Xgboost模型在机器学习、深度学习中经久不衰，不论是分类还是回归任务都是一个不错的baseline甚至最终可用的模型，XGB对任务的普适性也决定了其具有大量的可调节参数，针对同一个任务，不同的参数设置可能带来不同甚至相差甚远的性能结果，因为寻找当前任务下可用、有效的参数是一个必不可少的过程，在上一篇文章XGB系列-XGB参数指南_wwlsm_zql的博客-CSDN博客在运行 XGBoost 之前，我们必须设置三种类型的参数: 通用参数、提升参数和任务参数。本文提供了对XGB模型的全部参数的介绍，用于指导对参数的选择https://blog.csdn.net/wwlsm_zql/article/details/126192959介绍了XGB的所有参数，针对如果繁多的参数，试探枚举是一个非常庞大的工作量，因此本文介绍通过hyperopt实现自动参数寻优，找到适合自己任务的最佳参数。

代码链接：colab代码https://colab.research.google.com/drive/1dm3Bk0VlEuBed8FMMeoZGWUR8xY84Ho9#scrollTo=ILPR3vXWdAvY

安装依赖的包

!pip install xgboost sklearn hyperopt

导入基本库

# 导入基本包
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.metrics import accuracy_score
from hyperopt import STATUS_OK, Trials, fmin, hp, tpe
from sklearn.model_selection import train_test_split

加载数据，并拆分

df = pd.read_csv("drive/MyDrive/data_daily/Wholesalecustomersdata.csv")

x = df.drop('Channel', axis=1)
y = df['Channel']
"""将分类任务转换为0-1"""
y[y == 2] = 0
y[y == 1] = 1

"""切分数据集"""
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 0)

使用优化器进行参数寻优

1. 定义参数空间，指定参数的所有候选空间
2. 定义训练过程和评估的目标（损失函数）
3. 执行寻优过程
4. 获取最优的参数组合

初始化参数空间

The available hyperopt optimization algorithms are -

• hp.choice(label, options) — Returns one of the options, which should be a list or tuple.

• hp.randint(label, upper) — Returns a random integer between the range [0, upper).

• hp.uniform(label, low, high) — Returns a value uniformly between low and high.

• hp.quniform(label, low, high, q) — Returns a value round(uniform(low, high) / q) * q, i.e it rounds the decimal values and returns an integer.

• hp.normal(label, mean, std) — Returns a real value that’s normally-distributed with mean and standard deviation sigma.

space={'max_depth': hp.quniform("max_depth", 3, 18, 1),
        'gamma': hp.uniform ('gamma', 1,9),
        'reg_alpha' : hp.quniform('reg_alpha', 40,180,1),
        'reg_lambda' : hp.uniform('reg_lambda', 0,1),
        'colsample_bytree' : hp.uniform('colsample_bytree', 0.5,1),
        'min_child_weight' : hp.quniform('min_child_weight', 0, 10, 1),
        'n_estimators': 180,
        'seed': 0
    }

定义优化目标

def objective(space):
    clf=xgb.XGBClassifier(
                    n_estimators =space['n_estimators'], max_depth = int(space['max_depth']), gamma = space['gamma'],
                    reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),
                    colsample_bytree=int(space['colsample_bytree']))
    
    evaluation = [( X_train, y_train), ( X_test, y_test)]
    
    clf.fit(X_train, y_train,
            eval_set=evaluation, eval_metric="auc",
            early_stopping_rounds=10,verbose=False)
    

    pred = clf.predict(X_test)
    accuracy = accuracy_score(y_test, pred>0.5)
    print ("SCORE:", accuracy)
    return {'loss': -accuracy, 'status': STATUS_OK }

寻优过程

trials = Trials()

best_hyperparams = fmin(fn = objective,
                        space = space,
                        algo = tpe.suggest,
                        max_evals = 100,
                        trials = trials)

打印结果

• Here best_hyperparams gives us the optimal parameters that best fit model and better loss function value.

• trials is an object that contains or stores all the relevant information such as hyperparameter, loss-functions for each set of parameters that the model has been trained.

• 'fmin' is an optimization function that minimizes the loss function and takes in 4 inputs - fn, space, algo and max_evals.

• Algorithm used is tpe.suggest.

print("The best hyperparameters are : ","\n")
print(best_hyperparams)