def create_recommendation(self, rand_prob=0.0, n_models=5, description='', acq_func='expected_improvement', integrate_acq=True):
    'Get a recommendation for a point to evaluate next.\n\n        The create recommendation utility represents the core of the Thor\n        Bayesian optimization software. This function will contact the Thor\n        server and request a new configuration of machine learning parameters\n        that serve the object of maximizing the metric of interest.\n\n        Parameters:\n            rand_prob (optional, float): This parameter represents that a random\n                point in the input space is chosen instead of selecting a\n                configuration of parameters using Bayesian optimization. As\n                such, this parameter can be used to benchmark against random\n                search and otherwise to perform pure exploration of the\n                parameter space.\n            n_models (optional, int): The number of Gaussian process models to\n                sample using elliptical slice sampling. Setting this to a large\n                number will produce a better characterization of uncertainty in\n                the acquisition function.\n            description (optional, str): An optional per-observation\n                descriptor, potentially useful for identifying one observation\n                among many others in a large experiment.  Defaults to "".\n            acq_func (optional, str): A string specifying which acquisition\n                function should be used to construct the newest recommendation.\n                It can be useful to sometimes vary the acquisition function to\n                enable exploitation towards the end of an experiment.\n            integrate_acq (optional, bool): An indicator for whether or not we\n                should construct an integrated acquisition function using models\n                sampled from the posterior. The alternative is to not integrate\n                and to return a single recommendation for each of the sampled\n                models, of which there are `n_models`.\n\n        Returns:\n            RecommendationClient: A recommendation client object\n                corresponding to the recommended set of parameters. If the\n                acquisition function is not integrated, a list of\n                RecommendationClient objects may be returned instead, one for\n                each sampled model.\n        '
    post_data = {'auth_token': self.auth_token, 'experiment_id': self.experiment_id, 'n_models': n_models, 'rand_prob': rand_prob, 'description': description, 'acq_func': acq_func, 'integrate_acq': integrate_acq}
    result = requests.post(url=self.base_url.format('create_recommendation'), json=post_data)
    recs = json_parser(result, self.auth_token, RecommendationClient)
    return (recs[0] if (len(recs) == 1) else recs)
Get a recommendation for a point to evaluate next.

The create recommendation utility represents the core of the Thor
Bayesian optimization software. This function will contact the Thor
server and request a new configuration of machine learning parameters
that serve the object of maximizing the metric of interest.

Parameters:
    rand_prob (optional, float): This parameter represents that a random
        point in the input space is chosen instead of selecting a
        configuration of parameters using Bayesian optimization. As
        such, this parameter can be used to benchmark against random
        search and otherwise to perform pure exploration of the
        parameter space.
    n_models (optional, int): The number of Gaussian process models to
        sample using elliptical slice sampling. Setting this to a large
        number will produce a better characterization of uncertainty in
        the acquisition function.
    description (optional, str): An optional per-observation
        descriptor, potentially useful for identifying one observation
        among many others in a large experiment.  Defaults to "".
    acq_func (optional, str): A string specifying which acquisition
        function should be used to construct the newest recommendation.
        It can be useful to sometimes vary the acquisition function to
        enable exploitation towards the end of an experiment.
    integrate_acq (optional, bool): An indicator for whether or not we
        should construct an integrated acquisition function using models
        sampled from the posterior. The alternative is to not integrate
        and to return a single recommendation for each of the sampled
        models, of which there are `n_models`.

Returns:
    RecommendationClient: A recommendation client object
        corresponding to the recommended set of parameters. If the
        acquisition function is not integrated, a list of
        RecommendationClient objects may be returned instead, one for
        each sampled model.
def create_recommendation(self, rand_prob=0.0, n_models=5, description=, acq_func='expected_improvement', integrate_acq=True):
    'Get a recommendation for a point to evaluate next.\n\n        The create recommendation utility represents the core of the Thor\n        Bayesian optimization software. This function will contact the Thor\n        server and request a new configuration of machine learning parameters\n        that serve the object of maximizing the metric of interest.\n\n        Parameters:\n            rand_prob (optional, float): This parameter represents that a random\n                point in the input space is chosen instead of selecting a\n                configuration of parameters using Bayesian optimization. As\n                such, this parameter can be used to benchmark against random\n                search and otherwise to perform pure exploration of the\n                parameter space.\n            n_models (optional, int): The number of Gaussian process models to\n                sample using elliptical slice sampling. Setting this to a large\n                number will produce a better characterization of uncertainty in\n                the acquisition function.\n            description (optional, str): An optional per-observation\n                descriptor, potentially useful for identifying one observation\n                among many others in a large experiment.  Defaults to .\n            acq_func (optional, str): A string specifying which acquisition\n                function should be used to construct the newest recommendation.\n                It can be useful to sometimes vary the acquisition function to\n                enable exploitation towards the end of an experiment.\n            integrate_acq (optional, bool): An indicator for whether or not we\n                should construct an integrated acquisition function using models\n                sampled from the posterior. The alternative is to not integrate\n                and to return a single recommendation for each of the sampled\n                models, of which there are `n_models`.\n\n        Returns:\n            RecommendationClient: A recommendation client object\n                corresponding to the recommended set of parameters. If the\n                acquisition function is not integrated, a list of\n                RecommendationClient objects may be returned instead, one for\n                each sampled model.\n        '
    post_data = {'auth_token': self.auth_token, 'experiment_id': self.experiment_id, 'n_models': n_models, 'rand_prob': rand_prob, 'description': description, 'acq_func': acq_func, 'integrate_acq': integrate_acq}
    result = requests.post(url=self.base_url.format('create_recommendation'), json=post_data)
    recs = json_parser(result, self.auth_token, RecommendationClient)
    return (recs[0] if (len(recs) == 1) else recs)