
1) An integer as the index of an immediate child DNA.
2) A name (string) for named decisions whose DNASpec has a not-None
`name` argument.
3) An ID (string or KeyPath) for the decision point to retrieve.
See `DNASpec.id` for details.
4) A DecisionPoint object whose decision value will be retrived.
Returns:
The return value should be one of the following:
1) A DNA object if the key only maps to a single DNA object.
2) None if the decision point exists but it's inactive.
3) A list of DNA or None if there are multiple decision points associated
with the key.
| def __getitem__(
self, key: Union[int, slice, str, object_utils.KeyPath, 'DecisionPoint']
) -> Union[None, 'DNA', List[Optional['DNA']]]:
"""Get an immediate child DNA or DNA in the sub-tree.
Args:
key: The key for retrieving the sub-DNA or sub-DNA list. The key should
be one of:
1) An integer as the index of an immediate child DNA.
2) A name (string) for named decisions whose DNASpec has a not-None
`name` argument.
3) An ID (string or KeyPath) for the decision point to retrieve.
See `DNASpec.id` for details.
4) A DecisionPoint object whose decision value will be retrived.
Returns:
The return value should be one of the following:
1) A DNA object if the key only maps to a single DNA object.
2) None if the decision point exists but it's inactive.
3) A list of DNA or None if there are multiple decision points associated
with the key.
"""
if isinstance(key, (int, slice)):
return self.children[key]
if isinstance(key, DNASpec):
key = key.id
return self._decision_by_id[key]
else:
v = self.named_decisions.get(key, None)
if v is None:
v = self._decision_by_id[key]
return v
| (self, key: Union[int, slice, str, pyglove.core.object_utils.value_location.KeyPath, pyglove.core.geno.base.DecisionPoint]) -> Union[NoneType, pyglove.core.geno.base.DNA, List[Optional[pyglove.core.geno.base.DNA]]] | [
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|
40,365 | pyglove.core.geno.base | __hash__ | Hash code. | def __hash__(self):
"""Hash code."""
return hash((self.value, tuple(self.children)))
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|
40,366 | pyglove.core.geno.base | __init__ | Constructor.
Args:
value: Value for current node.
children: Child DNA(s).
spec: DNA spec that constraint current node.
metadata: Optional dict as controller metadata for the DNA.
allow_partial: If True, allow the object to be partial.
**kwargs: keyword arguments that will be passed through to
symbolic.Object.
| @object_utils.explicit_method_override
def __init__(
self,
value: Union[None, int, float, str, List[Any], Tuple[Any]] = None,
# Set MISSING_VALUE to use default from pg_typing.
children: Optional[List['DNA']] = None,
spec: Optional[DNASpec] = None,
metadata: Optional[Dict[str, Any]] = None,
*,
allow_partial: bool = False,
**kwargs):
"""Constructor.
Args:
value: Value for current node.
children: Child DNA(s).
spec: DNA spec that constraint current node.
metadata: Optional dict as controller metadata for the DNA.
allow_partial: If True, allow the object to be partial.
**kwargs: keyword arguments that will be passed through to
symbolic.Object.
"""
value, children, metadata = self._parse_value_and_children(
value, children, metadata, spec)
super().__init__(
value=value,
children=children,
metadata=metadata or symbolic.Dict(),
allow_partial=allow_partial,
**kwargs)
self._decision_by_id_cache = None
self._named_decisions = None
self._userdata = AttributeDict()
self._cloneable_metadata_keys = set()
self._cloneable_userdata_keys = set()
self._spec = None
if spec:
self.use_spec(spec)
| (self, value: Union[NoneType, int, float, str, List[Any], Tuple[Any]] = None, children: Optional[List[pyglove.core.geno.base.DNA]] = None, spec: Optional[pyglove.core.geno.base.DNASpec] = None, metadata: Optional[Dict[str, Any]] = None, *, allow_partial: bool = False, **kwargs) | [
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|
40,367 | pyglove.core.geno.base | __iter__ | Iterate child DNA(s). | def __iter__(self):
"""Iterate child DNA(s)."""
return self.children.__iter__()
| (self) | [
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|
40,369 | pyglove.core.geno.base | __lt__ | null | def __lt__(self, other: 'DNA') -> bool:
if not isinstance(other, DNA):
raise TypeError(f'unorderable types: DNA & {type(other)}')
return self.__cmp__(other) == -1
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|
40,370 | pyglove.core.geno.base | __ne__ | null | def __ne__(self, other: 'DNA') -> bool:
return not self == other
| (self, other: pyglove.core.geno.base.DNA) -> bool | [
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|
40,375 | pyglove.core.geno.base | _ensure_dna_spec | Raises error if current DNA is not bound with a DNASpec. | def _ensure_dna_spec(self) -> None:
"""Raises error if current DNA is not bound with a DNASpec."""
if self._spec is None:
raise ValueError(f'{self!r} is not bound with a DNASpec.')
| (self) -> NoneType | [
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|
40,380 | pyglove.core.geno.base | _on_bound | Event that is triggered when any symbolic member changes. | def _on_bound(self):
"""Event that is triggered when any symbolic member changes."""
super()._on_bound()
self._decision_by_id_cache = None
self._named_decisions = None
| (self) | [
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|
40,385 | pyglove.core.geno.base | _parse_value_and_children | Parse value (leaf) and children from maybe compositional value. | def _parse_value_and_children(
self,
value: Union[
int, # As a single chosen index.
float, # As a single chosen value.
str, # As a custom genome.
List[Any], # As multi-choice. (coexisting)
Tuple[Any], # As a conditional choice.
None],
children: Optional[List['DNA']],
metadata: Optional[Dict[str, Any]],
dna_spec: Optional[DNASpec]
) -> Tuple[Union[int, None, float], Optional[List['DNA']]]:
"""Parse value (leaf) and children from maybe compositional value."""
if isinstance(value, (list, tuple)):
# The value is compositional, therefore we need to parse the decision
# for current node and construct the children.
if children is not None:
raise ValueError(
f'\'children\' ({children!r}) must be None when '
f'\'value\' ({value!r}) is compositional.')
new_value = None
children = []
if isinstance(value, list):
# Space or multi-choices
children = [DNA(v) for v in value]
else:
# Conditional choices.
if len(value) < 2:
raise ValueError(
f'Tuple as conditional choices must have at least 2 '
f'items. Encountered {value}.')
