python-smac-svm实例

最新推荐文章于 2024-09-10 08:41:59 发布

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最新推荐文章于 2024-09-10 08:41:59 发布 · 672 阅读

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本文介绍了如何使用Python的SMAC（Sequential Model-based Algorithm Configuration）库来优化支持向量机（SVM）的参数配置。通过示例代码展示了SMAC如何与SVM结合，以提高模型的预测性能，并探讨了这种方法在机器学习调参中的价值。

import logging
import numpy as np
from sklearn import svm, datasets
from sklearn.model_selection import cross_val_score

# Import ConfigSpace and different types of parameters
from smac.configspace import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
    UniformFloatHyperparameter, UniformIntegerHyperparameter
from ConfigSpace.conditions import InCondition

# Import SMAC-utilities
from smac.tae.execute_func import ExecuteTAFuncDict
from smac.scenario.scenario import Scenario
from smac.facade.smac_facade import SMAC


iris = datasets.load_iris()

def svm_from_cfg(cfg):
    """ Creates a SVM based on a configuration and evaluates it on the
    iris-dataset using cross-validation.

    Parameters:
    -----------
    cfg: Configuration (ConfigSpace.ConfigurationSpace.Configuration)
        Configuration containing the parameters.
        Configurations are indexable!

    Returns:
    --------
    A crossvalidated mean score for the svm on the loaded data-set.
    """
    # For deactivated parameters, the configuration stores None-values.
    # This is not accepted by the SVM, so we remove them.
    cfg = {
   
   k : cfg[k] for k in cfg if cfg[k]}
    # We translate boolean values:
    cfg["shrinking"] = True if cfg["shrinking"]