Sanitizers

def sanitize_choice(...)

def sanitize_choice(value: T, valid_choices: Iterable[T], default: T) -> T

The function sanitize_choice(...), returns the value if it is present in the set of valid choices; otherwise, returns the default value.

Parameters:

• value (T): The value to be checked.
• valid_choices (Iterable[T]): A collection of valid choices.
• default: The default value to be returned if value is not in valid_choices.

Returns:

• T: The original value if valid, or the default value if not.

---

def sanitize_param(...)

def sanitize_param(value: T, default: T, condition: Callable[[T], bool]) -> T:

The function sanitize_param(...), returns the value if it satisfies the specified condition; otherwise, returns the default value.

Parameters:

• value (T): The value to be checked.
• default (T): The default value to be returned if the condition is not satisfied.
• condition (Callable[[T], bool]): A function that takes a value and returns a boolean, determining if the value is valid.

Returns:

• T: The original value if the condition is satisfied, or the default value if not.

---

def sanitize_seed(...)

def sanitize_seed(seed: Any) -> Optional[int]:

The function sanitize_param(...), returns the seed if it is a non-negative integer; otherwise, returns None.

Parameters:

• seed (Any): The seed value to be validated.

Returns:

• Optional[int]: The original seed if it is a non-negative integer, or None if it is invalid.

---

def sanitize_bounds(...)

def sanitize_bounds(
    bounds: Any, 
    problem_size: int
) -> Dict[str, npt.NDArray[np.float64]]

The function sanitize_bounds(...), validate and normalize feature bounds.

Parameters:

• bounds (Any): he input bounds, which must be either None or a dictionary with 'low' and 'high' keys.
• problem_size (int): The expected length for the normalized bounds lists, corresponding to the number of features in the problem.

Returns:

• Dict[str, list]: Dictionary {'low': [low_1, ..., low_N], 'high': [high_1, ..., high_N]}.