if isinstance(value[0], (float, int)):
new_value = value[0]
if len(value) == 2:
if isinstance(value[1], list):
# NOTE(daiyip): Multi-choice is allowed only as a leaf choice.
children = [DNA(v) for v in value[1]]
elif isinstance(value[1], (int, float, str)):
children = [DNA(value[1])]
else:
children.append(DNA(value[1:]))
else:
raise ValueError(
f'Tuple as conditional choices only allow multiple '
f'choices to be used at the last position. '
f'Encountered: {value}')
else:
# Normalize DNA by removing trivial intermediate nodes,
# which is DNA with empty value and only one child.
# NOTE(daiyip): during deserialization (from_json) of nested DNA,
# the elements in children might be dicts that are not yet converted
# to DNA. Therefore, we always call `symbolic.from_json` on children,
# which is a no-op for already initialized child DNA.
new_value = value
children = symbolic.from_json(children) or []
if len(children) == 1 and children[0].value is None:
children = children[0].children
if new_value is None and len(children) == 1:
c = children[0]
new_value, children, metadata = c.value, c.children, c.metadata
return new_value, children, metadata
| (self, value: Union[int, float, str, List[Any], Tuple[Any], NoneType], children: Optional[List[pyglove.core.geno.base.DNA]], metadata: Optional[Dict[str, Any]], dna_spec: Optional[pyglove.core.geno.base.DNASpec]) -> Tuple[Union[int, NoneType, float], Optional[List[pyglove.core.geno.base.DNA]]] | [
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|
40,390 | pyglove.core.geno.base | _sym_clone | Override to copy DNASpec. | def _sym_clone(self, deep: bool, memo: Any = None) -> 'DNA':
"""Override to copy DNASpec."""
other = super()._sym_clone(deep, memo)
other._spec = self._spec # pylint: disable=protected-access
for k, v in self._userdata.items():
if k in self._cloneable_userdata_keys:
other._userdata[k] = v # pylint: disable=protected-access
other._cloneable_userdata_keys = set(self._cloneable_userdata_keys) # pylint: disable=protected-access
# Remove none-clonable meta-data.
metadata = {}
for k, v in self.metadata.items():
if k in self._cloneable_metadata_keys:
metadata[k] = v
other.rebind(metadata=metadata)
other._cloneable_metadata_keys = set(self._cloneable_metadata_keys) # pylint: disable=protected-access
return other
| (self, deep: bool, memo: Optional[Any] = None) -> pyglove.core.geno.base.DNA | [
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|
40,399 | pyglove.core.geno.base | format | Customize format method for DNA for more compact representation. | def format(self,
compact: bool = False,
verbose: bool = True,
root_indent: int = 0,
list_wrap_threshold: int = 80,
as_dict: bool = False,
**kwargs):
"""Customize format method for DNA for more compact representation."""
if as_dict and self.spec:
details = object_utils.format(
self.to_dict(value_type='choice_and_literal'),
False,
verbose,
root_indent,
**kwargs)
return f'DNA({details})'
if 'list_wrap_threshold' not in kwargs:
kwargs['list_wrap_threshold'] = list_wrap_threshold
if not verbose:
return super().format(False, verbose, root_indent, **kwargs)
if self.is_leaf:
return f'DNA({self.value!r})'
rep = object_utils.format(
self.to_json(compact=True, type_info=False),
compact, verbose, root_indent, **kwargs)
if rep and rep[0] == '(':
# NOTE(daiyip): for conditional choice from the root,
# we don't want to keep duplicate round bracket.
return f'DNA{rep}'
return f'DNA({rep})'
| (self, compact: bool = False, verbose: bool = True, root_indent: int = 0, list_wrap_threshold: int = 80, as_dict: bool = False, **kwargs) | [
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|
40,400 | pyglove.core.geno.base | get | Get an immediate child DNA or DNA in the sub-tree. | def get(self,
key: Union[int, slice, str, object_utils.KeyPath, 'DecisionPoint'],
default: Any = None
) -> Union[Any, None, 'DNA', List[Optional['DNA']]]:
"""Get an immediate child DNA or DNA in the sub-tree."""
try:
return self[key]
except KeyError:
return default
| (self, key: Union[int, slice, str, pyglove.core.object_utils.value_location.KeyPath, pyglove.core.geno.base.DecisionPoint], default: Optional[Any] = None) -> Union[Any, NoneType, pyglove.core.geno.base.DNA, List[Optional[pyglove.core.geno.base.DNA]]] | [
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|
40,402 | pyglove.core.geno.base | iter_dna | Iterate DNA of the space starting from self. | def iter_dna(self):
"""Iterate DNA of the space starting from self."""
self._ensure_dna_spec()
return self.spec.iter_dna(self)
| (self) | [
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|
40,404 | pyglove.core.geno.base | next_dna | Get the next DNA in the spec. | def next_dna(self) -> Optional['DNA']:
"""Get the next DNA in the spec."""
self._ensure_dna_spec()
return self.spec.next_dna(self)
| (self) -> Optional[pyglove.core.geno.base.DNA] | [
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|
40,406 | pyglove.core.geno.base | parameters | Returns parameters for this DNA to emit based on its spec.
Deprecated: use `to_dict` instead.
Args:
use_literal_values: If True, use literal values from DNASpec for Choices,
otherwise returns '{choice}/{num_candidates} ({literal})'. Otherwise
returns '{choice}/{num_candidates}'.
Returns:
Dict of parameter names to their values mapped from this DNA.
Raises:
RuntimeError: If DNA is not associated with a DNASpec.
| def parameters(
self, use_literal_values: bool = False) -> Dict[str, str]:
"""Returns parameters for this DNA to emit based on its spec.
Deprecated: use `to_dict` instead.
Args:
use_literal_values: If True, use literal values from DNASpec for Choices,
otherwise returns '{choice}/{num_candidates} ({literal})'. Otherwise
returns '{choice}/{num_candidates}'.
Returns:
Dict of parameter names to their values mapped from this DNA.
Raises:
RuntimeError: If DNA is not associated with a DNASpec.
"""
value_type = 'choice_and_literal' if use_literal_values else 'choice'
return self.to_dict(value_type=value_type)
| (self, use_literal_values: bool = False) -> Dict[str, str] | [
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|
40,411 | pyglove.core.geno.base | set_metadata | Set metadata associated with a key.
Metadata associated with the DNA will be persisted and carried over across
processes, which is different the `userdata`. (See `set_userdata` for more
details.)
Args:
key: Key for the metadata.
value: Value for the metadata.
cloneable: If True, the key/value will be propagated during clone.
Returns:
Self.
| def set_metadata(
self, key: str, value: Any, cloneable: bool = False) -> 'DNA':
"""Set metadata associated with a key.
Metadata associated with the DNA will be persisted and carried over across
processes, which is different the `userdata`. (See `set_userdata` for more
details.)
Args:
key: Key for the metadata.
value: Value for the metadata.
cloneable: If True, the key/value will be propagated during clone.
Returns:
Self.
"""
self.metadata.rebind(
{key: value}, raise_on_no_change=False, skip_notification=True)
if cloneable:
self._cloneable_metadata_keys.add(key)
return self
| (self, key: str, value: Any, cloneable: bool = False) -> pyglove.core.geno.base.DNA | [
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|
40,412 | pyglove.core.geno.base | set_userdata | Sets user data associated with a key.
User data associated with the DNA will live only within current process,
and is not carried over during serialization/deserialization, which is
different from DNA metadata. (See `set_metadata` for more details.)
