Source code for deephyper.problem._hyperparameter

import copy
import json

import ConfigSpace as cs
import ConfigSpace.hyperparameters as csh
import numpy as np
from ConfigSpace.read_and_write import json as cs_json

import deephyper.core.exceptions as dh_exceptions
import deephyper.skopt

def convert_to_skopt_dim(cs_hp, surrogate_model=None):
    if surrogate_model in ["RF", "ET", "GBRT", "HGBRT", "MF", "BT"]:
        # models not sensitive to the metric space such as trees
        surrogate_model_type = "rule_based"
        # models sensitive to the metric space such as GP, neural networks
        surrogate_model_type = "distance_based"

    if isinstance(cs_hp, csh.UniformIntegerHyperparameter):
        skopt_dim =
            prior="log-uniform" if cs_hp.log else "uniform",
    elif isinstance(cs_hp, csh.UniformFloatHyperparameter):
        skopt_dim =
            prior="log-uniform" if cs_hp.log else "uniform",
    elif isinstance(cs_hp, csh.CategoricalHyperparameter):
        # the transform is important if we don't want the complexity of trees
        # to explode with categorical variables
        skopt_dim =
            transform="onehot" if surrogate_model_type == "distance_based" else "label",
    elif isinstance(cs_hp, csh.OrdinalHyperparameter):
        categories = list(cs_hp.sequence)
        if all(
            isinstance(x, (int, np.integer)) or isinstance(x, (float, np.floating))
            for x in categories
            transform = "identity"
            transform = "label"
        skopt_dim =
            categories=categories,, transform=transform
    elif isinstance(cs_hp, csh.Constant):
        categories = [cs_hp.value]
        skopt_dim =
            categories=categories,, transform="label"
        raise TypeError(f"Cannot convert hyperparameter of type {type(cs_hp)}")

    return skopt_dim

def convert_to_skopt_space(cs_space, surrogate_model=None):
    """Convert a ConfigurationSpace to a scikit-optimize Space.

        cs_space (ConfigurationSpace): the ``ConfigurationSpace`` to convert.
        surrogate_model (str, optional): the type of surrogate model/base estimator used to perform Bayesian optimization. Defaults to None.

        TypeError: if the input space is not a ConfigurationSpace.

    Returns: a scikit-optimize Space.

    # verify pre-conditions
    if not (isinstance(cs_space, cs.ConfigurationSpace)):
        raise TypeError("Input space should be of type ConfigurationSpace")

    sample_with_config_space = (
        len(cs_space.get_conditions()) > 0 or len(cs_space.get_forbiddens()) > 0

    # convert the ConfigSpace to
    dimensions = []
    for hp in cs_space.get_hyperparameters():
        dimensions.append(convert_to_skopt_dim(hp, surrogate_model))

    skopt_space =
        dimensions, config_space=cs_space if sample_with_config_space else None
    return skopt_space

def check_hyperparameter(parameter, name=None, default_value=None):
    """Check if the passed parameter is a valid description of an hyperparameter.

    :meta private:

        parameter (str|Hyperparameter): an instance of ``ConfigSpace.hyperparameters.hyperparameter`` or a synthetic description (e.g., ``list``, ``tuple``).
        parameter (str): the name of the hyperparameter. Only required when the parameter is not a ``ConfigSpace.hyperparameters.hyperparameter``.
        default_value: a default value for the hyperparameter.

        Hyperparameter: the ConfigSpace hyperparameter instance corresponding to the ``parameter`` description.
    if isinstance(parameter, csh.Hyperparameter):
        return parameter

    if not isinstance(parameter, (list, tuple, np.ndarray, dict)):
        raise ValueError(
            "Shortcut definition of an hyper-parameter has to be a list, tuple, array or dict."

    if not (type(name) is str):
        raise ValueError("The 'name' of an hyper-parameter should be a string!")

