评估器节点

此文件是 TPOT 库的一部分。

TPOT 当前版本由 Cedars-Sinai 开发，开发者包括：- Pedro Henrique Ribeiro (https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/) - Anil Saini (anil.saini@cshs.org) - Jose Hernandez (jgh9094@gmail.com) - Jay Moran (jay.moran@cshs.org) - Nicholas Matsumoto (nicholas.matsumoto@cshs.org) - Hyunjun Choi (hyunjun.choi@cshs.org) - Gabriel Ketron (gabriel.ketron@cshs.org) - Miguel E. Hernandez (miguel.e.hernandez@cshs.org) - Jason Moore (moorejh28@gmail.com)

TPOT 最初版本主要由宾夕法尼亚大学开发，开发者包括：- Randal S. Olson (rso@randalolson.com) - Weixuan Fu (weixuanf@upenn.edu) - Daniel Angell (dpa34@drexel.edu) - Jason Moore (moorejh28@gmail.com) - 以及许多其他慷慨的开源贡献者

TPOT 是自由软件：您可以根据自由软件基金会发布的 GNU 宽通用公共许可证的条款重新分发和/或修改它，许可证版本为第 3 版，或（由您选择）任何更高版本。

TPOT 的分发是出于希望它能有用，但不提供任何担保；甚至不包括对适销性或特定用途适用性的暗示担保。详情请参阅 GNU 宽通用公共许可证。

您应该随 TPOT 一起收到一份 GNU 宽通用公共许可证的副本。如果没有，请参见 https://gnu.ac.cn/licenses/。

`EstimatorNodeIndividual` ¶

基础：SklearnIndividual

请注意，ConfigurationSpace 不支持 None 作为参数。请改用特殊的字符串“”。TPOT 会自动将此字符串的实例替换为 Python 的 None。

参数

名称	类型	描述	默认值
`method`	`类型`	要使用的评估器的类	必需
`space`	`ConfigurationSpace \| 字典`	要使用的超参数空间。如果传入字典，则超参数是固定的，不会学习。	必需

源代码位于 tpot/search_spaces/nodes/estimator_node.py

class EstimatorNodeIndividual(SklearnIndividual):
    """
    Note that ConfigurationSpace does not support None as a parameter. Instead, use the special string "<NONE>". TPOT will automatically replace instances of this string with the Python None. 

    Parameters
    ----------
    method : type
        The class of the estimator to be used

    space : ConfigurationSpace|dict
        The hyperparameter space to be used. If a dict is passed, hyperparameters are fixed and not learned.

    """
    def __init__(self, method: type, 
                        space: ConfigurationSpace|dict, #TODO If a dict is passed, hyperparameters are fixed and not learned. Is this confusing? Should we make a second node type?
                        hyperparameter_parser: callable = None,
                        rng=None) -> None:
        super().__init__()
        self.method = method
        self.space = space

        if hyperparameter_parser is None:
            self.hyperparameter_parser = default_hyperparameter_parser
        else:
            self.hyperparameter_parser = hyperparameter_parser

        if isinstance(space, dict):
            self.hyperparameters = space
        else:
            rng = np.random.default_rng(rng)
            self.space.seed(rng.integers(0, 2**32))
            self.hyperparameters = dict(self.space.sample_configuration())

    def mutate(self, rng=None):
        if isinstance(self.space, dict): 
            return False

        rng = np.random.default_rng(rng)
        self.space.seed(rng.integers(0, 2**32))
        self.hyperparameters = dict(self.space.sample_configuration())
        return True

    def crossover(self, other, rng=None):
        if isinstance(self.space, dict):
            return False

        rng = np.random.default_rng(rng)
        if self.method != other.method:
            return False

        #loop through hyperparameters, randomly swap items in self.hyperparameters with items in other.hyperparameters
        for hyperparameter in self.space:
            if rng.choice([True, False]):
                if hyperparameter in other.hyperparameters:
                    self.hyperparameters[hyperparameter] = other.hyperparameters[hyperparameter]

        return True



    @final #this method should not be overridden, instead override hyperparameter_parser
    def export_pipeline(self, **kwargs):
        return self.method(**self.hyperparameter_parser(self.hyperparameters))

    def unique_id(self):
        #return a dictionary of the method and the hyperparameters
        method_str = self.method.__name__
        params = list(self.hyperparameters.keys())
        params = sorted(params)

        id_str = f"{method_str}({', '.join([f'{param}={self.hyperparameters[param]}' for param in params])})"

        return id_str

评估器节点

EstimatorNodeIndividual ¶

`EstimatorNodeIndividual` ¶