Args:
key: Key of the user data.
value: Value of the user data.
cloneable: If True, the key/value will be carry over to the cloned DNA.
Returns:
Self.
| def set_userdata(
self, key: str, value: Any, cloneable: bool = False) -> 'DNA':
"""Sets user data associated with a key.
User data associated with the DNA will live only within current process,
and is not carried over during serialization/deserialization, which is
different from DNA metadata. (See `set_metadata` for more details.)
Args:
key: Key of the user data.
value: Value of the user data.
cloneable: If True, the key/value will be carry over to the cloned DNA.
Returns:
Self.
"""
self._userdata[key] = value
if cloneable:
self._cloneable_userdata_keys.add(key)
return self
| (self, key: str, value: Any, cloneable: bool = False) -> pyglove.core.geno.base.DNA | [
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|
40,428 | pyglove.core.geno.base | sym_jsonify | Convert DNA to JSON object.
Args:
compact: Whether use compact form. If compact, the nested number structure
in DNA.parse will be used, otherwise members will be rendered out as
regular symbolic Object.
type_info: If True, type information will be included in output, otherwise
type information will not be included. Applicable when compact is set
to True.
**kwargs: Keyword arguments that will be passed to symbolic.Object if
compact is False.
Returns:
JSON representation of DNA.
| def sym_jsonify(
self,
compact: bool = True,
type_info: bool = True,
**kwargs) -> Any:
"""Convert DNA to JSON object.
Args:
compact: Whether use compact form. If compact, the nested number structure
in DNA.parse will be used, otherwise members will be rendered out as
regular symbolic Object.
type_info: If True, type information will be included in output, otherwise
type information will not be included. Applicable when compact is set
to True.
**kwargs: Keyword arguments that will be passed to symbolic.Object if
compact is False.
Returns:
JSON representation of DNA.
"""
if not compact:
json_value = super().sym_jsonify(**kwargs)
assert isinstance(json_value, dict), json_value
if self._cloneable_metadata_keys:
json_value['_cloneable_metadata_keys'] = list(
self._cloneable_metadata_keys)
return json_value
if self.children:
child_nodes = [c.sym_jsonify(compact, type_info=False, **kwargs)
for c in self.children]
if self.value is not None:
if len(child_nodes) == 1:
# Chain single choices into one tuple.
single_choice = child_nodes[0]
if isinstance(single_choice, tuple):
value = (self.value,) + single_choice
else:
value = (self.value, single_choice)
else:
# Put multiple choice as sub-nodes.
value = (self.value, child_nodes)
else:
value = child_nodes
else:
value = self.value
if type_info:
json_value = {
object_utils.JSONConvertible.TYPE_NAME_KEY: (
self.__class__.__serialization_key__
),
'format': 'compact',
'value': symbolic.to_json(value),
}
# NOTE(daiyip): For now, we only attach metadata from the root node for
# faster serialization/deserialization speed. This should be revised if
# metadata for child DNA is used.
if self.metadata:
json_value['metadata'] = symbolic.to_json(self.metadata)
if self._cloneable_metadata_keys:
json_value['_cloneable_metadata_keys'] = list(
self._cloneable_metadata_keys)
return json_value
else:
return value
| (self, compact: bool = True, type_info: bool = True, **kwargs) -> Any | [
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|
40,440 | pyglove.core.geno.base | to_dict | Returns the dict representation of current DNA.
Args:
key_type: Key type in returned dictionary. Acceptable values are:
* 'id': Use the ID (canonical location) of each decision point as key.
This is the default behavior.
* 'name_or_id': Use the name of each decision point as key if it's
present, otherwise use ID as key. When the name of a decision
point is presented, it is guaranteed not to clash with other
decision points' names or IDs.
* 'dna_spec': Use the DNASpec object of each decision point as key.
value_type: Value type for choices in returned dictionary.
Acceptable values are:
* 'value': Use the index of the chosen candidate for `Choices`, and
use the float number for `Float`. This is the default behavior.
* 'dna': Use `DNA` for all decision points.
* 'choice': Use '{index}/{num_candidates}' for the chosen candidate
for `Choices`, and the chosen float number for `Float`.
* 'literal': Use the literal value for the chosen candidate for
`Choices`, and the chosen float number for `Float`. If the literal
value for the `Choices` decision point is not present, fall back
to the '{index}/{num_candidates}' format.
* 'choice_and_literal': Use '{index}/{num_candidates} ({literal})'
for the chosen candidate for `Choices` and then chosen float number
for `Float`. If the literal value for the `Choices` decision point
is not present, fall back to the '{index}/{num_candidates}' format.
multi_choice_key: 'subchoice', 'parent', or 'both'. If 'subchoice', each
subchoice will insert a key into the dict. If 'parent', subchoices of
a multi-choice will share the parent spec as key, its value will be
a list of decisions from the subchoices. If 'both', the dict will
contain both the keys for subchoices and the key for the parent
multi-choice.
include_inactive_decisions: If True, inactive decisions from the search
space will be added to the dict with value None. Otherwise they will
be absent in the dict.
filter_fn: Decision point filter. If None, all the decision points will be
included in the dict. Otherwise only the decision points that pass
the filter (returns True) will be included.
Returns:
A dictionary of requested key type to value type mapped from the DNA.
Raises:
ValueError: argument `key_type` or `value_type` is not valid.
RuntimeError: If DNA is not associated with a DNASpec.
| def to_dict(
self,
key_type='id',
value_type='value',
multi_choice_key='subchoice',
include_inactive_decisions=False,
filter_fn: Optional[Callable[[DecisionPoint], bool]] = None
) -> Dict[Union[DecisionPoint, str],
Union[None, 'DNA', float, int, str,
List['DNA'], List[int], List[str]]]:
"""Returns the dict representation of current DNA.
Args:
key_type: Key type in returned dictionary. Acceptable values are:
* 'id': Use the ID (canonical location) of each decision point as key.
This is the default behavior.
* 'name_or_id': Use the name of each decision point as key if it's
present, otherwise use ID as key. When the name of a decision
point is presented, it is guaranteed not to clash with other
decision points' names or IDs.
* 'dna_spec': Use the DNASpec object of each decision point as key.
value_type: Value type for choices in returned dictionary.
Acceptable values are:
* 'value': Use the index of the chosen candidate for `Choices`, and
use the float number for `Float`. This is the default behavior.
* 'dna': Use `DNA` for all decision points.
* 'choice': Use '{index}/{num_candidates}' for the chosen candidate
for `Choices`, and the chosen float number for `Float`.
* 'literal': Use the literal value for the chosen candidate for
`Choices`, and the chosen float number for `Float`. If the literal
value for the `Choices` decision point is not present, fall back
to the '{index}/{num_candidates}' format.
