使用 Hyperopt 进行参数调优

最新推荐文章于 2023-08-07 23:00:47 发布

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最新推荐文章于 2023-08-07 23:00:47 发布 · 1.2k 阅读

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CC 4.0 BY-SA版权

这篇博客介绍了如何使用 Hyperopt 工具进行参数调优，通过一系列实验展示了不同参数组合对模型性能的影响，最终找到了最优的参数设置，包括 n_estimators、x_colsample_bytree、x_learning_rate、x_max_depth 和 x_reg_alpha。

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def objective(space):
    
    model = xgb.XGBRegressor(
        max_depth = int(space['max_depth']),
        n_estimators = int(space['n_estimators']),
        subsample = space['subsample'],
        colsample_bytree = space['colsample_bytree'],
        learning_rate = space['learning_rate'],
        reg_alpha = space['reg_alpha']
    )

    X_train, X_test, y_train, y_test = train_test_split(train, 'count')
    eval_set  = [( X_train, y_train), ( X_test, y_test)]


    (_, registered_pred) = fit_and_predict(train, model, 'registered_log')
    (_, casual_pred) = fit_and_predict(train, model, 'casual_log')
   
    y_test = train[train.is_test == True]['count']
    y_pred = (np.exp2(registered_pred) - 1) + (np.exp2(casual_pred) -1)
    
    score = rmsle(y_test, y_pred)
    print ("SCORE:", score)

    return{'loss':score, 'status': STATUS_OK }

space ={
    'max_depth': hp.quniform("x_max_depth", 2, 20, 1),
    'n_estimators': hp.quniform(