Funccem
CEMState (tuple)
¶
CEMState(center, stdev, stdev_min, stdev_max, stdev_max_change, parenthood_ratio, maximize)
Source code in evotorch/algorithms/functional/funccem.py
cem(*, center_init, parenthood_ratio, objective_sense, stdev_init=None, radius_init=None, stdev_min=None, stdev_max=None, stdev_max_change=None)
¶
Get an initial state for the cross entropy method (CEM).
The received initial state, a named tuple of type CEMState
, is to be
passed to the function cem_ask(...)
to receive the solutions belonging
to the first generation of the evolutionary search.
References:
Rubinstein, R. (1999). The cross-entropy method for combinatorial
and continuous optimization.
Methodology and computing in applied probability, 1(2), 127-190.
Duan, Y., Chen, X., Houthooft, R., Schulman, J., Abbeel, P. (2016).
Benchmarking deep reinforcement learning for continuous control.
International conference on machine learning. PMLR, 2016.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
center_init |
Union[torch.Tensor, numpy.ndarray] |
Center (i.e. mean) of the initial search distribution.
Expected as a PyTorch tensor with at least 1 dimension.
If the given |
required |
stdev_init |
Union[float, torch.Tensor, numpy.ndarray] |
Standard deviation of the initial search distribution.
If this is given as a scalar |
None |
radius_init |
Union[float, numbers.Number, numpy.ndarray, torch.Tensor] |
Radius for the initial search distribution, representing
the euclidean norm for the first standard deviation vector.
Setting this value as |
None |
parenthood_ratio |
float |
Proportion of the solutions that will be chosen as the parents for the next generation. For example, if this is given as 0.5, the top 50% of the solutions will be chosen as parents. |
required |
objective_sense |
str |
Expected as a string, either as 'min' or as 'max'. Determines if the goal is to minimize or is to maximize. |
required |
stdev_min |
Union[float, torch.Tensor, numpy.ndarray] |
Minimum allowed standard deviation for the search distribution. Can be given as a scalar or as a tensor with one or more dimensions. When given with at least 2 dimensions, the extra leftmost dimensions will be interpreted as batch dimensions. |
None |
stdev_max |
Union[float, torch.Tensor, numpy.ndarray] |
Maximum allowed standard deviation for the search distribution. Can be given as a scalar or as a tensor with one or more dimensions. When given with at least 2 dimensions, the extra leftmost dimensions will be interpreted as batch dimensions. |
None |
stdev_max_change |
Union[float, torch.Tensor, numpy.ndarray] |
Maximum allowed change for the standard deviation
vector. If this is given as a scalar, this scalar will serve as a
limiter for the change of the entire standard deviation vector.
For example, a scalar value of 0.2 means that the elements of the
standard deviation vector cannot change more than the 20% of their
original values. If this is given as a vector (i.e. as a
1-dimensional tensor), each element of |
None |
Returns:
Type | Description |
---|---|
CEMState |
A named tuple, of type |
Source code in evotorch/algorithms/functional/funccem.py
def cem(
*,
center_init: BatchableVector,
parenthood_ratio: float,
objective_sense: str,
stdev_init: Optional[Union[float, BatchableVector]] = None,
radius_init: Optional[Union[float, BatchableScalar]] = None,
stdev_min: Optional[Union[float, BatchableVector]] = None,
stdev_max: Optional[Union[float, BatchableVector]] = None,
stdev_max_change: Optional[Union[float, BatchableVector]] = None,
) -> CEMState:
"""
Get an initial state for the cross entropy method (CEM).
The received initial state, a named tuple of type `CEMState`, is to be
passed to the function `cem_ask(...)` to receive the solutions belonging
to the first generation of the evolutionary search.
References:
Rubinstein, R. (1999). The cross-entropy method for combinatorial
and continuous optimization.
Methodology and computing in applied probability, 1(2), 127-190.
Duan, Y., Chen, X., Houthooft, R., Schulman, J., Abbeel, P. (2016).