* 'choice_and_literal': Use '{index}/{num_candidates} ({literal})'
for the chosen candidate for `Choices` and then chosen float number
for `Float`. If the literal value for the `Choices` decision point
is not present, fall back to the '{index}/{num_candidates}' format.
multi_choice_key: 'subchoice', 'parent', or 'both'. If 'subchoice', each
subchoice will insert a key into the dict. If 'parent', subchoices of
a multi-choice will share the parent spec as key, its value will be
a list of decisions from the subchoices. If 'both', the dict will
contain both the keys for subchoices and the key for the parent
multi-choice.
include_inactive_decisions: If True, inactive decisions from the search
space will be added to the dict with value None. Otherwise they will
be absent in the dict.
filter_fn: Decision point filter. If None, all the decision points will be
included in the dict. Otherwise only the decision points that pass
the filter (returns True) will be included.
Returns:
A dictionary of requested key type to value type mapped from the DNA.
Raises:
ValueError: argument `key_type` or `value_type` is not valid.
RuntimeError: If DNA is not associated with a DNASpec.
"""
if key_type not in ['id', 'name_or_id', 'dna_spec']:
raise ValueError(
f'\'key_type\' must be either \'id\', \'name_or_id\' '
f'or \'dna_spec\'. Encountered: {key_type!r}.')
if value_type not in ['dna', 'value', 'choice',
'literal', 'choice_and_literal']:
raise ValueError(
f'\'value_type\' must be either \'dna\', \'value\', \'choice\' '
f'\'literal\' or \'choice_and_literal\'. '
f'Encountered: {value_type!r}.')
if multi_choice_key not in ['subchoice', 'parent', 'both']:
raise ValueError(
f'\'multi_choice_key\' must be either \'subchoice\', \'parent\', or '
f'\'both\'. Encountered: {multi_choice_key!r}.')
multi_choice_use_parent_as_key = multi_choice_key != 'subchoice'
multi_choice_use_subchoice_as_key = multi_choice_key != 'parent'
filter_fn = filter_fn or (lambda x: True)
self._ensure_dna_spec()
dict_repr = dict()
def _needs_subchoice_key(subchoice):
return (multi_choice_use_subchoice_as_key
and (not multi_choice_use_parent_as_key
or (key_type != 'name_or_id' or subchoice.name is None)))
def _key(spec: 'DecisionPoint'):
if key_type == 'id':
return spec.id.path
elif key_type == 'name_or_id':
return spec.name if spec.name else spec.id.path
else:
return spec
def _put(key, value):
if key in dict_repr:
accumulated = dict_repr[key]
if not isinstance(accumulated, list):
accumulated = [accumulated]
accumulated.append(value)
value = accumulated
dict_repr[key] = value
return value
def _dump_node(dna: DNA):
"""Dump node value to dict representation."""
if isinstance(dna.spec, DecisionPoint) and filter_fn(dna.spec):
key = _key(dna.spec)
value = None
if dna.spec.is_categorical and dna.value is not None:
if value_type == 'dna':
value = dna
elif value_type == 'value':
value = dna.value
else:
value = dna.spec.format_candidate(
dna.value, display_format=value_type)
if dna.spec.is_subchoice:
# Append multi-choice values into parent's key.
if multi_choice_use_parent_as_key:
_put(_key(dna.spec.parent_spec), value)
# Insert subchoice in its own key.
if _needs_subchoice_key(dna.spec):
_put(key, value)
else:
_put(key, value)
elif dna.spec.is_numerical or dna.spec.is_custom_decision_point:
if value_type == 'dna':
value = dna
else:
value = dna.value
_put(key, value)
for child_dna in dna.children:
_dump_node(child_dna)
_dump_node(self)
if not include_inactive_decisions:
return dict_repr
result = dict()
for dp in self.spec.decision_points:
if not filter_fn(dp):
continue
if dp.is_categorical and dp.is_subchoice:
if multi_choice_use_parent_as_key:
if dp.subchoice_index == 0:
k = _key(dp.parent_spec)
result[k] = dict_repr.get(k, None)
if _needs_subchoice_key(dp):
k = _key(dp)
result[k] = dict_repr.get(k, None)
else:
k = _key(dp)
result[k] = dict_repr.get(k, None)
return result
| (self, key_type='id', value_type='value', multi_choice_key='subchoice', include_inactive_decisions=False, filter_fn: Optional[Callable[[pyglove.core.geno.base.DecisionPoint], bool]] = None) -> Dict[Union[pyglove.core.geno.base.DecisionPoint, str], Union[NoneType, pyglove.core.geno.base.DNA, float, int, str, List[pyglove.core.geno.base.DNA], List[int], List[str]]] | [
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|
40,443 | pyglove.core.geno.base | to_numbers | Returns a (maybe) nested structure of numbers as decisions.
Args:
flatten: If True, the hierarchy of the numbers will not be preserved.
Decisions will be returned as a flat list in DFS order. Otherwise, a
nestable structure of numbers will be returned.
Returns:
A flat list or a hierarchical structure of numbers as the decisions made
for each decision point.
| def to_numbers(
self, flatten: bool = True,
) -> Union[List[Union[int, float, str]],
object_utils.Nestable[Union[int, float, str]]]:
"""Returns a (maybe) nested structure of numbers as decisions.
Args:
flatten: If True, the hierarchy of the numbers will not be preserved.
Decisions will be returned as a flat list in DFS order. Otherwise, a
nestable structure of numbers will be returned.
Returns:
A flat list or a hierarchical structure of numbers as the decisions made
for each decision point.
"""
if flatten:
decisions = [self.value] if self.value is not None else []
for c in self.children:
decisions.extend(c.to_numbers(flatten))
return decisions
else:
if self.value is None:
return [c.to_numbers(flatten) for c in self.children]
elif not self.children:
return self.value
elif len(self.children) == 1:
child = self.children[0].to_numbers(flatten)
if isinstance(child, tuple):
return tuple([self.value, list(child)])
else:
return (self.value, child)
else:
assert len(self.children) > 1
return (self.value, [c.to_numbers(flatten) for c in self.children])
| (self, flatten: bool = True) -> Union[List[Union[str, int, float]], Any, int, float, str] | [
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|
40,444 | pyglove.core.geno.base | use_spec | Use a DNA spec for this node and children recursively.
Args:
spec: DNA spec.
Returns:
Self.
Raises:
ValueError: current DNA tree does not conform to the DNA spec.
| def use_spec(self, spec: DNASpec) -> 'DNA':
"""Use a DNA spec for this node and children recursively.
Args:
spec: DNA spec.
Returns:
Self.
Raises:
ValueError: current DNA tree does not conform to the DNA spec.
