deephyper.problem.Categorical#

deephyper.problem.Categorical(name: str, items: Sequence[Union[str, int, float]], *, default: T | None = 'None', weights: Sequence[float] | None = 'None', ordered: Literal[False], meta: dict | None = 'None') → ConfigSpace.hyperparameters.CategoricalHyperparameter[source]#
deephyper.problem.Categorical(name: str, items: Sequence[Union[str, int, float]], *, default: T | None = 'None', weights: Sequence[float] | None = 'None', ordered: Literal[True], meta: dict | None = 'None') → ConfigSpace.hyperparameters.OrdinalHyperparameter
deephyper.problem.Categorical(name: str, items: Sequence[Union[str, int, float]], *, default: T | None = 'None', weights: Sequence[float] | None = 'None', ordered: bool = False, meta: dict | None = 'None') → CategoricalHyperparameter | OrdinalHyperparameter

Creates a Categorical Hyperparameter.

CategoricalHyperparameter’s can be used to represent a discrete choice. Optionally, you can specify that these values are also ordered in some manner, e.g. ["small", "medium", "large"].

# A simple categorical hyperparameter
c = Categorical("animals", ["cat", "dog", "mouse"])

# With a default
c = Categorical("animals", ["cat", "dog", "mouse"], default="mouse")

# Make them weighted
c = Categorical("animals", ["cat", "dog", "mouse"], weights=[0.1, 0.8, 3.14])

# Specify it's an OrdinalHyperparameter (ordered categories)
# ... note that you can't apply weights to an Ordinal
o = Categorical("size", ["small", "medium", "large"], ordered=True)

# Add some meta information for your own tracking
c = Categorical("animals", ["cat", "dog", "mouse"], meta={"use": "Favourite Animal"})

Note

Categorical is actually a function, please use the corresponding return types if doing an isinstance(param, type) check with either CategoricalHyperparameter and/or OrdinalHyperparameter.

Parameters

  • name (str) – The name of the hyperparameter

  • items (Sequence[T],) –

    A list of items to put in the category. Note that there are limitations:

    • Can’t use None, use a string “None” instead and convert as required.

    • Can’t have duplicate categories, use weights if required.

  • default (T | None = None) – The default value of the categorical hyperparameter

  • weights (Sequence[float] | None = None) – The weights to apply to each categorical. Each item will be sampled according to these weights.

  • ordered (bool = False) – Whether the categorical is ordered or not. If True, this will return an OrdinalHyperparameter, otherwise it remain a CategoricalHyperparameter.

  • meta (dict | None = None) – Any additional meta information you would like to store along with the hyperparamter.