Benchmarking deep reinforcement learning for continuous control.
International conference on machine learning. PMLR, 2016.
Args:
center_init: Center (i.e. mean) of the initial search distribution.
Expected as a PyTorch tensor with at least 1 dimension.
If the given `center` tensor has more than 1 dimensions, the extra
leftmost dimensions will be interpreted as batch dimensions.
stdev_init: Standard deviation of the initial search distribution.
If this is given as a scalar `s`, the standard deviation for the
search distribution will be interpreted as `[s, s, ..., s]` whose
length is the same with the length of `center_init`.
If this is given as a 1-dimensional tensor, the given tensor will
be interpreted as the standard deviation vector.
If this is given as a tensor with at least 2 dimensions, the extra
leftmost dimension(s) will be interpreted as batch dimensions.
If you wish to express the coverage area of the initial search
distribution in terms of "radius" instead, you can leave
`stdev_init` as None, and provide a value for the argument
`radius_init`.
radius_init: Radius for the initial search distribution, representing
the euclidean norm for the first standard deviation vector.
Setting this value as `r` means that the standard deviation
vector will be initialized as a vector `[s, s, ..., s]`
whose norm will be equal to `r`. In the non-batched case,
`radius_init` is expected as a scalar value.
If `radius_init` is given as a tensor with 1 or more
dimensions, those dimensions will be considered as batch
dimensions. If you wish to express the coverage are of the initial
search distribution in terms of the standard deviation values
instead, you can leave `radius_init` as None, and provide a value
for the argument `stdev_init`.
parenthood_ratio: Proportion of the solutions that will be chosen as
the parents for the next generation. For example, if this is
given as 0.5, the top 50% of the solutions will be chosen as
parents.
objective_sense: Expected as a string, either as 'min' or as 'max'.
Determines if the goal is to minimize or is to maximize.
stdev_min: Minimum allowed standard deviation for the search
distribution. Can be given as a scalar or as a tensor with one or
more dimensions. When given with at least 2 dimensions, the extra
leftmost dimensions will be interpreted as batch dimensions.
stdev_max: Maximum allowed standard deviation for the search
distribution. Can be given as a scalar or as a tensor with one or
more dimensions. When given with at least 2 dimensions, the extra
leftmost dimensions will be interpreted as batch dimensions.
stdev_max_change: Maximum allowed change for the standard deviation
vector. If this is given as a scalar, this scalar will serve as a
limiter for the change of the entire standard deviation vector.
For example, a scalar value of 0.2 means that the elements of the
standard deviation vector cannot change more than the 20% of their
original values. If this is given as a vector (i.e. as a
1-dimensional tensor), each element of `stdev_max_change` will
serve as a limiter to its corresponding element within the standard
deviation vector. If `stdev_max_change` is given as a tensor with
at least 2 dimensions, the extra leftmost dimension(s) will be
interpreted as batch dimensions.
If you do not wish to have such a limiter, you can leave this as
None.
Returns:
A named tuple, of type `CEMState`, storing the hyperparameters and the
initial state of the cross entropy method.
"""
from .misc import _get_stdev_init
center_init = torch.as_tensor(center_init)
if center_init.ndim < 1:
raise ValueError(
"The center of the search distribution for the functional CEM was expected"
" as a tensor with at least 1 dimension."
f" However, the encountered `center_init` is {center_init}, of shape {center_init.shape}."
)
solution_length = center_init.shape[-1]
if solution_length == 0:
raise ValueError("Solution length cannot be 0")
stdev_init = _get_stdev_init(center_init=center_init, stdev_init=stdev_init, radius_init=radius_init)
device = center_init.device
dtype = center_init.dtype
def as_vector_like_center(x: Iterable, vector_name: str) -> torch.Tensor:
x = torch.as_tensor(x, dtype=dtype, device=device)
if x.ndim == 0:
x = x.repeat(solution_length)
else:
if x.shape[-1] != solution_length:
raise ValueError(
f"`{vector_name}` has an incompatible length."
f" The length of `{vector_name}`: {x.shape[-1]},"
f" but the solution length implied by the provided `center_init` is {solution_length}."