"""
if not isinstance(spec, DNASpec):
raise ValueError(
f'Argument \'spec\' must be a `pg.DNASpec` object. '
f'Encountered: {spec!r}.')
if self._spec is spec:
return self
def _use_spec_for_child_choices(spec: DNASpec, children: List[DNA]):
"""Use spec for child choices."""
assert spec.is_categorical, spec
if spec.num_choices != len(children):
raise ValueError(
f'Number of choices ({spec.num_choices}) does not match with '
f'the number of child values (len(children)). '
f'Spec: {spec!r}, Children: {children!r}.')
for i, child in enumerate(children):
subchoice = spec.subchoice(i)
child.use_spec(subchoice)
child_values = [c.value for c in children]
if spec.sorted and sorted(child_values) != child_values:
raise ValueError(
f'Child values {child_values!r} are not sorted. Spec: {spec!r}.')
if spec.distinct and len(set(child_values)) != len(child_values):
raise ValueError(
f'Child values {child_values!r} are not distinct. Spec: {spec!r}.')
# Skip dummy DNA specs.
while spec.is_space and len(spec.elements) == 1:
spec = spec.elements[0]
if spec.is_space:
# Multiple value composition.
if self.value is not None:
raise ValueError(
f'DNA value type mismatch. Value: {self.value}, Spec: {spec!r}.')
if len(spec.elements) != len(self.children):
raise ValueError(
f'Length of DNA child values ({len(self.children)}) is different '
f'from the number of elements ({len(spec.elements)}) '
f'in Spec: {spec!r}.')
for i, elem_spec in enumerate(spec.elements):
self.children[i].use_spec(elem_spec)
elif spec.is_categorical:
if spec.num_choices == 1:
# Single choice.
if not isinstance(self.value, int):
raise ValueError(
f'DNA value type mismatch. Value: {self.value}, Spec: {spec!r}.')
if self.value >= len(spec.candidates):
raise ValueError(
f'Value of DNA is out of range according to the DNA spec. '
f'Value: {self.value}, Spec: {spec!r}.')
chosen_candidate = spec.candidates[self.value]
assert chosen_candidate.is_space, chosen_candidate
# Empty template in chosen candidate.
if not chosen_candidate.elements and self.children:
raise ValueError(
f'There is no DNA spec for child DNA values. '
f'Child values: {self.children}.')
# None-empty template in chosen candidate.
if len(chosen_candidate.elements) > 1:
# Children are DNA of multiple encoders in chosen composition.
if len(chosen_candidate.elements) != len(self.children):
raise ValueError(
f'Number of elements in child templates '
f'({len(chosen_candidate.elements)}) does not match with '
f'the length of children ({len(self.children)}) from DNA: '
f'{self!r}, Spec: {chosen_candidate}.')
for i, elem_spec in enumerate(chosen_candidate.elements):
self.children[i].use_spec(elem_spec)
elif len(chosen_candidate.elements) == 1:
# Children are multiple choices of the only encoder
# in chosen composition.
sub_spec = chosen_candidate
while sub_spec.is_space and len(sub_spec.elements) == 1:
sub_spec = sub_spec.elements[0]
if sub_spec.is_numerical or sub_spec.is_custom_decision_point:
if len(self.children) != 1:
raise ValueError(
f'Encountered more than 1 value.'
f'Child value: {self.children}, Spec: {sub_spec}.')
self.children[0].use_spec(sub_spec)
else:
assert sub_spec.is_categorical, sub_spec
_use_spec_for_child_choices(sub_spec, self.children)
else:
# Multiple choices.
if self.value is not None:
raise ValueError(
f'Cannot apply multi-choice DNA spec on '
f'value {self.value}: {spec!r}.')
_use_spec_for_child_choices(spec, self.children)
elif spec.is_numerical:
if not isinstance(self.value, float):
raise ValueError(
f'DNA value type mismatch. Value: {self.value}, '
f'Spec: {spec!r}.')
if self.value < spec.min_value:
raise ValueError(
f'DNA value should be no less than {spec.min_value}. '
f'Encountered {self.value}, Spec: {spec!r}.')
if self.value > spec.max_value:
raise ValueError(
f'DNA value should be no greater than {spec.max_value}. '
f'Encountered {self.value}, Spec: {spec!r}.')
else:
assert spec.is_custom_decision_point, spec
if not isinstance(self.value, str):
raise ValueError(
f'DNA value type mismatch, Value: {self.value!r}, Spec: {spec!r}.')
self._spec = spec
return self
| (self, spec: pyglove.core.geno.base.DNASpec) -> pyglove.core.geno.base.DNA | [
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|
40,445 | pyglove.core.geno.dna_generator | DNAGenerator | Base class for DNA generator.
A DNA generator is an object that produces :class:`pyglove.DNA`, and
optionally takes feedback from the caller to improve its future proposals.
To implement a DNA generator, the user must implement the `_propose` method,
and can optionally override the `_setup`, `_feedback` and `_replay` methods.
* Making proposals (Required): This method defines what to return as the
next DNA from the generator, users MUST override the `_propose` method to
implement this logic. `_propose` can raise `StopIteration` when no more
DNA can be produced.
* Custom setup (Optional): Usually a DNAGenerator subclass has its internal
state, which can be initialized when the search space definition is
attached to the DNAGenerator. To do so, the user can override the `_setup`
method, in which we can access the search space definition (DNASpec object)
via `self.dna_spec`.
* Taking feedback (Optional): A DNAGenerator may take feedbacks from the
caller on the fitness of proposed DNA to improve future proposals. The
fitness is measured by a reward (a float number as the measure of a single
objective, or a tuple of float numbers as the measure for multiple
objectives). The user should override the `_feedback` method to implement
such logics. If the reward is for multiple objectives. The user should
override the `multi_objective` property to return True.
* State recovery (Optional): DNAGenerator was designed with distributed
computing in mind, in which a process can be preempted or killed
unexpectedly. Therefore, a DNAGenerator should be able to recover its
state from historical proposals and rewards. The `recover` method was
introduced for such purpose, whose default implementation is to replay the
history through the `_feedback` method. If the user has a custom replay
logic other than `_feedback`, they should override the `_replay` method.
In some use cases, the user may want to implement their own checkpointing
logic. In such cases, the user can override the `recover` method as a
no-op. As aside note, the `recover` method will be called by the tuning
backend (see `tuning.py`) after `setup` but before `propose`.
See also:
* :class:`pyglove.geno.Sweeping`
* :class:`pyglove.geno.Random`
* :func:`pyglove.geno.dna_generator`
| class DNAGenerator(symbolic.Object):
"""Base class for DNA generator.
A DNA generator is an object that produces :class:`pyglove.DNA`, and
optionally takes feedback from the caller to improve its future proposals.
To implement a DNA generator, the user must implement the `_propose` method,
and can optionally override the `_setup`, `_feedback` and `_replay` methods.
* Making proposals (Required): This method defines what to return as the
next DNA from the generator, users MUST override the `_propose` method to
implement this logic. `_propose` can raise `StopIteration` when no more
DNA can be produced.
* Custom setup (Optional): Usually a DNAGenerator subclass has its internal
state, which can be initialized when the search space definition is
attached to the DNAGenerator. To do so, the user can override the `_setup`
method, in which we can access the search space definition (DNASpec object)
via `self.dna_spec`.
