AcidificationModel/api/model_optimize/KNN_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)

from sklearn.neighbors import KNeighborsRegressor
KNN_model = KNeighborsRegressor(n_neighbors=10)
r2_score=cross_val_score(KNN_model,Xtrain,Ytrain,cv=5)
print(r2_score)
print(r2_score.mean())