def parse_model_config_params(model_params, num_settings, random_state):
    '\n\n    Args:\n        model_params:\n        num_settings:\n        random_state:\n\n    Returns:\n\n    '
    param_distributions = dict()
    dist_types = dict(randint=randint, expon=expon, uniform=uniform)
    for (param, param_value) in model_params.items():
        if (param_value[0] in ['randint', 'expon', 'uniform']):
            param_distributions[param] = dist_types[param_value[0]](*param_value[1:])
        else:
            param_distributions[param] = param_value
    return sampler_generator(ParameterSampler(param_distributions, n_iter=num_settings, random_state=random_state))
Args:
    model_params:
    num_settings:
    random_state:

Returns:
def parse_model_config_params(model_params, num_settings, random_state):
    '\n\n    Args:\n        model_params:\n        num_settings:\n        random_state:\n\n    Returns:\n\n    '
    param_distributions = dict()
    dist_types = dict(randint=randint, expon=expon, uniform=uniform)
    for (param, param_value) in model_params.items():
        if (param_value[0] in ['randint', 'expon', 'uniform']):
            param_distributions[param] = dist_types[param_value[0]](*param_value[1:])
        else:
            param_distributions[param] = param_value
    return sampler_generator(ParameterSampler(param_distributions, n_iter=num_settings, random_state=random_state))