)
return x
if stdev_min is None:
stdev_min = 0.0
stdev_min = as_vector_like_center(stdev_min, "stdev_min")
if stdev_max is None:
stdev_max = float("inf")
stdev_max = as_vector_like_center(stdev_max, "stdev_max")
if stdev_max_change is None:
stdev_max_change = float("inf")
stdev_max_change = as_vector_like_center(stdev_max_change, "stdev_max_change")
parenthood_ratio = float(parenthood_ratio)
if objective_sense == "min":
maximize = False
elif objective_sense == "max":
maximize = True
else:
raise ValueError(
f"`objective_sense` was expected as 'min' or 'max', but it was received as {repr(objective_sense)}"
)
return CEMState(
center=center_init,
stdev=stdev_init,
stdev_min=stdev_min,
stdev_max=stdev_max,
stdev_max_change=stdev_max_change,
parenthood_ratio=parenthood_ratio,
maximize=maximize,
)
cem_ask(state, *, popsize)
¶
Obtain a population from cross entropy method, given the state.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
state |
CEMState |
The current state of the cross entropy method search. |
required |
popsize |
int |
Number of solutions to be generated for the requested population. |
required |
Returns:
Type | Description |
---|---|
Tensor |
Population, as a tensor of at least 2 dimensions. |
Source code in evotorch/algorithms/functional/funccem.py
def cem_ask(state: CEMState, *, popsize: int) -> torch.Tensor:
"""
Obtain a population from cross entropy method, given the state.
Args:
state: The current state of the cross entropy method search.
popsize: Number of solutions to be generated for the requested
population.
Returns:
Population, as a tensor of at least 2 dimensions.
"""
return _cem_ask(state.center, state.stdev, state.parenthood_ratio, popsize)
cem_tell(state, values, evals)
¶
Given the old state and the evals (fitnesses), obtain the next state.
From this state tuple, the center point of the search distribution can be
obtained via the field .center
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
state |
CEMState |
The old state of the cross entropy method search. |
required |
values |
Tensor |
The most recent population, as a PyTorch tensor. |
required |
evals |
Tensor |
Evaluation results (i.e. fitnesses) for the solutions expressed
by |
required |
Returns:
Type | Description |
---|---|
CEMState |
The new state of the cross entropy method search. |
Source code in evotorch/algorithms/functional/funccem.py
def cem_tell(state: CEMState, values: torch.Tensor, evals: torch.Tensor) -> CEMState:
"""
Given the old state and the evals (fitnesses), obtain the next state.
From this state tuple, the center point of the search distribution can be
obtained via the field `.center`.
Args:
state: The old state of the cross entropy method search.
values: The most recent population, as a PyTorch tensor.
evals: Evaluation results (i.e. fitnesses) for the solutions expressed
by `values`. For example, if `values` is shaped `(N, L)`, this means
that there are `N` solutions (of length `L`). So, `evals` is
expected as a 1-dimensional tensor of length `N`, where `evals[i]`
expresses the fitness of the solution `values[i, :]`.
If `values` is shaped `(B, N, L)`, then there is also a batch
dimension, so, `evals` is expected as a 2-dimensional tensor of
shape `(B, N)`.
Returns:
The new state of the cross entropy method search.
"""
new_center, new_stdev = _cem_tell(
state.stdev_min,
state.stdev_max,
state.stdev_max_change,
state.parenthood_ratio,
state.maximize,
state.center,
state.stdev,
values,
evals,
)
return CEMState(
center=new_center,
stdev=new_stdev,
stdev_min=state.stdev_min,
stdev_max=state.stdev_max,
stdev_max_change=state.stdev_max_change,
parenthood_ratio=state.parenthood_ratio,
maximize=state.maximize,
)