* Taking feedback (Optional): A DNAGenerator may take feedbacks from the
caller on the fitness of proposed DNA to improve future proposals. The
fitness is measured by a reward (a float number as the measure of a single
objective, or a tuple of float numbers as the measure for multiple
objectives). The user should override the `_feedback` method to implement
such logics. If the reward is for multiple objectives. The user should
override the `multi_objective` property to return True.
* State recovery (Optional): DNAGenerator was designed with distributed
computing in mind, in which a process can be preempted or killed
unexpectedly. Therefore, a DNAGenerator should be able to recover its
state from historical proposals and rewards. The `recover` method was
introduced for such purpose, whose default implementation is to replay the
history through the `_feedback` method. If the user has a custom replay
logic other than `_feedback`, they should override the `_replay` method.
In some use cases, the user may want to implement their own checkpointing
logic. In such cases, the user can override the `recover` method as a
no-op. As aside note, the `recover` method will be called by the tuning
backend (see `tuning.py`) after `setup` but before `propose`.
See also:
* :class:`pyglove.geno.Sweeping`
* :class:`pyglove.geno.Random`
* :func:`pyglove.geno.dna_generator`
"""
def setup(self, dna_spec: DNASpec) -> None:
"""Setup DNA spec."""
self._dna_spec = dna_spec
self._num_proposals = 0
self._num_feedbacks = 0
self._setup()
def _setup(self) -> None:
"""Subclass should override this for adding additional setup logics."""
@property
def multi_objective(self) -> bool:
"""If True, current DNA generator supports multi-objective optimization."""
return False
@property
def needs_feedback(self) -> bool:
"""Returns True if the DNAGenerator needs feedback."""
return self._feedback.__code__ is not DNAGenerator._feedback.__code__ # pytype: disable=attribute-error
@property
def dna_spec(self) -> Optional[DNASpec]:
return getattr(self, '_dna_spec', None)
@property
def num_proposals(self):
"""Get number of proposals that are already produced."""
return self._num_proposals
@property
def num_feedbacks(self):
"""Get number of proposals whose feedback are provided."""
return self._num_feedbacks
def propose(self) -> DNA:
"""Propose a DNA to evaluate."""
dna = self._propose()
self._num_proposals += 1
return dna
def _propose(self) -> DNA:
"""Actual propose method which should be implemented by the child class."""
raise NotImplementedError()
def feedback(self, dna: DNA, reward: Union[float, Tuple[float]]) -> None:
"""Feedback a completed trial to the algorithm.
Args:
dna: a DNA object.
reward: reward for the DNA. It is a float if `self.multi_objective`
returns False, otherwise it's a tuple of floats.
"""
if self.needs_feedback:
if self.multi_objective and isinstance(reward, float):
reward = (reward,)
elif not self.multi_objective and isinstance(reward, tuple):
if len(reward) != 1:
raise ValueError(
f'{self!r} is single objective, but the reward {reward!r} '
f'contains multiple objectives.')
reward = reward[0]
self._feedback(dna, reward)
self._num_feedbacks += 1
def _feedback(self, dna: DNA, reward: Union[float, Tuple[float]]) -> None:
"""Actual feedback method which should be implemented by the child class.
The default implementation is no-op.
Args:
dna: a DNA object.
reward: reward for the DNA. It is a float if `self.multi_objective`
returns False, otherwise it's a tuple of floats.
"""
def recover(
self,
history: Iterable[Tuple[DNA, Union[None, float, Tuple[float]]]]
) -> None:
"""Recover states by replaying the proposal history.
NOTE: `recover` will always be called before first `propose` and could be
called multiple times if there are multiple source of history, e.g: trials
from a previous study and existing trials from current study.
Args:
history: An iterable object that consists of historically proposed DNA
with its reward. The reward will be None if it is not yet provided
(via feedback).
"""
for i, (dna, reward) in enumerate(history):
self._replay(i, dna, reward)
self._num_proposals += 1
if reward is not None:
self._num_feedbacks += 1
def _replay(
self,
trial_id: int,
dna: DNA,
reward: Union[None, float, Tuple[float]]):
"""Replay a single DNA from the history for state recovery.
The default implementation to call `DNAGenerator._feedback`. Subclasses that
have states and can be recovered from replaying the history should override
this method. See class `Sweeping` as an example.
Args:
trial_id: A zero-based integer as the trial ID for the DNA.
dna: A historically proposed DNA.
reward: The reward for the DNA. If None, the reward is not yet fed back
to the optimizer.
"""
del trial_id
if reward is not None:
self._feedback(dna, reward)
def __iter__(self) -> Iterator[
Union[DNA,
Tuple[DNA, Callable[[Union[float, Tuple[float]]], None]]]]:
"""Iterates DNA generated from current DNAGenerator.
NOTE(daiyip): `setup` needs to be called first before a DNAGenerator can
be iterated.
Yields:
A tuple of (DNA, feedback) if current DNAGenerator requires feedback,
otherwise DNA.
"""
while True:
try:
dna = self.propose()
if self.needs_feedback:
feedback = lambda r: self.feedback(dna, r)
yield (dna, feedback)
else:
yield dna
except StopIteration:
break
| () | [
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|
40,454 | pyglove.core.geno.dna_generator | __iter__ | Iterates DNA generated from current DNAGenerator.
NOTE(daiyip): `setup` needs to be called first before a DNAGenerator can
be iterated.
Yields:
A tuple of (DNA, feedback) if current DNAGenerator requires feedback,
otherwise DNA.
| def __iter__(self) -> Iterator[
Union[DNA,
Tuple[DNA, Callable[[Union[float, Tuple[float]]], None]]]]:
"""Iterates DNA generated from current DNAGenerator.
NOTE(daiyip): `setup` needs to be called first before a DNAGenerator can
be iterated.
Yields:
A tuple of (DNA, feedback) if current DNAGenerator requires feedback,
otherwise DNA.
"""
while True:
try:
dna = self.propose()
if self.needs_feedback:
feedback = lambda r: self.feedback(dna, r)
yield (dna, feedback)
else:
yield dna
except StopIteration:
break
| (self) -> Iterator[Union[pyglove.core.geno.base.DNA, Tuple[pyglove.core.geno.base.DNA, Callable[[Union[float, Tuple[float]]], NoneType]]]] | [
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|
40,461 | pyglove.core.geno.dna_generator | _feedback | Actual feedback method which should be implemented by the child class.
The default implementation is no-op.
Args:
dna: a DNA object.
reward: reward for the DNA. It is a float if `self.multi_objective`
returns False, otherwise it's a tuple of floats.
| def _feedback(self, dna: DNA, reward: Union[float, Tuple[float]]) -> None:
"""Actual feedback method which should be implemented by the child class.
The default implementation is no-op.
Args:
dna: a DNA object.
reward: reward for the DNA. It is a float if `self.multi_objective`
returns False, otherwise it's a tuple of floats.
