searchalgorithm
This namespace contains SearchAlgorithm, the base class for all
evolutionary algorithms.
LazyReporter
¶
This class provides an interface of storing and reporting status. This class is designed to be inherited by other classes.
Let us assume that we have the following class inheriting from LazyReporter:
class Example(LazyReporter):
def __init__(self):
LazyReporter.__init__(self, a=self._get_a, b=self._get_b)
def _get_a(self):
return ... # return the status 'a'
def _get_b(self):
return ... # return the status 'b'
At its initialization phase, this Example class registers its methods
_get_a and _get_b as its status providers.
Having the LazyReporter interface, the Example class gains a status
property:
ex = Example()
print(ex.status["a"]) # Get the status 'a'
print(ex.status["b"]) # Get the status 'b'
Once a status is queried, its computation result is stored to be re-used later. After running the code above, if we query the status 'a' again:
then the status 'a' is not computed again (i.e. _get_a is not
called again). Instead, the stored status value of 'a' is re-used.
To force re-computation of the status values, one can execute:
Or the Example instance can clear its status from within one of its methods:
Source code in evotorch/algorithms/searchalgorithm.py
class LazyReporter:
"""
This class provides an interface of storing and reporting status.
This class is designed to be inherited by other classes.
Let us assume that we have the following class inheriting from
LazyReporter:
```python
class Example(LazyReporter):
def __init__(self):
LazyReporter.__init__(self, a=self._get_a, b=self._get_b)
def _get_a(self):
return ... # return the status 'a'
def _get_b(self):
return ... # return the status 'b'
```
At its initialization phase, this Example class registers its methods
``_get_a`` and ``_get_b`` as its status providers.
Having the LazyReporter interface, the Example class gains a ``status``
property:
```python
ex = Example()
print(ex.status["a"]) # Get the status 'a'
print(ex.status["b"]) # Get the status 'b'
```
Once a status is queried, its computation result is stored to be re-used
later. After running the code above, if we query the status 'a' again:
```python
print(ex.status["a"]) # Getting the status 'a' again
```
then the status 'a' is not computed again (i.e. ``_get_a`` is not
called again). Instead, the stored status value of 'a' is re-used.
To force re-computation of the status values, one can execute:
```python
ex.clear_status()
```
Or the Example instance can clear its status from within one of its
methods:
```python
class Example(LazyReporter):
...
def some_method(self):
...
self.clear_status()
```
"""
@staticmethod
def _missing_status_producer():
return None
def __init__(self, **kwargs):
"""
`__init__(...)`: Initialize the LazyReporter instance.
Args:
kwargs: Keyword arguments, mapping the status keys to the
methods or functions providing the status values.
"""
self.__getters = kwargs
self.__computed = {}
def get_status_value(self, key: Any) -> Any:
"""
Get the specified status value.
Args:
key: The key (i.e. the name) of the status variable.
"""
if key not in self.__computed:
self.__computed[key] = self.__getters[key]()
return self.__computed[key]
def has_status_key(self, key: Any) -> bool:
"""
Return True if there is a status variable with the specified key.
Otherwise, return False.
Args:
key: The key (i.e. the name) of the status variable whose
existence is to be checked.
Returns:
True if there is such a key; False otherwise.
"""
return key in self.__getters
def iter_status_keys(self):
"""Iterate over the status keys."""
return self.__getters.keys()
def clear_status(self):
"""Clear all the stored values of the status variables."""
self.__computed.clear()
def is_status_computed(self, key) -> bool:
"""
Return True if the specified status is computed yet.
Return False otherwise.
Args:
key: The key (i.e. the name) of the status variable.
Returns:
True if the status of the given key is computed; False otherwise.
"""
return key in self.__computed
def update_status(self, additional_status: Mapping):
"""
Update the stored status with an external dict-like object.
The given dict-like object can override existing status keys
with new values, and also bring new keys to the status.
Args:
additional_status: A dict-like object storing the status update.
"""
for k, v in additional_status.items():
if k not in self.__getters:
self.__getters[k] = LazyReporter._missing_status_producer
self.__computed[k] = v
def add_status_getters(self, getters: Mapping):
"""
Register additional status-getting functions.
