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Estimator node gradual

此文件是 TPOT 库的一部分。

TPOT 当前版本由 Cedars-Sinai 的以下人员开发： - 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 原始版本主要由宾夕法尼亚大学的以下人员开发： - 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 较宽松通用公共许可证（无论是许可证的第 3 版，还是您选择的任何更高版本）的条款重新分发和/或修改它。

发布 TPOT 的目的是希望它有用，但不提供任何形式的担保；甚至不包括适销性或特定用途适用性的默示担保。更多详细信息请参阅 GNU 较宽松通用公共许可证。

您应该已经随 TPOT 收到了 GNU 较宽松通用公共许可证的副本。如果没有，请参见 https://gnu.ac.cn/licenses/。

EstimatorNodeIndividual_gradual

基类：SklearnIndividual

请注意，ConfigurationSpace 不支持将 None 作为参数。请改用特殊字符串“None”。TPOT 将自动用 Python 的 None 替换此字符串的实例。

参数

名称 类型 描述 默认值
method type

要使用的 estimator 类

 必需
space ConfigurationSpace | dict

要使用的超参数空间。如果传入的是 dict，则超参数是固定的，不会被学习。

 必需
源代码位于 tpot/search_spaces/nodes/estimator_node_gradual.py 
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class EstimatorNodeIndividual_gradual(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
        self.hyperparameters = gradual_hyperparameter_update(params=self.hyperparameters, configspace=self.space, rng=rng)
        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