mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-08 14:24:00 +00:00
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.decomposition import PCA
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from sklearn.cross_decomposition import PLSRegression
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from sklearn.metrics import r2_score, mean_squared_error
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import copy
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from math import *
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#载入数据
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data_path = './/merged1124.xlsx' #数据
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label_path = './/label.xlsx' #标签（反射率）
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D = pd.read_excel(data_path)
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L = pd.read_excel(label_path)
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data = np.array(D)
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label = np.array(L)
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print(label.shape)
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# 绘制原始后图片
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plt.figure(500)
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x_col = data[:,0] #数组逆序
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y_col = data[:, 1:40]
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plt.plot(x_col, y_col)
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plt.xlabel("Wavenumber(nm)")
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plt.ylabel("Absorbance")
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plt.title("The spectrum of the tomato dataset",fontweight= "semibold",fontsize='x-large')
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# plt.savefig('.//Result//MSC.png')
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plt.show()
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#随机划分数据集
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x_data = np.transpose(data[:, 1:40])
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print(x_data.shape)
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y_data = copy.deepcopy(label)
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print(y_data.shape)
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test_ratio = 0.2
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X_train,X_test,y_train,y_test = train_test_split(x_data,y_data,test_size=test_ratio,shuffle=True,random_state=2)
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#载入数据
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#PCA降维到10个维度,测试该数据最好
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pca=PCA(n_components=28) #只保留2个特征
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pca.fit(X_train)
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X_train_reduction = pca.transform(X_train)
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X_test_reduction = pca.transform(X_test)
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# PCA降维后图片绘制
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plt.figure(100)
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plt.scatter(X_train_reduction[:, 0], X_train_reduction[:, 1],marker='o')
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plt.xlabel("Wavenumber(nm)")
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plt.ylabel("Absorbance")
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plt.title("The PCA for tomato dataset",fontweight= "semibold",fontsize='large')
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# plt.savefig('.//Result//PCA.png')
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plt.show()
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#pls预测
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pls2 = PLSRegression(n_components=5)
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pls2.fit(X_train_reduction, y_train)
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train_pred = pls2.predict(X_train_reduction)
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pred = pls2.predict(X_test_reduction)
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#计算R2
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train_R2 = r2_score(train_pred,y_train)
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R2 = r2_score(y_test,pred) #Y_true, Pred
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print('训练R2:{}'.format(train_R2))
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print('测试R2:{}'.format(R2))
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#计算MSE
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print('********************')
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x_MSE = mean_squared_error(train_pred,y_train)
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t_MSE = mean_squared_error(y_test,pred)
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print('训练MSE:{}'.format(x_MSE))
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print('测试MSE:{}'.format(t_MSE))
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#计算RMSE
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print('********************')
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print('训练RMSE:{}'.format(sqrt(x_MSE)))
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print('测试RMSE:{}'.format(sqrt(t_MSE)))
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