Integer

ConfigSpace.api.types.integer

Integer

Integer(
    name: str,
    bounds: tuple[int, int],
    *,
    distribution: Distribution | None = None,
    default: int | None = None,
    log: bool = False,
    meta: dict | None = None
) -> (
    UniformIntegerHyperparameter
    | NormalIntegerHyperparameter
    | BetaIntegerHyperparameter
)

Create an IntegerHyperparameter.

# Uniformly distributed
Integer("a", (1, 10))
Integer("a", (1, 10), distribution=Uniform())

# Normally distributed at 2 with std 3
Integer("b", (0, 5), distribution=Normal(2, 3))

# Beta distributed with alpha 1 and beta 2
Integer("c", (0, 3), distribution=Beta(1, 2))

# Give it a default value
Integer("a", (1, 10), default=4)

# Sample on a log scale
Integer("a", (1, 100), log=True)

# Add meta info to the param
Integer("a", (1, 10), meta={"use": "For counting chickens"})

Note

Integer is actually a function, please use the corresponding return types if doing an isinstance(param, type) check and not Integer.

PARAMETER	DESCRIPTION
name	The name to give to this hyperparameter TYPE: str
bounds	The bounds to give to the integer. TYPE: tuple[int, int]
distribution	The distribution to use for the hyperparameter. See above TYPE: Distribution | None DEFAULT: None
default	The default value to give to the hyperparameter. TYPE: int | None DEFAULT: None
log	Whether to this parameter lives on a log scale TYPE: bool DEFAULT: False
meta	Any meta information you want to associate with this parameter TYPE: dict | None DEFAULT: None

RETURNS	DESCRIPTION
UniformIntegerHyperparameter | NormalIntegerHyperparameter | BetaIntegerHyperparameter	The corresponding hyperparameter type

Source code in src/ConfigSpace/api/types/integer.py

def Integer(
    name: str,
    bounds: tuple[int, int],
    *,
    distribution: Distribution | None = None,
    default: int | None = None,
    log: bool = False,
    meta: dict | None = None,
) -> (
    UniformIntegerHyperparameter
    | NormalIntegerHyperparameter
    | BetaIntegerHyperparameter
):
    """Create an IntegerHyperparameter.

    ```python
    # Uniformly distributed
    Integer("a", (1, 10))
    Integer("a", (1, 10), distribution=Uniform())

    # Normally distributed at 2 with std 3
    Integer("b", (0, 5), distribution=Normal(2, 3))

    # Beta distributed with alpha 1 and beta 2
    Integer("c", (0, 3), distribution=Beta(1, 2))

    # Give it a default value
    Integer("a", (1, 10), default=4)

    # Sample on a log scale
    Integer("a", (1, 100), log=True)

    # Add meta info to the param
    Integer("a", (1, 10), meta={"use": "For counting chickens"})
    ```

    !!! note

        `Integer` is actually a function, please use the corresponding return types if
        doing an `isinstance(param, type)` check and not `Integer`.

    Args:
        name:
            The name to give to this hyperparameter

        bounds:
            The bounds to give to the integer.

        distribution:
            The distribution to use for the hyperparameter. See above

        default:
            The default value to give to the hyperparameter.

        log:
            Whether to this parameter lives on a log scale

        meta:
            Any meta information you want to associate with this parameter

    Returns:
        The corresponding hyperparameter type
    """
    if distribution is None:
        distribution = Uniform()

    if bounds is None and isinstance(distribution, Uniform):
        raise ValueError("`bounds` must be specifed for Uniform distribution")

    lower, upper = bounds

    if isinstance(distribution, Uniform):
        return UniformIntegerHyperparameter(
            name=name,
            lower=lower,
            upper=upper,
            log=log,
            default_value=default,
            meta=meta,
        )

    if isinstance(distribution, Normal):
        return NormalIntegerHyperparameter(
            name=name,
            lower=lower,
            upper=upper,
            log=log,
            default_value=default,
            meta=meta,
            mu=distribution.mu,
            sigma=distribution.sigma,
        )

    if isinstance(distribution, Beta):
        return BetaIntegerHyperparameter(
            name=name,
            lower=lower,
            upper=upper,
            log=log,
            default_value=default,
            meta=meta,
            alpha=distribution.alpha,
            beta=distribution.beta,
        )

    raise ValueError(f"Unknown distribution type {type(distribution)}")