python-autosklearn-MLPClassifier_mlpclassifier函数-优快云博客

本文介绍如何利用Python库AutoSklearn自动化构建多层感知器（MLPClassifier）模型，通过自动调参来优化模型性能。内容包括AutoSklearn的安装、配置以及在分类任务中的应用实例。

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from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
    UniformIntegerHyperparameter, UniformFloatHyperparameter

import sklearn.metrics
import autosklearn.classification
import autosklearn.pipeline.components.classification
from autosklearn.pipeline.components.base \
    import AutoSklearnClassificationAlgorithm
from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA, \
    PREDICTIONS


# Create MLP classifier component for auto-sklearn.
class MLPClassifier(AutoSklearnClassificationAlgorithm):
    def __init__(self,
                 hidden_layer_depth,
                 num_nodes_per_layer,
                 activation,
                 alpha,
                 solver,
                 random_state=None,
                 ):
        # 神经网络的隐藏层的深度
        self.hidden_layer_depth = hidden_layer_depth
        # 每一层的节点数
        self.num_nodes_per_layer = num_nodes_per_layer
        # 激活函数
        self.activation = activation
        # 学习率
        self.alpha = alpha
        # 优化器
        self.solver = solver
        # 随机种子
        self.random_state = random_state

    def fit(self, X, y):
        self.num_nodes_per_layer = int(sel