Args:
getters: A dictionary-like object where the keys are the
additional status variable names, and values are functions
which are expected to compute/retrieve the values for those
status variables.
"""
self.__getters.update(getters)
@property
def status(self) -> "LazyStatusDict":
"""Get a LazyStatusDict which is bound to this LazyReporter."""
return LazyStatusDict(self)
status: LazyStatusDict
property
readonly
¶
Get a LazyStatusDict which is bound to this LazyReporter.
__init__(self, **kwargs)
special
¶
__init__(...): Initialize the LazyReporter instance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
kwargs |
Keyword arguments, mapping the status keys to the methods or functions providing the status values. |
{} |
Source code in evotorch/algorithms/searchalgorithm.py
add_status_getters(self, getters)
¶
Register additional status-getting functions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
getters |
Mapping |
A dictionary-like object where the keys are the additional status variable names, and values are functions which are expected to compute/retrieve the values for those status variables. |
required |
Source code in evotorch/algorithms/searchalgorithm.py
def add_status_getters(self, getters: Mapping):
"""
Register additional status-getting functions.
Args:
getters: A dictionary-like object where the keys are the
additional status variable names, and values are functions
which are expected to compute/retrieve the values for those
status variables.
"""
self.__getters.update(getters)
clear_status(self)
¶
get_status_value(self, key)
¶
Get the specified status value.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
Any |
The key (i.e. the name) of the status variable. |
required |
Source code in evotorch/algorithms/searchalgorithm.py
has_status_key(self, key)
¶
Return True if there is a status variable with the specified key. Otherwise, return False.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
Any |
The key (i.e. the name) of the status variable whose existence is to be checked. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if there is such a key; False otherwise. |
Source code in evotorch/algorithms/searchalgorithm.py
def has_status_key(self, key: Any) -> bool:
"""
Return True if there is a status variable with the specified key.
Otherwise, return False.
Args:
key: The key (i.e. the name) of the status variable whose
existence is to be checked.
Returns:
True if there is such a key; False otherwise.
"""
return key in self.__getters
is_status_computed(self, key)
¶
Return True if the specified status is computed yet. Return False otherwise.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
key |
The key (i.e. the name) of the status variable. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if the status of the given key is computed; False otherwise. |
Source code in evotorch/algorithms/searchalgorithm.py
iter_status_keys(self)
¶
update_status(self, additional_status)
¶
Update the stored status with an external dict-like object. The given dict-like object can override existing status keys with new values, and also bring new keys to the status.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
additional_status |
Mapping |
A dict-like object storing the status update. |
required |
Source code in evotorch/algorithms/searchalgorithm.py
def update_status(self, additional_status: Mapping):
"""
Update the stored status with an external dict-like object.
The given dict-like object can override existing status keys
with new values, and also bring new keys to the status.
Args:
additional_status: A dict-like object storing the status update.
"""
for k, v in additional_status.items():
if k not in self.__getters:
self.__getters[k] = LazyReporter._missing_status_producer
self.__computed[k] = v
LazyStatusDict (Mapping)
¶
A Mapping subclass used by the status property of a LazyReporter.
The interface of this object is similar to a read-only dictionary.
Source code in evotorch/algorithms/searchalgorithm.py
class LazyStatusDict(Mapping):
"""
A Mapping subclass used by the `status` property of a `LazyReporter`.
The interface of this object is similar to a read-only dictionary.
"""
def __init__(self, lazy_reporter: LazyReporter):
"""
`__init__(...)`: Initialize the LazyStatusDict object.
Args:
lazy_reporter: The LazyReporter object whose status is to be
accessed.
