deephyper.problem.HpProblem#

class deephyper.problem.HpProblem(config_space=None)[source]#

Bases: object

Class to define an hyperparameter problem.

>>> from deephyper.problem import HpProblem
>>> problem = HpProblem()

Parameters: config_space (ConfigurationSpace, optional) – In case the HpProblem is defined from a ConfigurationSpace.

Methods

`add_condition`	Add a condition to the `HpProblem`.
`add_conditions`	Add a list of condition to the `HpProblem`.
`add_forbidden_clause`	Add a forbidden clause to the `HpProblem`.
`add_hyperparameter`	Add an hyperparameter to the `HpProblem`.
`add_hyperparameters`	Add a list of hyperparameters.
`check_configuration`	Check if a configuration is valid.
`to_json`	Returns a dict version of the space which can be saved as JSON.

Attributes

`default_configuration`	The default configuration as a dictionnary.
`hyperparameter_names`	The list of hyperparameters names.
`space`	The wrapped ConfigSpace object.

add_condition(condition)[source]#

Add a condition to the HpProblem.

>>> from deephyper.problem import HpProblem
>>> import ConfigSpace as cs
>>> problem = HpProblem()
>>> x = problem.add_hyperparameter((0.0, 10.0), "x")
>>> y = problem.add_hyperparameter((1e-4, 1.0), "y")
>>> problem.add_condition(cs.LessThanCondition(y, x, 1.0))

Parameters: condition – A ConfigSpace condition.

add_conditions(conditions: list) → None [source]#

Add a list of condition to the HpProblem.

Parameters: conditions (list) – A list of ConfigSpace conditions.

add_forbidden_clause(clause)[source]#

Add a forbidden clause to the HpProblem.

For example if we want to optimize \(\frac{1}{x}\) where \(x\) cannot be equal to 0:

>>> from deephyper.problem import HpProblem
>>> import ConfigSpace as cs
>>> problem = HpProblem()
>>> x = problem.add_hyperparameter((0.0, 10.0), "x")
>>> problem.add_forbidden_clause(cs.ForbiddenEqualsClause(x, 0.0))

Parameters: clause – a ConfigSpace forbidden clause.

add_hyperparameter(value, name: Optional[str] = None, default_value=None) → ConfigSpace.hyperparameters.Hyperparameter[source]#

Add an hyperparameter to the HpProblem.

Hyperparameters can be added to a HpProblem with a short syntax:

>>> problem.add_hyperparameter((0, 10), "discrete", default_value=5)
>>> problem.add_hyperparameter((0.0, 10.0), "real", default_value=5.0)
>>> problem.add_hyperparameter([0, 10], "categorical", default_value=0)

Sampling distributions can be provided:

>>> problem.add_hyperparameter((0.0, 10.0, "log-uniform"), "real", default_value=5.0)

It is also possible to use ConfigSpace Hyperparameters:

>>> import ConfigSpace.hyperparameters as csh
>>> csh_hp = csh.UniformIntegerHyperparameter(
...     name='uni_int', lower=10, upper=100, log=False)
>>> problem.add_hyperparameter(csh_hp)

Parameters

value (tuple or list or ConfigSpace.Hyperparameter) – a valid hyperparametr description.
name (str) – The name of the hyperparameter to add.
default_value (float or int or str) – A default value for the corresponding hyperparameter.

Returns

a ConfigSpace Hyperparameter object corresponding to the (value, name, default_value).

Return type

ConfigSpace.Hyperparameter

add_hyperparameters(hp_list)[source]#

Add a list of hyperparameters. It can be useful when a list of ConfigSpace.Hyperparameter are defined and we need to add them to the HpProblem.

Parameters: hp_list (ConfigSpace.Hyperparameter) – a list of ConfigSpace hyperparameters.
Returns: The list of added hyperparameters.
Return type: list

check_configuration(parameters: dict)[source]#: Check if a configuration is valid. Raise an error if not.

property default_configuration#: The default configuration as a dictionnary.

property hyperparameter_names#: The list of hyperparameters names.

property space#: The wrapped ConfigSpace object.

to_json()[source]#: Returns a dict version of the space which can be saved as JSON.