Skip to content

联合

该文件是TPOT库的一部分。

当前版本的TPOT是由以下人员在Cedars-Sinai开发的： - 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) - Miguel E. Hernandez (miguel.e.hernandez@cshs.org) - Jason Moore (moorejh28@gmail.com)

TPOT的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 是免费软件：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，许可证的版本可以是第 3 版，或者（根据您的选择）任何以后的版本。

TPOT 的发布是希望它能有用， 但没有任何保证；甚至没有对 适销性或特定用途适用性的暗示保证。更多详情请参阅 GNU 较宽松通用公共许可证。

您应该已经收到了一份GNU较宽松通用公共许可证的副本，随TPOT一起提供。如果没有，请参见http://www.gnu.org/licenses/。

UnionPipeline

基类: SearchSpace

Source code in tpot2/search_spaces/pipelines/union.py 
106
107
108
109
110
111
112
113
114
115
116
class UnionPipeline(SearchSpace):
    def __init__(self, search_spaces : List[SearchSpace] ) -> None:
        """
        Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.
        """

        self.search_spaces = search_spaces

    def generate(self, rng=None):
        rng = np.random.default_rng(rng)
        return UnionPipelineIndividual(self.search_spaces, rng=rng)

__init__(search_spaces)

接收一个搜索空间列表。将生成一个顺序长度的管道。管道中的每一步将对应于相同索引中提供的搜索空间。

Source code in tpot2/search_spaces/pipelines/union.py 
107
108
109
110
111
112
def __init__(self, search_spaces : List[SearchSpace] ) -> None:
    """
    接受一个搜索空间列表。将生成一个顺序长度的管道。管道中的每一步将对应于提供的相同索引中的搜索空间。
    """

    self.search_spaces = search_spaces

UnionPipelineIndividual

基类：SklearnIndividual

接收一个搜索空间列表。每个空间是一个SearchSpaces列表。 将生成一个FeatureUnion管道。管道中的每个步骤将对应于相同索引中提供的搜索空间。 生成的管道将是管道中步骤的FeatureUnion。

Source code in tpot2/search_spaces/pipelines/union.py 
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
class UnionPipelineIndividual(SklearnIndividual):
    """
    Takes in a list of search spaces. each space is a list of SearchSpaces.
    Will produce a FeatureUnion pipeline. Each step in the pipeline will correspond to the the search space provided in the same index.
    The resulting pipeline will be a FeatureUnion of the steps in the pipeline.

    """

    def __init__(self, search_spaces : List[SearchSpace], rng=None) -> None:
        super().__init__()
        self.search_spaces = search_spaces

        self.pipeline = []
        for space in self.search_spaces:
            self.pipeline.append(space.generate(rng))

    def mutate(self, rng=None):
        rng = np.random.default_rng(rng)
        step = rng.choice(self.pipeline)
        return step.mutate(rng)


    def crossover(self, other, rng=None):
        #swap a random step in the pipeline with the corresponding step in the other pipeline
        rng = np.random.default_rng(rng)

        cx_funcs = [self._crossover_node, self._crossover_swap_node]
        rng.shuffle(cx_funcs)
        for cx_func in cx_funcs:
            if cx_func(other, rng):
                return True

        return False

    def _crossover_swap_node(self, other, rng):
        rng = np.random.default_rng(rng)
        idx = rng.integers(1,len(self.pipeline))

        self.pipeline[idx], other.pipeline[idx] = other.pipeline[idx], self.pipeline[idx]
        return True

    def _crossover_node(self, other, rng):
        rng = np.random.default_rng(rng)

        crossover_success = False
        for idx in range(len(self.pipeline)):
            if rng.random() < 0.5:
                if self.pipeline[idx].crossover(other.pipeline[idx], rng):
                    crossover_success = True

        return crossover_success

    def export_pipeline(self, **kwargs):
        return sklearn.pipeline.make_union(*[step.export_pipeline(**kwargs) for step in self.pipeline])

    def unique_id(self):
        l = [step.unique_id() for step in self.pipeline]
        l = ["FeatureUnion"] + l
        return TupleIndex(tuple(l))