"""
super().__init__()
self.__lazy_reporter = lazy_reporter
def __getitem__(self, key: Any) -> Any:
result = self.__lazy_reporter.get_status_value(key)
if isinstance(result, (torch.Tensor, ObjectArray)):
result = as_read_only_tensor(result)
return result
def __len__(self) -> int:
return len(list(self.__lazy_reporter.iter_status_keys()))
def __iter__(self):
for k in self.__lazy_reporter.iter_status_keys():
yield k
def __contains__(self, key: Any) -> bool:
return self.__lazy_reporter.has_status_key(key)
def _to_string(self) -> str:
with io.StringIO() as f:
print("<" + type(self).__name__, file=f)
for k in self.__lazy_reporter.iter_status_keys():
if self.__lazy_reporter.is_status_computed(k):
r = repr(self.__lazy_reporter.get_status_value(k))
else:
r = "<not yet computed>"
print(" ", k, "=", r, file=f)
print(">", end="", file=f)
f.seek(0)
entire_str = f.read()
return entire_str
def __str__(self) -> str:
return self._to_string()
def __repr__(self) -> str:
return self._to_string()
__init__(self, lazy_reporter)
special
¶
__init__(...): Initialize the LazyStatusDict object.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
lazy_reporter |
LazyReporter |
The LazyReporter object whose status is to be accessed. |
required |
SearchAlgorithm (LazyReporter)
¶
Base class for all evolutionary search algorithms.
An algorithm developer is expected to inherit from this base class,
and override the method named _step() to define how a single
step of this new algorithm is performed.
For each core status dictionary element, a new method is expected
to exist within the inheriting class. These status reporting
methods are then registered via the keyword arguments of the
__init__(...) method of SearchAlgorithm.
To sum up, a newly developed algorithm inheriting from this base class is expected in this structure:
from evotorch import Problem
class MyNewAlgorithm(SearchAlgorithm):
def __init__(self, problem: Problem):
SearchAlgorithm.__init__(
self, problem, status1=self._get_status1, status2=self._get_status2, ...
)
def _step(self):
# Code that defines how a step of this algorithm
# should work goes here.
...
def _get_status1(self):
# The value returned by this function will be shown
# in the status dictionary, associated with the key
# 'status1'.
return ...
def _get_status2(self):
# The value returned by this function will be shown
# in the status dictionary, associated with the key
# 'status2'.
return ...
Source code in evotorch/algorithms/searchalgorithm.py
class SearchAlgorithm(LazyReporter):
"""
Base class for all evolutionary search algorithms.
An algorithm developer is expected to inherit from this base class,
and override the method named `_step()` to define how a single
step of this new algorithm is performed.
For each core status dictionary element, a new method is expected
to exist within the inheriting class. These status reporting
methods are then registered via the keyword arguments of the
`__init__(...)` method of `SearchAlgorithm`.
To sum up, a newly developed algorithm inheriting from this base
class is expected in this structure:
```python
from evotorch import Problem
class MyNewAlgorithm(SearchAlgorithm):
def __init__(self, problem: Problem):
SearchAlgorithm.__init__(
self, problem, status1=self._get_status1, status2=self._get_status2, ...
)
def _step(self):
# Code that defines how a step of this algorithm
# should work goes here.
...
def _get_status1(self):
# The value returned by this function will be shown
# in the status dictionary, associated with the key
# 'status1'.
return ...
def _get_status2(self):
# The value returned by this function will be shown
# in the status dictionary, associated with the key
# 'status2'.
return ...
```
"""
def __init__(self, problem: Problem, **kwargs):
"""
Initialize the SearchAlgorithm instance.
Args:
problem: Problem to work with.
kwargs: Any additional keyword argument, in the form of `k=f`,
is accepted in this manner: for each pair of `k` and `f`,
`k` is accepted as the status key (i.e. a status variable
name), and `f` is accepted as a function (probably a method
of the inheriting class) that will generate the value of that
status variable.
"""
super().__init__(**kwargs)
self._problem = problem
self._before_step_hook = Hook()
self._after_step_hook = Hook()
self._log_hook = Hook()
self._end_of_run_hook = Hook()
self._steps_count: int = 0
self._first_step_datetime: Optional[datetime] = None
@property
def problem(self) -> Problem:
"""
The problem object which is being worked on.
"""
return self._problem
@property
def before_step_hook(self) -> Hook:
"""
Use this Hook to add more behavior to the search algorithm
to be performed just before executing a step.
"""
return self._before_step_hook
@property
def after_step_hook(self) -> Hook:
"""
Use this Hook to add more behavior to the search algorithm
to be performed just after executing a step.
The dictionaries returned by the functions registered into
this Hook will be accumulated and added into the status
dictionary of the search algorithm.
