基于 Optuna 的模型超参数优化

文章目录

1. Optuna简介

1.1 Optuna 介绍

Optuna官网: https://optuna.org/

Optuna是一个开源的超参数优化(HPO)框架，用于自动执行超参数的搜索空间。为了找到最佳的超参数集，Optuna使用贝叶斯方法。它支持下面列出的各种类型的采样器：

GridSampler (使用网格搜索)
RandomSampler (使用随机采样)
TPESampler (使用树结构的Parzen估计器算法)
CmaEsSampler (使用CMA-ES算法)

一个极简的 Optuna 的优化程序中只有三个最核心的概念，目标函数(objective)，单次试验(trial)，和研究(study)。其中

objective 负责定义待优化函数并指定参/超参数数范围
trial 对应着 objective 的单次执行
study 则负责管理优化，决定优化的方式，总试验的次数、试验结果的记录等功能。

1.2 Optuna 安装

可以选择基于pip的安装或者基于conda的安装方式。
Optuna is available at the Python Package Index and on Anaconda Cloud.

pip install optuna

conda install -c conda-forge optuna

1.3 Optuna 示例

下面举一个简单的例子，有助于大家的理解：

定义

x

,

y

∈

(

−

10

,

10

)

x,y∈(−10,10)

$x, y \in (- 10, 10)$ ，求

f

(

x

)

=

(

x

+

y

)

2

f(x)=(x+y)^2

$f (x) = (x + y)^{2}$ 取得最大值时，

x

,

y

x,y

$x, y$ 的取值？

import optuna
 
def objective(trial):
    x = trial.suggest_uniform('x', -10, 10)
    y = trial.suggest_uniform('y', -10, 10)
    return (x + y) ** 2
 
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=100)
 
print(study.best_params)
print(study.best_value)

更多关于 Optuna 的基本介绍可以参考博客:

使用Optuna的XGBoost模型的高效超参数优化: https://blog.csdn.net/weixin_26752765/article/details/108225744

2. LGBM和XGBoost调参汇总

2.1 LGBM

2.1.1 定义Objective

from lightgbm import LGBMRegressor
import optuna
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, KFold
import optuna.integration.lightgbm as oplgb

def objective(trial):
    X_train, X_test, y_train, y_test=train_test_split(data, target, train_size=0.3)# 数据集划分
    param = {
        'metric': 'rmse', 
        'random_state': 48,
        'n_estimators': 20000,
        'reg_alpha': trial.suggest_loguniform('reg_alpha', 1e-3, 10.0),
        'reg_lambda': trial.suggest_loguniform('reg_lambda', 1e-3, 10.0),
        'colsample_bytree': trial.suggest_categorical('colsample_bytree', [0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.0]),
        'subsample': trial.suggest_categorical('subsample', [0.4,0.5,0.6,0.7,0.8,1.0]),
        'learning_rate': trial.suggest_categorical('learning_rate', [0.006,0.008,0.01,0.014,0.017,0.02]),
        'max_depth': trial.suggest_categorical('max_depth', [5, 7, 9, 11, 13, 15, 17, 20, 50]),
        'num_leaves' : trial.suggest_int('num_leaves', 1, 1000),
        'min_child_samples': trial.suggest_int('min_child_samples', 1, 300),
        'cat_smooth' : trial.suggest_int('cat_smooth', 1, 100)      
    }
    
    lgb=LGBMRegressor(**param)
    lgb.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=100, verbose=False)
    pred_lgb=lgb.predict(X_test)
    rmse = mean_squared_error(y_test, pred_lgb, squared=False)
    return rmse

2.1.2 调参try

study=optuna.create_study(direction='minimize')
n_trials=50 # try50次
study.optimize(objective, n_trials=n_trials)

2.1.3 绘图

optuna.visualization.plot_optimization_history(study)# 绘制

optuna.visualization.plot_parallel_coordinate(study)#

optuna.visualization.plot_param_importances(study)#

2.1.4 最佳参数

params=study.best_params
params['metric'] = 'rmse'

2.2 XGBOOST

2.2.1 定义Objectove

def objective(trial):
    data = train.iloc[:, :-1]
    target = train.target
    train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.3, random_state=42)
    param = {
        'lambda': trial.suggest_loguniform('lambda', 1e-3, 10.0),
        'alpha': trial.suggest_loguniform('alpha', 1e-3, 10.0),
        'colsample_bytree': trial.suggest_categorical('colsample_bytree', [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]),
        'subsample': trial.suggest_categorical('subsample', [0.4, 0.5, 0.6, 0.7, 0.8, 1.0]),
        'learning_rate': trial.suggest_categorical('learning_rate',
                                                   [0.008, 0.009, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02]),
        'n_estimators': 4000,
        'max_depth': trial.suggest_categorical('max_depth', [5, 7, 9, 11, 13, 15, 17, 20]),
        'random_state': trial.suggest_categorical('random_state', [24, 48, 2020]),
        'min_child_weight': trial.suggest_int('min_child_weight', 1, 300),
    }
    model = xgb.XGBRegressor(**param)
    model.fit(train_x, train_y, eval_set=[(test_x, test_y)], early_stopping_rounds=100, verbose=False)
    preds = model.predict(test_x)
    rmse = mean_squared_error(test_y, preds, squared=False)
    return rmse

2.2.2 调参try

study = optuna.create_study(direction='minimize')
n_trials=1
study.optimize(objective, n_trials=n_trials)
print('Number of finished trials:', len(study.trials))
print("------------------------------------------------")
print('Best trial:', study.best_trial.params)
print("------------------------------------------------")
print(study.trials_dataframe())
print("------------------------------------------------")

2.2.3 绘图

optuna.visualization.plot_optimization_history(study).show()
#plot_parallel_coordinate: interactively visualizes the hyperparameters and scores
optuna.visualization.plot_parallel_coordinate(study).show()
'''plot_slice: shows the evolution of the search. You can see where in the hyperparameter space your search
went and which parts of the space were explored more.'''
optuna.visualization.plot_slice(study).show()
optuna.visualization.plot_contour(study, params=['alpha',
                            #'max_depth',
                            'lambda',
                            'subsample',
                            'learning_rate',
                            'subsample']).show()
#Visualize parameter importances.
optuna.visualization.plot_param_importances(study).show()
#Visualize empirical distribution function
optuna.visualization.plot_edf(study).show()