import ConfigSpace

from sklearnbot.config_spaces import ConfigSpaceWrapper


def get_hyperparameter_search_space(seed) -> ConfigSpaceWrapper:
    """
    The Bernoulli NB configuration space based on the search space from
    auto-sklearn:
    https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/bernoulli_nb.py

    Parameters
    ----------
    seed: int
        Random seed that will be used to sample random configurations

    Returns
    -------
    cs: ConfigSpace.ConfigurationSpace
        The configuration space object
    """
    cs = ConfigSpace.ConfigurationSpace('sklearn.naive_bayes.BernoulliNB', seed)

    # the smoothing parameter is a non-negative float
    # I will limit it to 1000 and put it on a logarithmic scale. (SF)
    # Please adjust that, if you know a proper range, this is just a guess.
    alpha = ConfigSpace.UniformFloatHyperparameter(
        name='alpha', lower=1e-2, upper=100, default_value=1, log=True)
    fit_prior = ConfigSpace.CategoricalHyperparameter(
        name='fit_prior', choices=[True, False], default_value=True)

    hyperparameters = [alpha, fit_prior]

    return ConfigSpaceWrapper(cs, hyperparameters, None)

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def get_hyperparameter_search_space(seed) ->ConfigSpaceWrapper:
    """
    The Bernoulli NB configuration space based on the search space from
    auto-sklearn:
    https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/bernoulli_nb.py

    Parameters
    ----------
    seed: int
        Random seed that will be used to sample random configurations

    Returns
    -------
    cs: ConfigSpace.ConfigurationSpace
        The configuration space object
    """
    cs = ConfigSpace.ConfigurationSpace('sklearn.naive_bayes.BernoulliNB', seed
        )
    alpha = ConfigSpace.UniformFloatHyperparameter(name='alpha', lower=0.01,
        upper=100, default_value=1, log=True)
    fit_prior = ConfigSpace.CategoricalHyperparameter(name='fit_prior',
        choices=[True, False], default_value=True)
    hyperparameters = [alpha, fit_prior]
    return ConfigSpaceWrapper(cs, hyperparameters, None)

import ConfigSpace
from sklearnbot.config_spaces import ConfigSpaceWrapper


def get_hyperparameter_search_space(seed) ->ConfigSpaceWrapper:
    """
    The Bernoulli NB configuration space based on the search space from
    auto-sklearn:
    https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/bernoulli_nb.py

    Parameters
    ----------
    seed: int
        Random seed that will be used to sample random configurations

    Returns
    -------
    cs: ConfigSpace.ConfigurationSpace
        The configuration space object
    """
    cs = ConfigSpace.ConfigurationSpace('sklearn.naive_bayes.BernoulliNB', seed
        )
    alpha = ConfigSpace.UniformFloatHyperparameter(name='alpha', lower=0.01,
        upper=100, default_value=1, log=True)
    fit_prior = ConfigSpace.CategoricalHyperparameter(name='fit_prior',
        choices=[True, False], default_value=True)
    hyperparameters = [alpha, fit_prior]
    return ConfigSpaceWrapper(cs, hyperparameters, None)

import ConfigSpace

from sklearnbot.config_spaces import ConfigSpaceWrapper


def get_hyperparameter_search_space(seed) -> ConfigSpaceWrapper:
    """
    The Bernoulli NB configuration space based on the search space from
    auto-sklearn:
    https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/bernoulli_nb.py

    Parameters
    ----------
    seed: int
        Random seed that will be used to sample random configurations

    Returns
    -------
    cs: ConfigSpace.ConfigurationSpace
        The configuration space object
    """
    cs = ConfigSpace.ConfigurationSpace('sklearn.naive_bayes.BernoulliNB', seed)

    # the smoothing parameter is a non-negative float
    # I will limit it to 1000 and put it on a logarithmic scale. (SF)
    # Please adjust that, if you know a proper range, this is just a guess.
    alpha = ConfigSpace.UniformFloatHyperparameter(
        name='alpha', lower=1e-2, upper=100, default_value=1, log=True)
    fit_prior = ConfigSpace.CategoricalHyperparameter(
        name='fit_prior', choices=[True, False], default_value=True)

    hyperparameters = [alpha, fit_prior]

    return ConfigSpaceWrapper(cs, hyperparameters, None)
