AcidificationModel/api/model_optimize/data_increase.py

# 导入常用包
import os
import pandas as pd
import numpy as np
from PIL import Image
from model_saver import save_model

# 机器学习模型导入
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import GradientBoostingRegressor as GBSTR
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor as XGBR

# 导入数据处理函数
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler

# 导入评分函数
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error 
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import accuracy_score
from sklearn.metrics import log_loss
from sklearn.metrics import roc_auc_score

import pickle
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from pathlib import Path

## 土壤反酸筛选数据
data=pd.read_excel('model_optimize\data\data_filt.xlsx')   

x = data.iloc[:,1:10]
print(x)
# x = data.iloc[:,1:9]
y = data.iloc[:,-1]

y1 = data.iloc[:,-2]
y2 = data.iloc[:,-1]
# 绝对值
y = abs(y1 - y2)

print(y)

# 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'

# ## 精准降酸数据
# data=pd.read_excel('model_optimize\data\Acidity_reduce.xlsx')

# x = data.iloc[:,1:]
# y = data.iloc[:,0]
# # 为 x 赋予列名
# x.columns = [
#     'pH',        
#     'OM',              
#     'CL', 
#     'H',
#     'Al'
# ]
# y.name = 'target'


## 数据集划分
Xtrain, Xtest, Ytrain, Ytest = train_test_split(x, y, test_size=0.2, random_state=42)

## 模型
# 随机森林回归模型
rfc = RandomForestRegressor(random_state=1)
# XGBR
XGB = XGBR(random_state=1)
# GBSTR
GBST = GBSTR(random_state=1)
# KNN
KNN = KNeighborsRegressor(n_neighbors=4)

# 增量训练：每次增加10%的训练数据
increment = 0.1  # 每次增加的比例
train_sizes = np.arange(0.1, 1.1, increment)  # 从0.1到1.0的训练集大小
r2_scores_rfc = []  
r2_scores_xgb = []  
r2_scores_gbst = []  
r2_scores_knn = []  # 用来记录KNN的r2_score


# 对于每种训练集大小，训练模型并记录r2_score
for size in train_sizes:
    # 计算当前训练集的大小
    current_size = int(size * len(Xtrain))
    
    # RandomForestRegressor
    rfc.fit(Xtrain[:current_size], Ytrain[:current_size])
    # XGBRegressor
    XGB.fit(Xtrain[:current_size], Ytrain[:current_size])
    # GBST
    GBST.fit(Xtrain[:current_size], Ytrain[:current_size])
    # KNN
    KNN.fit(Xtrain[:current_size], Ytrain[:current_size])

    # r2_score
    y_pred_rfc = rfc.predict(Xtest)
    score_rfc = r2_score(Ytest, y_pred_rfc)
    r2_scores_rfc.append(score_rfc)
    
    # XGBRegressor的r2_score
    y_pred_xgb = XGB.predict(Xtest)
    score_xgb = r2_score(Ytest, y_pred_xgb)
    r2_scores_xgb.append(score_xgb)

    # GBST的r2_score
    y_pred_gbst = GBST.predict(Xtest)
    score_gbst = r2_score(Ytest, y_pred_gbst)
    r2_scores_gbst.append(score_gbst)

    # KNN的r2_score
    y_pred_knn = KNN.predict(Xtest)
    score_knn = r2_score(Ytest, y_pred_knn)
    r2_scores_knn.append(score_knn)

    # 输出当前的训练进度与r2评分
    print(f"Training with {size * 100:.2f}% of the data:")
    print(f"  - Random Forest R2 score: {score_rfc}")
    print(f"  - XGB R2 score: {score_xgb}")
    print(f"  - GBST R2 score: {score_gbst}")
    print(f"  - KNN R2 score: {score_knn}")

# 绘制R2评分随训练数据大小变化的图形
import matplotlib.pyplot as plt

plt.plot(train_sizes * 100, r2_scores_rfc, marker='o', label='Random Forest')
plt.plot(train_sizes * 100, r2_scores_xgb, marker='x', label='XGBoost')
plt.plot(train_sizes * 100, r2_scores_gbst, marker='s', label='Gradient Boosting')
# plt.plot(train_sizes * 100, r2_scores_knn, marker='^', label='KNN')


plt.xlabel('Training data size (%)')
plt.ylabel('R2 Score')
plt.title('Model Performance with Incremental Data')
plt.legend()
plt.grid(True)
plt.show()


# 选择后半部分数据
# import matplotlib.pyplot as plt

# # 选择后半部分数据
# half_index = len(train_sizes) // 2

# # 绘制后半部分的数据
# plt.plot(train_sizes[half_index:] * 100, r2_scores_rfc[half_index:], marker='o', label='Random Forest')
# plt.plot(train_sizes[half_index:] * 100, r2_scores_xgb[half_index:], marker='x', label='XGBoost')
# plt.plot(train_sizes[half_index:] * 100, r2_scores_gbst[half_index:], marker='s', label='Gradient Boosting')
# plt.plot(train_sizes[half_index:] * 100, r2_scores_knn[half_index:], marker='^', label='KNN')

# plt.xlabel('Training data size (%)')
# plt.ylabel('R2 Score')
# plt.title('Model Performance with Incremental Data (Second Half)')
# plt.legend()
# plt.grid(True)
# plt.show()