"""
return self._after_step_hook
@property
def log_hook(self) -> Hook:
"""
Use this Hook to add more behavior to the search algorithm
at the moment of logging the constructed status dictionary.
This Hook is executed after the execution of `after_step_hook`
is complete.
The functions in this Hook are assumed to expect a single
argument, that is the status dictionary of the search algorithm.
"""
return self._log_hook
@property
def end_of_run_hook(self) -> Hook:
"""
Use this Hook to add more behavior to the search algorithm
at the end of a run.
This Hook is executed after all the generations of a run
are done.
The functions in this Hook are assumed to expect a single
argument, that is the status dictionary of the search algorithm.
"""
return self._end_of_run_hook
@property
def step_count(self) -> int:
"""
Number of search steps performed.
This is equivalent to the number of generations, or to the
number of iterations.
"""
return self._steps_count
@property
def steps_count(self) -> int:
"""
Deprecated alias for the `step_count` property.
It is recommended to use the `step_count` property instead.
"""
return self._steps_count
def step(self):
"""
Perform a step of the search algorithm.
"""
self._before_step_hook()
self.clear_status()
if self._first_step_datetime is None:
self._first_step_datetime = datetime.now()
self._step()
self._steps_count += 1
self.update_status({"iter": self._steps_count})
self.update_status(self._problem.status)
extra_status = self._after_step_hook.accumulate_dict()
self.update_status(extra_status)
if len(self._log_hook) >= 1:
self._log_hook(dict(self.status))
def _step(self):
"""
Algorithm developers are expected to override this method
in an inheriting subclass.
The code which defines how a step of the evolutionary algorithm
is performed goes here.
"""
raise NotImplementedError
def run(self, num_generations: int, *, reset_first_step_datetime: bool = True):
"""
Run the algorithm for the given number of generations
(i.e. iterations).
Args:
num_generations: Number of generations.
reset_first_step_datetime: If this argument is given as True,
then, the datetime of the first search step will be forgotten.
Forgetting the first step's datetime means that the first step
taken by this new run will be the new first step datetime.
"""
if reset_first_step_datetime:
self.reset_first_step_datetime()
for _ in range(int(num_generations)):
self.step()
if len(self._end_of_run_hook) >= 1:
self._end_of_run_hook(dict(self.status))
@property
def first_step_datetime(self) -> Optional[datetime]:
"""
Get the datetime when the algorithm took the first search step.
If a step is not taken at all, then the result will be None.
"""
return self._first_step_datetime
def reset_first_step_datetime(self):
"""
Reset (or forget) the first step's datetime.
"""
self._first_step_datetime = None
after_step_hook: Hook
property
readonly
¶
Use this Hook to add more behavior to the search algorithm to be performed just after executing a step.
The dictionaries returned by the functions registered into this Hook will be accumulated and added into the status dictionary of the search algorithm.
before_step_hook: Hook
property
readonly
¶
Use this Hook to add more behavior to the search algorithm to be performed just before executing a step.
end_of_run_hook: Hook
property
readonly
¶
Use this Hook to add more behavior to the search algorithm at the end of a run.
This Hook is executed after all the generations of a run are done.
The functions in this Hook are assumed to expect a single argument, that is the status dictionary of the search algorithm.
first_step_datetime: Optional[datetime.datetime]
property
readonly
¶
Get the datetime when the algorithm took the first search step. If a step is not taken at all, then the result will be None.
log_hook: Hook
property
readonly
¶
Use this Hook to add more behavior to the search algorithm at the moment of logging the constructed status dictionary.
This Hook is executed after the execution of after_step_hook
is complete.
The functions in this Hook are assumed to expect a single argument, that is the status dictionary of the search algorithm.
problem: Problem
property
readonly
¶
The problem object which is being worked on.
step_count: int
property
readonly
¶
Number of search steps performed.
This is equivalent to the number of generations, or to the number of iterations.
steps_count: int
property
readonly
¶
Deprecated alias for the step_count property.
It is recommended to use the step_count property instead.
__init__(self, problem, **kwargs)
special
¶
Initialize the SearchAlgorithm instance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
problem |
Problem |
Problem to work with. |
required |
kwargs |
Any additional keyword argument, in the form of |
{} |
Source code in evotorch/algorithms/searchalgorithm.py
def __init__(self, problem: Problem, **kwargs):
"""
Initialize the SearchAlgorithm instance.
