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Copy pathHS_15_各时期选取不同变量的情况.py
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196 lines (141 loc) · 5.87 KB
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r'''
自行整理数据 参考如下格式整理Excel，并放入 以日期命名的sheet 中，必须将比值参数写为其他形式
SPAD NDVI WI VOG1 NDWI （WI-NDWI）
0.091192753 0.747816054 1.046051303 1.3417481 0.055930666 18.70264338
0.080065359 0.747029793 1.048070413 1.308156232 0.054341737 19.28665657
0.080764488 0.729497354 1.049204052 1.221287842 0.055720883 18.8296381
0.055387819 0.74600639 1.045696277 1.380875638 0.056603774 18.47396757
0.068351361 0.756081194 1.05111755 1.375104661 0.060047379 17.50480324
0.101584751 0.760787825 1.046881814 1.394096166 0.053751765 19.47623157
0.062190744 0.752103787 1.049978364 1.354010387 0.056636217 18.5389918
0.068851859 0.744299357 1.04427334 1.256709452 0.052392344 19.93179251
0.086923254 0.757803563 1.05003026 1.259241126 0.058729204 17.87918419
0.066938127 0.733067066 1.045379196 1.239851485 0.054616653 19.14030136
该脚本由于 water 与相关系数做普通回归分析
主要包含以下几种：
一元线性：y=ax+b
(a)/(b)/(c)/(d)分别代表四个生育期
'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
import os
import argparse
import time
from scipy.optimize import curve_fit
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from pylab import mpl
import csv
mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
print(__doc__)
start = time.clock()
starttime = datetime.datetime.now()
print('START RUNNING...')
print('Current time:%s'%time.ctime())
print('-'*80)
data_path = r'J:\论文数据\output\ordinary_regress_water\遥感参数\water.xlsx'
full_path=os.path.split(data_path)[0]
HS_path=full_path
def reg(X,y):
reg=LinearRegression().fit(X,y)
coef=round(reg.coef_[0][0],4)
intercept=round(reg.intercept_[0],3)
model=str('y=')+str(coef)+'x+'+str(intercept)
y_predict=reg.predict(X)
delta=abs(y-y_predict)
data=pd.Series(delta.reshape(1,-1)[0])
index=data[data<4].index
return index,model,coef,intercept,y_predict
def main():
#对应玉米的四个生育期
data1 = pd.read_excel(data_path, sheet_name='0605')
data2 = pd.read_excel(data_path, sheet_name='0622')
data3 = pd.read_excel(data_path, sheet_name='0717')
data4 = pd.read_excel(data_path, sheet_name='0826')
#Load dataset
col_0605=['SDy','SDr','Ry','Rr','SDr#SDy']
col_0622=['Dr','Ry']
col_0717=['Dr','Ry']
col_0826=['Db','SDr#SDb','(SDr - SDy) # (SDr + SDy)']
periods=['0605','0622','0717','0826']
def fit_and_draw(data,cols,period):
try:
os.makedirs(os.path.join(full_path,period))
except:
pass
HS_path=os.path.join(full_path,period)
all_data=[]
for index,col in enumerate(cols):
fig,axes=plt.subplots(1,1,figsize=(4,4))
LR=LinearRegression()
data=data
print('Current parameter is %s'%col)
X=np.array(data[col]).reshape(-1,1)
y = np.array ( data['water'] ).reshape ( -1, 1 )
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=1/3)
reg_LR=LR.fit(X_train,y_train)
coef=round(reg_LR.coef_[0][0],4)
intercept=round(reg_LR.intercept_[0],3)
y_train_pre=reg_LR.predict ( X_train )
R_2=round ( r2_score (y_train, y_train_pre ), 3 )
model=str(r'y = '+str(coef)+'x+'+str(intercept))
y_pre = reg_LR.predict ( X_test )
rmse = round ( np.sqrt ( mean_squared_error ( y_test, y_pre ) ), 3 )
r_2 = round ( r2_score (y_test, y_pre ), 3 )
RE = round ( np.mean ( abs ( (y_pre - y_test) ) / y_test ), 3 )
with open ( os.path.join(HS_path,'%s_%s.txt'%(period,col)), 'a+' ) as f:
f.writelines('当前参数为：'+str(col)+':'+period+'\n')
f.writelines ( u'模型:' +model + '\n' )
f.writelines ( u'模型建立的时 R2:' +str(R_2) + '\n' )
f.writelines ( 'r_2:' + str ( r_2) + '\n' )
f.writelines ( 'rmse:' + str ( rmse) + '\n' )
f.writelines ( 'RE:' + str ( RE) + '\n' )
f.writelines ( '-'*50 + '\n' )
my_data=[{'col':col},{'model':model},{'R2':R_2},{'r2':r_2},{'RMSE':rmse},{'RE%':RE*100}]
all_data.append(my_data)
#plot
ax=axes
ax.scatter(y_test,y_pre,s=10,marker='*')
#实测值与预测值再次建立回归模型，再次计算新模型的决定系数、rmse、绝对误差
_, model, coef1, intercept1,y_pre_new = reg ( y_test, y_pre )
# with open ( os.path.join(HS_path,'%s_model_02.txt'%i), 'a+' ) as f:
# f.writelines('当前参数为：'+str(i)+'\n')
# f.writelines ( u'模型:' +model + '\n' )
# f.writelines ( 'r_2:' + str ( r_2) + '\n' )
# f.writelines ( 'rmse:' + str ( rmse) + '\n' )
# f.writelines ( 'RE:' + str ( RE) + '\n' )
# f.writelines ( '--'*50 + '\n' )
line=np.linspace(0,0.35,50)*coef1+intercept1
ax.plot(np.linspace(0,0.35,50),line,label=model+'\n'+r'$R^2=$'+str(r_2))
ax.set_xlabel('实测值 Measured value')
ax.set_ylabel('预测值 Predicted value')
line_2= np.linspace(0,0.35,50)
ax.plot ( line_2,line_2,'--')
ax.legend(loc=4,edgecolor='white')
ax.set_xticks(np.linspace(0,0.35,8))
ax.set_xticks(np.linspace(0,0.35,8))
ax.set_xlim(0,0.35)
ax.set_ylim(0,0.35)
ax.spines['top'].set_visible ( False )
ax.spines['right'].set_visible ( False )
plt.savefig(os.path.join(HS_path,'./%s_%s.png'%(period,col)))
print(all_data)
headers=['col','model','R2','r2','RMSE','RE%']
with open(os.path.join(HS_path, '%s.csv'%(period)), 'a', newline='') as f:
writer=csv.DictWriter(f,headers)
writer.writeheader()
for DATA in all_data:
writer.writerows(DATA)
#fit_and_draw(data1,col_0605,periods[0])
fit_and_draw(data2,col_0622,periods[1])
fit_and_draw(data3,col_0717,periods[2])
fit_and_draw(data4,col_0826,periods[3])
main()
end = time.clock()
print('Running:%fs' % (end - start))
print('DONE...')