[PYTHON] How to set xg boost using Optuna

How to set xg boost using Optuna

Let's set the regression of xgboost.

For xgboost, please refer to other HP. "What is Xgboost?" & A memo about the main parameters " https://qiita.com/2357gi/items/913af8b813b069617aad

Later, I referred to the parameters on the official website. https://xgboost.readthedocs.io/en/latest/parameter.html

I put in various things, but the decision tree system tends to be overfitted, so I think it is better to set the parameters that control it firmly. In xgboost, it is lambda and ʻalpha, but when setting with python, specify it with reg_ like reg_lambdaandreg_alpha`.

#Set the objective function of optuna
#It is a parameter setting of gtree.
def objective(trial):
    eta =  trial.suggest_loguniform('eta', 1e-8, 1.0)
    gamma = trial.suggest_loguniform('gamma', 1e-8, 1.0)
    max_depth = trial.suggest_int('max_depth', 1, 20)
    min_child_weight = trial.suggest_loguniform('min_child_weight', 1e-8, 1.0)
    max_delta_step = trial.suggest_loguniform('max_delta_step', 1e-8, 1.0)
    subsample = trial.suggest_uniform('subsample', 0.0, 1.0)
    reg_lambda = trial.suggest_uniform('reg_lambda', 0.0, 1000.0)
    reg_alpha = trial.suggest_uniform('reg_alpha', 0.0, 1000.0)
    
  
    regr =xgb.XGBRegressor(eta = eta, gamma = gamma, max_depth = max_depth,
                           min_child_weight = min_child_weight, max_delta_step = max_delta_step,
                           subsample = subsample,reg_lambda = reg_lambda,reg_alpha = reg_alpha)
 
    score = cross_val_score(regr, X_train, y_train, cv=5, scoring="r2")
    r2_mean = score.mean()
    print(r2_mean)
 
    return r2_mean

#Find the optimal value with optuna
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=500)
 
#Fits tuned hyperparameters
optimised_rf = xgb.XGBRegressor(eta = study.best_params['eta'],gamma = study.best_params['gamma'],
                                max_depth = study.best_params['max_depth'],min_child_weight = study.best_params['min_child_weight'],
                                max_delta_step = study.best_params['max_delta_step'],subsample = study.best_params['subsample'],
                                reg_lambda = study.best_params['reg_lambda'],reg_alpha = study.best_params['reg_alpha'])
 
optimised_rf.fit(X_train ,y_train)

This is the result of Boston.

It's all.

# -*- coding: utf-8 -*-
 
from sklearn import datasets
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
import pandas as pd
import optuna
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score
 
#Load a Boston dataset
boston = datasets.load_boston()
 
#print(boston['feature_names'])
#Separate features and objective variables
X = boston['data']
y = boston['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)

#Set the objective function of optuna
#It is a parameter setting of gtree.
def objective(trial):
    eta =  trial.suggest_loguniform('eta', 1e-8, 1.0)
    gamma = trial.suggest_loguniform('gamma', 1e-8, 1.0)
    max_depth = trial.suggest_int('max_depth', 1, 20)
    min_child_weight = trial.suggest_loguniform('min_child_weight', 1e-8, 1.0)
    max_delta_step = trial.suggest_loguniform('max_delta_step', 1e-8, 1.0)
    subsample = trial.suggest_uniform('subsample', 0.0, 1.0)
    reg_lambda = trial.suggest_uniform('reg_lambda', 0.0, 1000.0)
    reg_alpha = trial.suggest_uniform('reg_alpha', 0.0, 1000.0)
    
  
    regr =xgb.XGBRegressor(eta = eta, gamma = gamma, max_depth = max_depth,
                           min_child_weight = min_child_weight, max_delta_step = max_delta_step,
                           subsample = subsample,reg_lambda = reg_lambda,reg_alpha = reg_alpha)
 
    score = cross_val_score(regr, X_train, y_train, cv=5, scoring="r2")
    r2_mean = score.mean()
    print(r2_mean)
 
    return r2_mean
 
#Find the optimal value with optuna
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=500)
 
#Fits tuned hyperparameters
optimised_rf = xgb.XGBRegressor(eta = study.best_params['eta'],gamma = study.best_params['gamma'],
                                max_depth = study.best_params['max_depth'],min_child_weight = study.best_params['min_child_weight'],
                                max_delta_step = study.best_params['max_delta_step'],subsample = study.best_params['subsample'],
                                reg_lambda = study.best_params['reg_lambda'],reg_alpha = study.best_params['reg_alpha'])
 
optimised_rf.fit(X_train ,y_train)
#View results
print("Fits training data")
print("Training data accuracy=", optimised_rf.score(X_train, y_train))
pre_train = optimised_rf.predict(X_train)
print("Fits test data")
print("Test data accuracy=", optimised_rf.score(X_test, y_test))
pre_test = optimised_rf.predict(X_test)
 
#Graph display
plt.scatter(y_train, pre_train, marker='o', cmap = "Blue", label="train")
plt.scatter(y_test ,pre_test, marker='o', cmap= "Red", label="test")
plt.title('boston')
plt.xlabel('measurment')
plt.ylabel('predict')
#Fine-tune the text here
x = 30  
y1 = 12
y2 = 10
s1 =  "train_r2 =" + str(optimised_rf.score(X_train, y_train))
s2 =  "test_r2 =" + str(optimised_rf.score(X_test, y_test))
plt.text(x, y1, s1)
plt.text(x, y2, s2)
 
plt.legend(loc="upper left", fontsize=14)
plt.show()