"""
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|
40,470 | pyglove.core.geno.dna_generator | _propose | Actual propose method which should be implemented by the child class. | def _propose(self) -> DNA:
"""Actual propose method which should be implemented by the child class."""
raise NotImplementedError()
| (self) -> pyglove.core.geno.base.DNA | [
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|
40,472 | pyglove.core.geno.dna_generator | _replay | Replay a single DNA from the history for state recovery.
The default implementation to call `DNAGenerator._feedback`. Subclasses that
have states and can be recovered from replaying the history should override
this method. See class `Sweeping` as an example.
Args:
trial_id: A zero-based integer as the trial ID for the DNA.
dna: A historically proposed DNA.
reward: The reward for the DNA. If None, the reward is not yet fed back
to the optimizer.
| def _replay(
self,
trial_id: int,
dna: DNA,
reward: Union[None, float, Tuple[float]]):
"""Replay a single DNA from the history for state recovery.
The default implementation to call `DNAGenerator._feedback`. Subclasses that
have states and can be recovered from replaying the history should override
this method. See class `Sweeping` as an example.
Args:
trial_id: A zero-based integer as the trial ID for the DNA.
dna: A historically proposed DNA.
reward: The reward for the DNA. If None, the reward is not yet fed back
to the optimizer.
"""
del trial_id
if reward is not None:
self._feedback(dna, reward)
| (self, trial_id: int, dna: pyglove.core.geno.base.DNA, reward: Union[NoneType, float, Tuple[float]]) | [
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|
40,476 | pyglove.core.geno.dna_generator | _setup | Subclass should override this for adding additional setup logics. | def _setup(self) -> None:
"""Subclass should override this for adding additional setup logics."""
| (self) -> NoneType | [
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|
40,486 | pyglove.core.geno.dna_generator | feedback | Feedback a completed trial to the algorithm.
Args:
dna: a DNA object.
reward: reward for the DNA. It is a float if `self.multi_objective`
returns False, otherwise it's a tuple of floats.
| def feedback(self, dna: DNA, reward: Union[float, Tuple[float]]) -> None:
"""Feedback a completed trial to the algorithm.
Args:
dna: a DNA object.
reward: reward for the DNA. It is a float if `self.multi_objective`
returns False, otherwise it's a tuple of floats.
"""
if self.needs_feedback:
if self.multi_objective and isinstance(reward, float):
reward = (reward,)
elif not self.multi_objective and isinstance(reward, tuple):
if len(reward) != 1:
raise ValueError(
f'{self!r} is single objective, but the reward {reward!r} '
f'contains multiple objectives.')
reward = reward[0]
self._feedback(dna, reward)
self._num_feedbacks += 1
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|
40,491 | pyglove.core.geno.dna_generator | propose | Propose a DNA to evaluate. | def propose(self) -> DNA:
"""Propose a DNA to evaluate."""
dna = self._propose()
self._num_proposals += 1
return dna
| (self) -> pyglove.core.geno.base.DNA | [
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|
40,493 | pyglove.core.geno.dna_generator | recover | Recover states by replaying the proposal history.
NOTE: `recover` will always be called before first `propose` and could be
called multiple times if there are multiple source of history, e.g: trials
from a previous study and existing trials from current study.
Args:
history: An iterable object that consists of historically proposed DNA
with its reward. The reward will be None if it is not yet provided
(via feedback).
| def recover(
self,
history: Iterable[Tuple[DNA, Union[None, float, Tuple[float]]]]
) -> None:
"""Recover states by replaying the proposal history.
NOTE: `recover` will always be called before first `propose` and could be
called multiple times if there are multiple source of history, e.g: trials
from a previous study and existing trials from current study.
Args:
history: An iterable object that consists of historically proposed DNA
with its reward. The reward will be None if it is not yet provided
(via feedback).
"""
for i, (dna, reward) in enumerate(history):
self._replay(i, dna, reward)
self._num_proposals += 1
if reward is not None:
self._num_feedbacks += 1
| (self, history: Iterable[Tuple[pyglove.core.geno.base.DNA, Union[NoneType, float, Tuple[float]]]]) -> NoneType | [
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|
40,497 | pyglove.core.geno.dna_generator | setup | Setup DNA spec. | def setup(self, dna_spec: DNASpec) -> None:
"""Setup DNA spec."""
self._dna_spec = dna_spec
self._num_proposals = 0
self._num_feedbacks = 0
self._setup()
| (self, dna_spec: pyglove.core.geno.base.DNASpec) -> NoneType | [
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|
40,527 | pyglove.core.geno.base | DNASpec | Base class for DNA specifications (genotypes).
A DNASpec object describes the rules and tips for generating a DNA.
* :class:`pyglove.geno.Space`: Represents a space or sub-space, which
contains a list of decision points.
* :class:`pyglove.geno.DecisionPoint`: Represents a concrete decision
point.
Concrete decision points are the following:
* :class:`pyglove.geno.Choices`: Represents a single categorical
choice or multiple related categorical choices. Each candidate
is a sub-space.
* :class:`pyglove.geno.Float`: Represents a continuous float value
within a range.
* :class:`pyglove.geno.CustomDecisionPoint`: Represents the
genotype for a :class:`pyglove.hyper.CustomHyper`.
All `DecisionPoints` provide `hints` for DNA generator as a reference
when generating values, it needs to be serializable to pass between client
and servers.
All DNASpec types allow user to attach their data via `set_userdata`
method, it's aimed to be used within the same process, thus not required to
be serializable.
| class DNASpec(symbolic.Object):
"""Base class for DNA specifications (genotypes).
A DNASpec object describes the rules and tips for generating a DNA.
* :class:`pyglove.geno.Space`: Represents a space or sub-space, which
contains a list of decision points.
* :class:`pyglove.geno.DecisionPoint`: Represents a concrete decision
point.
Concrete decision points are the following:
* :class:`pyglove.geno.Choices`: Represents a single categorical
choice or multiple related categorical choices. Each candidate
is a sub-space.
* :class:`pyglove.geno.Float`: Represents a continuous float value
within a range.
* :class:`pyglove.geno.CustomDecisionPoint`: Represents the
genotype for a :class:`pyglove.hyper.CustomHyper`.
All `DecisionPoints` provide `hints` for DNA generator as a reference
when generating values, it needs to be serializable to pass between client
and servers.
All DNASpec types allow user to attach their data via `set_userdata`
method, it's aimed to be used within the same process, thus not required to
be serializable.
