deephyper.problem.Integer#

deephyper.problem.Integer(name: str, bounds: tuple[int, int] | None = None, *, distribution: Uniform | None = None, default: int | None = None, q: int | None = None, log: bool = False, meta: dict | None = None) → ConfigSpace.hyperparameters.UniformIntegerHyperparameter[source]#

deephyper.problem.Integer(name: str, bounds: tuple[int, int] | None = None, *, distribution: ConfigSpace.api.distributions.Normal, default: int | None = None, q: int | None = None, log: bool = False, meta: dict | None = None) → ConfigSpace.hyperparameters.NormalIntegerHyperparameter

deephyper.problem.Integer(name: str, bounds: tuple[int, int] | None = None, *, distribution: ConfigSpace.api.distributions.Beta, default: int | None = None, q: int | None = None, log: bool = False, meta: dict | None = None) → ConfigSpace.hyperparameters.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", distribution=Normal(2, 3))
Integer("b", (0, 5), distribution=Normal(2, 3))  # ... bounded

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

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

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

# Quantized into three brackets
Integer("a", (1, 10), q=3)

# 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.

Parameters

name (str) – The name to give to this hyperparameter
bounds (tuple[int, int] | None = None) – The bounds to give to the integer. Note that by default, this is required for Uniform distribution, which is the default distribution
distribution (Uniform | Normal | Beta, = Uniform) – The distribution to use for the hyperparameter. See above
default (int | None = None) – The default value to give to the hyperparameter.
q (int | None = None) –
The quantization factor, must evenly divide the boundaries. Sampled values will be
```
    full range
1    4    7    10
|--------------|
|    |    |    |  q = 3
```
All samples here will then be in {1, 4, 7, 10}
Note

Quantization points act are not equal and require experimentation to be certain about
- https://github.com/automl/ConfigSpace/issues/264
log (bool = False) – Whether to this parameter lives on a log scale
meta (dict | None = None) – Any meta information you want to associate with this parameter

Returns

Returns the corresponding hyperparameter type

Return type

UniformIntegerHyperparameter | NormalIntegerHyperparameter | BetaIntegerHyperparameter