python-smac-randomforest自动化调参

最新推荐文章于 2024-12-02 10:42:41 发布

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最新推荐文章于 2024-12-02 10:42:41 发布 · 845 阅读

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本文介绍了如何使用Python中的SMAC（Sequential Model-based Algorithm Configuration）库与RandomForest算法相结合，进行高效的参数调优。通过SMAC的智能搜索策略，可以自动化地寻找RandomForest的最佳超参数配置，从而提升模型的预测性能。

import logging
import os
import inspect
import numpy as np
from sklearn.metrics import make_scorer
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_boston

from smac.configspace import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
    UniformFloatHyperparameter, UniformIntegerHyperparameter

from smac.tae.execute_func import ExecuteTAFuncDict
from smac.scenario.scenario import Scenario
from smac.facade.smac_facade import SMAC
# 导入数据
boston = load_boston()

def rf_from_cfg(cfg, seed):
    """
        Creates a random forest regressor from sklearn and fits the given data on it.
        This is the function-call we try to optimize. Chosen values are stored in
        the configuration (cfg).

        Parameters:
        -----------
        cfg: Configuration
            configuration chosen by smac
        seed: int or RandomState
            used to initialize the rf's random generator

        Returns:
        -----------
        np.mean(rmses): float
            mean of root mean square errors of random-forest test predictions
            per cv-fold
    """
    rfr = RandomForestRegressor(
        n_estimators=cfg["num_trees"],
        criterion=cfg["criterion"],
        min_samples_split=cfg