"""
# Override format kwargs for __str__.
__str_format_kwargs__ = dict(
compact=True,
verbose=False,
hide_default_values=True,
hide_missing_values=True
)
# NOTE(daiyip): we disable the symbolic comparison to allow hashing DNASpec
# by object ID, therefore we can use DNASpec objects as the keys for a dict.
# This is helpful when we want to align decision points using DNASpec as
# dictionary key. Users can use `pg.eq`/`pg.ne` for symbolic comparisons
# and `pg.hash` for symbolic hashing.
use_symbolic_comparison = False
def _on_bound(self):
"""Event that is triggered when object is modified."""
super()._on_bound()
self._id = None
self._named_decision_points_cache = None
self._decision_point_by_id_cache = None
self._userdata = AttributeDict()
def _on_path_change(self, old_path, new_path):
"""Event that is triggered when path changes."""
super()._on_path_change(old_path, new_path)
# We invalidate the ID cache and decision_point_by_id cache
# when the ancestor hierarchy changes, which will force the ID and
# the cache to be recomputed upon usage.
self._id = None
self._decision_point_by_id_cache = None
@property
def _decision_point_by_id(self):
"""Returns lazy-loaded ID to decision point mapping."""
if self._decision_point_by_id_cache is None:
cache = {}
for dp in self.decision_points:
if dp.is_categorical and dp.is_subchoice:
parent_key = dp.parent_spec.id
parent_value = cache.get(parent_key, None)
if parent_value is None:
parent_value = []
cache[parent_key] = parent_value
parent_value.append(dp)
else:
cache[dp.id] = dp
self._decision_point_by_id_cache = cache
return self._decision_point_by_id_cache
@property
def _named_decision_points(self):
"""Return lazy-loaded named decision points."""
if self._named_decision_points_cache is None:
named_decision_points = {}
for dp in self.decision_points:
if dp.name is not None:
v = named_decision_points.get(dp.name, None)
if v is None:
named_decision_points[dp.name] = dp
elif isinstance(v, list):
v.append(dp)
else:
named_decision_points[dp.name] = [v, dp]
self._named_decision_points_cache = named_decision_points
return self._named_decision_points_cache
@property
@abc.abstractmethod
def is_space(self) -> bool:
"""Returns True if current node is a sub-space."""
@property
@abc.abstractmethod
def is_categorical(self) -> bool:
"""Returns True if current node is a categorical choice."""
@property
@abc.abstractmethod
def is_subchoice(self) -> bool:
"""Returns True if current node is a subchoice of a multi-choice."""
@property
@abc.abstractmethod
def is_numerical(self) -> bool:
"""Returns True if current node is numerical decision."""
@property
@abc.abstractmethod
def is_custom_decision_point(self) -> bool:
"""Returns True if current node is a custom decision point."""
@abc.abstractmethod
def validate(self, dna: 'DNA') -> bool:
"""Validate whether a DNA value conforms to this spec."""
@property
@abc.abstractmethod
def decision_points(self) -> List['DecisionPoint']:
"""Returns all decision points in their declaration order."""
@property
def decision_ids(self) -> List[object_utils.KeyPath]:
"""Returns decision IDs."""
return list(self._decision_point_by_id.keys())
@property
def named_decision_points(
self) -> Dict[str, Union['DecisionPoint', List['DecisionPoint']]]:
"""Returns all named decision points in their declaration order."""
return self._named_decision_points
@property
@abc.abstractmethod
def space_size(self) -> int:
"""Returns the size of the search space. Use -1 for infinity."""
@abc.abstractmethod
def __len__(self) -> int:
"""Returns the number of decision points."""
def first_dna(self, attach_spec: bool = True) -> 'DNA':
"""Returns the first DNA in the spec."""
return self.next_dna(None, attach_spec)
def next_dna(self,
dna: Optional['DNA'] = None,
attach_spec: bool = True) -> Optional['DNA']:
"""Returns the next DNA in the space represented by this spec.
Args:
dna: The DNA whose next will be returned. If None, `next_dna` will return
the first DNA.
attach_spec: If True, current spec will be attached to the returned DNA.
Returns:
The next DNA or None if there is no next DNA.
"""
dna = self._next_dna(dna)
if attach_spec and dna is not None:
dna.use_spec(self)
return dna
@abc.abstractmethod
def _next_dna(self, dna: Optional['DNA'] = None) -> Optional['DNA']:
"""Next DNA generation logic that should be overridden by subclasses."""
def random_dna(self,
random_generator: Union[types.ModuleType,
random.Random,
None] = None,
attach_spec: bool = True,
previous_dna: Optional['DNA'] = None) -> 'DNA':
"""Returns a random DNA based on current spec.
Args:
random_generator: An optional Random object. If None, the global random
module will be used.
attach_spec: If True, current spec will be attached to the returned DNA.
previous_dna: An optional DNA representing previous DNA. This field might
be useful for generating stateful random DNAs.
Returns:
A random DNA based on current spec.
"""
random_generator = random_generator or random
dna = self._random_dna(random_generator, previous_dna)
if attach_spec:
dna.use_spec(self)
return dna
@abc.abstractmethod
def _random_dna(self,
random_generator: Union[types.ModuleType, random.Random],
previous_dna: Optional['DNA']) -> 'DNA':
"""Random DNA generation logic that should be overridden by subclasses."""
def iter_dna(self, dna: Optional['DNA'] = None, attach_spec: bool = True):
"""Iterate the DNA in the space represented by this spec.
Args:
dna: An optional DNA as the start point (exclusive) for iteration.
attach_spec: If True, the DNASpec will be attached to the DNA returned.
Yields:
The next DNA according to the spec.
"""
while True:
dna = self.next_dna(dna, attach_spec)
if dna is None:
break
yield dna
@property
def parent_spec(self) -> Optional['DNASpec']:
"""Returns parent spec. None if spec is root."""
if self.sym_parent is None:
return None
# NOTE(daiyip):
# For child specs of Space, `self.sym_parent` points to `Space.elements`.
# For child specs of Choices, `self.sym_parent` points to
# `Choices.candidates` or `Choices._subchoice_specs`.
assert self.sym_parent.sym_parent is not None
return self.sym_parent.sym_parent # pytype: disable | (location: pyglove.core.object_utils.value_location.KeyPath = , hints=None) | [
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|
40,533 | pyglove.core.geno.base | __getitem__ | Get decision point(s) by name or ID . | def __getitem__(
self,
name_or_id: Union[object_utils.KeyPath, str]
) -> Union['DecisionPoint', List['DecisionPoint']]:
"""Get decision point(s) by name or ID ."""
v = self._named_decision_points.get(name_or_id, None)
if v is None:
v = self._decision_point_by_id[name_or_id]
return v
| (self, name_or_id: Union[pyglove.core.object_utils.value_location.KeyPath, str]) -> Union[pyglove.core.geno.base.DecisionPoint, List[pyglove.core.geno.base.DecisionPoint]] | [
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|
40,536 | pyglove.core.geno.base | __init__ | null | @symbolic.members([
('name', pg_typing.Str().noneable(),
('Name of current node. If present, it should be unique in the search '
'space. We can use `root_spec[name]` to access named DNASpec.')),
])
class DecisionPoint(DNASpec):
"""Base class for decision points.
Child classes:
* :class:`pyglove.geno.Choices`
* :class:`pyglove.geno.Float`
* :class:`pyglove.geno.CustomDecisionPoint`
"""
@property
def is_space(self) -> bool:
return False
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|
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