    kwargs = {}
    if default_value is not None:
        kwargs["default_value"] = default_value

    if type(parameter) is tuple:  # Range of reals or integers
        if len(parameter) == 2:
            prior = "uniform"
        elif len(parameter) == 3:
            prior = parameter[2]
            assert prior in [
            ], f"Prior has to be 'uniform' or 'log-uniform' when {prior} was given for parameter '{name}'"
            parameter = parameter[:2]

        log = prior == "log-uniform"

        if all([isinstance(p, int) for p in parameter]):
            return csh.UniformIntegerHyperparameter(
                name=name, lower=parameter[0], upper=parameter[1], log=log, **kwargs
        elif any([isinstance(p, float) for p in parameter]):
            return csh.UniformFloatHyperparameter(
                name=name, lower=parameter[0], upper=parameter[1], log=log, **kwargs
    elif type(parameter) is list:  # Categorical
        if any(
            [isinstance(p, (str, bool)) or isinstance(p, np.bool_) for p in parameter]
            return csh.CategoricalHyperparameter(name, choices=parameter, **kwargs)
        elif all([isinstance(p, (int, float)) for p in parameter]):
            return csh.OrdinalHyperparameter(name, sequence=parameter, **kwargs)
    elif type(parameter) is dict:  # Integer or Real distribution
        # Normal
        if "mu" in parameter and "sigma" in parameter:
            if type(parameter["mu"]) is float:
                return csh.NormalFloatHyperparameter(name=name, **parameter, **kwargs)
            elif type(parameter["mu"]) is int:
                return csh.NormalIntegerHyperparameter(name=name, **parameter, **kwargs)
                raise ValueError(
                    "Wrong hyperparameter definition! 'mu' should be either a float or an integer."

    raise ValueError(
        f"Invalid dimension {name}: {parameter}. Read the documentation for"
        f" supported types."

[docs]class HpProblem: """Class to define an hyperparameter problem. >>> from deephyper.problem import HpProblem >>> problem = HpProblem() Args: config_space (ConfigurationSpace, optional): In case the ``HpProblem`` is defined from a `ConfigurationSpace`. """ def __init__(self, config_space=None): if config_space is not None and not ( isinstance(config_space, cs.ConfigurationSpace) ): raise ValueError( "Parameter 'config_space' should be an instance of ConfigurationSpace!" ) if config_space: self._space = copy.deepcopy(config_space) else: self._space = cs.ConfigurationSpace() self.references = [] # starting points def __str__(self): return repr(self) def __repr__(self): prob = repr(self._space) return prob def __len__(self): return len(self.hyperparameter_names)
[docs] def add_hyperparameter( self, value, name: str = None, default_value=None ) -> csh.Hyperparameter: """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) Args: 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: ConfigSpace.Hyperparameter: a ConfigSpace ``Hyperparameter`` object corresponding to the ``(value, name, default_value)``. """ if not (type(name) is str or name is None): raise dh_exceptions.problem.SpaceDimNameOfWrongType(name) csh_parameter = check_hyperparameter(value, name, default_value=default_value) self._space.add_hyperparameter(csh_parameter) return csh_parameter
[docs] def add_hyperparameters(self, hp_list): """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``. Args: hp_list (ConfigSpace.Hyperparameter): a list of ConfigSpace hyperparameters. Returns: list: The list of added hyperparameters. """ return [self.add_hyperparameter(hp) for hp in hp_list]
[docs] def add_forbidden_clause(self, clause): """Add a `forbidden clause <>`_ to the ``HpProblem``. For example if we want to optimize :math:`\\frac{1}{x}` where :math:`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)) Args: clause: a ConfigSpace forbidden clause. """ self._space.add_forbidden_clause(clause)
[docs] def add_condition(self, condition): """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)) Args: condition: A ConfigSpace condition. """ self._space.add_condition(condition)
[docs] def add_conditions(self, conditions: list) -> None: """Add a list of `condition <>`_ to the ``HpProblem``. Args: conditions (list): A list of ConfigSpace conditions. """ self._space.add_conditions(conditions)
@property def space(self): """The wrapped ConfigSpace object.""" return self._space @property def hyperparameter_names(self): """The list of hyperparameters names.""" return self._space.get_hyperparameter_names()
[docs] def check_configuration(self, parameters: dict): """Check if a configuration is valid. Raise an error if not.""" config = cs.Configuration(self._space, parameters) self._space.check_configuration(config)
@property def default_configuration(self): """The default configuration as a dictionnary.""" config = self._space.get_default_configuration().get_dictionary() return config
[docs] def to_json(self): """Returns a dict version of the space which can be saved as JSON.""" json_format = json.loads(cs_json.write(self._space)) return json_format