Args:
problem: Problem to work with.
kwargs: Any additional keyword argument, in the form of `k=f`,
is accepted in this manner: for each pair of `k` and `f`,
`k` is accepted as the status key (i.e. a status variable
name), and `f` is accepted as a function (probably a method
of the inheriting class) that will generate the value of that
status variable.
"""
super().__init__(**kwargs)
self._problem = problem
self._before_step_hook = Hook()
self._after_step_hook = Hook()
self._log_hook = Hook()
self._end_of_run_hook = Hook()
self._steps_count: int = 0
self._first_step_datetime: Optional[datetime] = None
reset_first_step_datetime(self)
¶
run(self, num_generations, *, reset_first_step_datetime=True)
¶
Run the algorithm for the given number of generations (i.e. iterations).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
num_generations |
int |
Number of generations. |
required |
reset_first_step_datetime |
bool |
If this argument is given as True, then, the datetime of the first search step will be forgotten. Forgetting the first step's datetime means that the first step taken by this new run will be the new first step datetime. |
True |
Source code in evotorch/algorithms/searchalgorithm.py
def run(self, num_generations: int, *, reset_first_step_datetime: bool = True):
"""
Run the algorithm for the given number of generations
(i.e. iterations).
Args:
num_generations: Number of generations.
reset_first_step_datetime: If this argument is given as True,
then, the datetime of the first search step will be forgotten.
Forgetting the first step's datetime means that the first step
taken by this new run will be the new first step datetime.
"""
if reset_first_step_datetime:
self.reset_first_step_datetime()
for _ in range(int(num_generations)):
self.step()
if len(self._end_of_run_hook) >= 1:
self._end_of_run_hook(dict(self.status))
step(self)
¶
Perform a step of the search algorithm.
Source code in evotorch/algorithms/searchalgorithm.py
def step(self):
"""
Perform a step of the search algorithm.
"""
self._before_step_hook()
self.clear_status()
if self._first_step_datetime is None:
self._first_step_datetime = datetime.now()
self._step()
self._steps_count += 1
self.update_status({"iter": self._steps_count})
self.update_status(self._problem.status)
extra_status = self._after_step_hook.accumulate_dict()
self.update_status(extra_status)
if len(self._log_hook) >= 1:
self._log_hook(dict(self.status))
SinglePopulationAlgorithmMixin
¶
A mixin class that can be inherited by a SearchAlgorithm subclass.
This mixin class assumes that the inheriting class has the following members:
problem: The problem object that is associated with the search algorithm. This attribute is already provided by the SearchAlgorithm base class.population: An attribute or a (possibly read-only) property which stores the population of the search algorithm as aSolutionBatchinstance.
This mixin class also assumes that the inheriting class might
contain an attribute (or a property) named obj_index.
If there is such an attribute and its value is not None, then this
mixin class assumes that obj_index represents the index of the
objective that is being focused on.
Upon initialization, this mixin class first determines whether or not
the algorithm is a single-objective one.
In more details, if there is an attribute named obj_index (and its
value is not None), or if the associated problem has only one objective,
then this mixin class assumes that the inheriting SearchAlgorithm is a
single objective algorithm.
Otherwise, it is assumed that the underlying algorithm works (or might
work) on multiple objectives.
In the single-objective case, this mixin class brings the inheriting
SearchAlgorithm the ability to report the following:
pop_best (best solution of the population),
pop_best_eval (evaluation result of the population's best solution),
mean_eval (mean evaluation result of the population),
median_eval (median evaluation result of the population).
In the multi-objective case, for each objective i, this mixin class
brings the inheriting SearchAlgorithm the ability to report the following:
obj<i>_pop_best (best solution of the population according),
obj<i>_pop_best_eval (evaluation result of the population's best
solution),
obj<i>_mean_eval (mean evaluation result of the population)
obj<iP_median_eval (median evaluation result of the population).
Source code in evotorch/algorithms/searchalgorithm.py
class SinglePopulationAlgorithmMixin:
"""
A mixin class that can be inherited by a SearchAlgorithm subclass.
