def __init__(self, fis, params, scoring='neg_root_mean_squared_error', n_jobs=None):
    "\n\n        Args:\n            fis (FIS): The Fuzzy Inference System to tune\n            params (dict of str to list): A mapping from encoded parameters to the list of values to explore.\n            scoring (str): The metric used for scoring. Must be one of sklearn's regression scorings.\n            n_jobs (int): Number of jobs to run in parallel.\n\n        "
    if (GridSearchCV is None):
        raise ModuleNotFoundError('scikit-learn is required for model tuning')
    self.fis = fis
    self.cv = GridSearchCV(ParametrizedFIS(fis), params, scoring=scoring, cv=TrivialSplitter(), refit=False, n_jobs=n_jobs)
Args:
    fis (FIS): The Fuzzy Inference System to tune
    params (dict of str to list): A mapping from encoded parameters to the list of values to explore.
    scoring (str): The metric used for scoring. Must be one of sklearn's regression scorings.
    n_jobs (int): Number of jobs to run in parallel.
def __init__(self, fis, params, scoring='neg_root_mean_squared_error', n_jobs=None):
    "\n\n        Args:\n            fis (FIS): The Fuzzy Inference System to tune\n            params (dict of str to list): A mapping from encoded parameters to the list of values to explore.\n            scoring (str): The metric used for scoring. Must be one of sklearn's regression scorings.\n            n_jobs (int): Number of jobs to run in parallel.\n\n        "
    if (GridSearchCV is None):
        raise ModuleNotFoundError('scikit-learn is required for model tuning')
    self.fis = fis
    self.cv = GridSearchCV(ParametrizedFIS(fis), params, scoring=scoring, cv=TrivialSplitter(), refit=False, n_jobs=n_jobs)