AcidificationModel/api/model_optimize/XGBR_filt.py

# 导入常用基本包
import os
import pandas as pd
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.cm as cm

# 机器学习相关库
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, GridSearchCV
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, accuracy_score, log_loss, roc_auc_score

# 导入XGBoost
from xgboost import XGBRegressor as XGBR

# 其他工具
import pickle
from pathlib import Path

# 导入数据
data=pd.read_excel('model_optimize\data\data_filt.xlsx')
x = data.iloc[:,1:10]
y = data.iloc[:,-1]

# 为 x 赋予列名
x.columns = [
    'organic_matter',        # OM g/kg
    'chloride',              # CL g/kg
    'cec',                   # CEC cmol/kg
    'h_concentration',       # H+ cmol/kg
    'hn',                    # HN mg/kg
    'al_concentration',      # Al3+ cmol/kg
    'free_alumina',          # Free alumina g/kg
    'free_iron',             # Free iron oxides g/kg
    'delta_ph'               # ΔpH
]

y.name = 'target_ph'

Xtrain, Xtest, Ytrain, Ytest = train_test_split(x, y, test_size=0.2, random_state=42)

# 
score_5cv_all = []
for i in np.arange(0.01, 0.5, 0.01):
    XGB = XGBR(learning_rate=i)
    score_5cv = cross_val_score(XGB, Xtrain, Ytrain, cv=5).mean()
    score_5cv_all.append(score_5cv)
    pass
score_max_5cv = max(score_5cv_all)
n_lr_5cv = np.arange(0.01,0.5,0.01)[score_5cv_all.index(score_max_5cv)]
print(
      "最大5cv得分：{}".format(score_max_5cv),
      "n_lr_5cv:{}".format(n_lr_5cv))

# 随机种子
score_5cv_all = []
for i in range(0, 200, 1):
    XGB =XGBR(learning_rate=n_lr_5cv
              ,random_state=i)
    score_5cv =cross_val_score(XGB, Xtrain, Ytrain, cv=5).mean()
    score_5cv_all.append(score_5cv)
    pass
score_max_5cv = max(score_5cv_all)
random_state_5cv = range(0, 200)[score_5cv_all.index(max(score_5cv_all))]
print(
      "最大5cv得分：{}".format(score_max_5cv),
      "random_5cv:{}".format(random_state_5cv))

#随机树数目
score_5cv_all = []
for i in range(1, 400, 1):
    XGB = XGBR(n_estimators=i,
               learning_rate=n_lr_5cv,
               random_state=random_state_5cv)
    score_5cv = cross_val_score(XGB, Xtrain, Ytrain, cv=5).mean()
    score_5cv_all.append(score_5cv)
    pass
score_max_5cv = max(score_5cv_all)
n_est_5cv = range(1,400)[score_5cv_all.index(score_max_5cv)]

print(
      "最大5cv得分：{}".format(score_max_5cv),
      "n_est_5cv:{}".format(n_est_5cv))

score_5cv_all = []
for i in range(1, 300, 1):
    XGB = XGBR(n_estimators=n_est_5cv,
               learning_rate=n_lr_5cv, 
               random_state=random_state_5cv, 
               max_depth=i)
    score_5cv = cross_val_score(XGB, Xtrain, Ytrain, cv=5).mean()
    score_5cv_all.append(score_5cv)
    pass
score_max_5cv = max(score_5cv_all)
max_depth_5cv = range(1,300)[score_5cv_all.index(score_max_5cv)]
print(
      "最大5cv得分：{}".format(score_max_5cv),
      "max_depth_5cv:{}".format(max_depth_5cv))

score_5cv_all = []
for i in np.arange(0,5,0.05):
    XGB = XGBR(n_estimators=n_est_5cv,
               learning_rate=n_lr_5cv,
               random_state=random_state_5cv,
               max_depth=max_depth_5cv,
               gamma= i)
    score_5cv = cross_val_score(XGB, Xtrain, Ytrain, cv=5).mean()
    score_5cv_all.append(score_5cv)
    pass
score_max_5cv = max(score_5cv_all)
max_gamma_5cv =  np.arange(0,5,0.05)[score_5cv_all.index(score_max_5cv)]
print(
      "最大5cv得分：{}".format(score_max_5cv),
      "gamma_5cv:{}".format(max_gamma_5cv))

score_5cv_all = []
for i in np.arange(0,5,0.05):
    XGB = XGBR(n_estimators=n_est_5cv,
               learning_rate=n_lr_5cv, 
               random_state=random_state_5cv,
               max_depth=max_depth_5cv,
               gamma=max_gamma_5cv,
               alpha=i)
    score_5cv = cross_val_score(XGB, Xtrain, Ytrain, cv=5).mean()
    score_5cv_all.append(score_5cv)
    pass
score_max_5cv = max(score_5cv_all)
max_alpha_5cv =  np.arange(0,5,0.05)[score_5cv_all.index(score_max_5cv)]
print(
      "最大5cv得分：{}".format(score_max_5cv),
      "alpha_5cv:{}".format(max_alpha_5cv))

XGB = XGBR(learning_rate=n_lr_5cv,n_estimators=n_est_5cv,random_state=random_state_5cv,max_depth=max_depth_5cv,gamma =max_gamma_5cv,alpha = max_alpha_5cv)
CV_score = cross_val_score(XGB, Xtrain, Ytrain, cv=5).mean()
CV_predictions = cross_val_predict(XGB, Xtrain, Ytrain, cv=5)
rmse1 = np.sqrt(mean_squared_error(Ytrain,CV_predictions))
regressor = XGB.fit(Xtrain, Ytrain)
test_predictions = regressor.predict(Xtest)
score_test = regressor.score(Xtest,Ytest)
rmse2 = np.sqrt(mean_squared_error(Ytest,test_predictions))
print("5cv:",CV_score)
print("rmse_5CV",rmse1)
print("test:",score_test)
print("rmse_test",rmse2)