This mixin class assumes that the inheriting class has the following
members:
- `problem`: The problem object that is associated with the search
algorithm. This attribute is already provided by the SearchAlgorithm
base class.
- `population`: An attribute or a (possibly read-only) property which
stores the population of the search algorithm as a `SolutionBatch`
instance.
This mixin class also assumes that the inheriting class _might_
contain an attribute (or a property) named `obj_index`.
If there is such an attribute and its value is not None, then this
mixin class assumes that `obj_index` represents the index of the
objective that is being focused on.
Upon initialization, this mixin class first determines whether or not
the algorithm is a single-objective one.
In more details, if there is an attribute named `obj_index` (and its
value is not None), or if the associated problem has only one objective,
then this mixin class assumes that the inheriting SearchAlgorithm is a
single objective algorithm.
Otherwise, it is assumed that the underlying algorithm works (or might
work) on multiple objectives.
In the single-objective case, this mixin class brings the inheriting
SearchAlgorithm the ability to report the following:
`pop_best` (best solution of the population),
`pop_best_eval` (evaluation result of the population's best solution),
`mean_eval` (mean evaluation result of the population),
`median_eval` (median evaluation result of the population).
In the multi-objective case, for each objective `i`, this mixin class
brings the inheriting SearchAlgorithm the ability to report the following:
`obj<i>_pop_best` (best solution of the population according),
`obj<i>_pop_best_eval` (evaluation result of the population's best
solution),
`obj<i>_mean_eval` (mean evaluation result of the population)
`obj<iP_median_eval` (median evaluation result of the population).
"""
class ObjectiveStatusReporter:
REPORTABLES = {"pop_best", "pop_best_eval", "mean_eval", "median_eval"}
def __init__(
self,
algorithm: SearchAlgorithm,
*,
obj_index: int,
to_report: str,
):
self.__algorithm = algorithm
self.__obj_index = int(obj_index)
if to_report not in self.REPORTABLES:
raise ValueError(f"Unrecognized report request: {to_report}")
self.__to_report = to_report
@property
def population(self) -> SolutionBatch:
return self.__algorithm.population
@property
def obj_index(self) -> int:
return self.__obj_index
def get_status_value(self, status_key: str) -> Any:
return self.__algorithm.get_status_value(status_key)
def has_status_key(self, status_key: str) -> bool:
return self.__algorithm.has_status_key(status_key)
def _get_pop_best(self):
i = self.population.argbest(self.obj_index)
return clone(self.population[i])
def _get_pop_best_eval(self):
pop_best = None
pop_best_keys = ("pop_best", f"obj{self.obj_index}_pop_best")
for pop_best_key in pop_best_keys:
if self.has_status_key(pop_best_key):
pop_best = self.get_status_value(pop_best_key)
break
if (pop_best is not None) and pop_best.is_evaluated:
return float(pop_best.evals[self.obj_index])
else:
return None
@torch.no_grad()
def _get_mean_eval(self):
return float(torch.mean(self.population.access_evals(self.obj_index)))
@torch.no_grad()
def _get_median_eval(self):
return float(torch.median(self.population.access_evals(self.obj_index)))
def __call__(self):
return getattr(self, "_get_" + self.__to_report)()
def __init__(self, *, exclude: Optional[Iterable] = None, enable: bool = True):
if not enable:
return
ObjectiveStatusReporter = self.ObjectiveStatusReporter
reportables = ObjectiveStatusReporter.REPORTABLES
single_obj: Optional[int] = None
self.__exclude = set() if exclude is None else set(exclude)
if hasattr(self, "obj_index") and (self.obj_index is not None):
single_obj = self.obj_index
elif len(self.problem.senses) == 1:
single_obj = 0
if single_obj is not None:
for reportable in reportables:
if reportable not in self.__exclude:
self.add_status_getters(
{reportable: ObjectiveStatusReporter(self, obj_index=single_obj, to_report=reportable)}
)
else:
for i_obj in range(len(self.problem.senses)):
for reportable in reportables:
if reportable not in self.__exclude:
self.add_status_getters(
{
f"obj{i_obj}_{reportable}": ObjectiveStatusReporter(
self, obj_index=i_obj, to_report=reportable
),
}
)