【机器学习】Optuna机器学习模型调参(LightGBM、XGBoost)

文章目录

• 1. optuna简介
• 2. LGBM和XGBoost调参汇总

• 2.1 LGBM

• 2.1.1 定义Objective
• 2.1.2 调参try
• 2.1.3 绘图
• 2.1.4 最佳参数

• 2.2 XGBOOST

• 2.2.1 定义Objectove
• 2.2.2 调参try
• 2.2.3 绘图
• 2.2.4 最佳参数

1. optuna简介

在Kaggle比赛的过程中我发现了一个问题（大家的Kernel模型中包含了众多c超参数设置，但是这些参数是如何设置的呢？），并在Discussion中提出了我的问题，并得到了众多大佬的回答，如下：

关于回答我汇总后发现都提到了关于optuna库的使用，optuna是什么呢？optuna是一个使用python编写的超参数调节框架。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，而 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。

下面举一个简单的栗子，有助于大家的理解：

定义，求取得最大值时，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)

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()

2.2.4 最佳参数

params=study.best_params