sklearn_api.py 文件源码

def __init__(self, operators=None, n_const=0, n_rows=1, n_columns=3, n_back=1, max_iter=1000,
                 max_nfev=10000, lambda_=4, f_tol=0, seed=None, random_state=None, n_jobs=1, metric=mean_squared_error):
        """
        :param operators: list of primitive excluding terminals
        :param n_const: number of symbolic constants
        :param n_rows: number of rows in the code block
        :param n_columns: number of columns in the code block
        :param n_back: number of rows to look back for connections
        :param metric: what to optimize for
        :param fun: `callable(individual)`, function to be optimized
        :param random_state: an instance of np.random.RandomState, a seed integer or None
        :param cls: The base class for individuals
        :type cls: (optional) instance of cartesian.cgp.Cartesian
        :param seed: (optional) can be passed instead of cls.
        :param lambda_: number of offspring per generation
        :param max_iter: maximum number of generations
        :param max_nfev: maximum number of function evaluations. Important, if fun is another optimizer
        :param f_tol: threshold for precision
        :param n_jobs: number of jobs for joblib embarrassingly easy parallel
        """
        self.operators = DEFAULT_PRIMITIVES or operators
        self.constants = [Constant("c_{}".format(i)) for i in range(n_const)]
        self.n_rows = n_rows
        self.n_back = n_back
        self.n_columns = n_columns
        self.n_out = None
        self.pset = None
        self.res = None
        self.model = None

        # parameters for algorithm
        self.max_nfev = max_nfev
        self.max_iter = max_iter
        self.lambda_ = lambda_
        self.f_tol = f_tol
        self.metric = metric
        self.random_state = check_random_state(random_state)
        self.n_jobs = n_jobs
        self.seed = seed