{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "798076ab-a8f3-47d4-a221-00b465568f41",
"metadata": {},
"outputs": [],
"source": [
"from statistics import mean\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import explained_variance_score,r2_score,median_absolute_error,mean_squared_error,mean_absolute_error\n",
"from scipy import stats\n",
"import numpy as np\n",
"plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n",
"plt.rcParams[\"font.size\"] = 16\n",
"plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "697bdeac-4ca7-4cf9-ad01-2ee00b4c7c72",
"metadata": {},
"outputs": [],
"source": [
"def scatter_out_1(x,y): ## x,y为两个需要做对比分析的两个量。\n",
" # ==========计算评价指标==========\n",
" BIAS = mean(x - y)\n",
" MSE = mean_squared_error(x, y)\n",
" RMSE = np.power(MSE, 0.5)\n",
" R2 = r2_score(x, y)\n",
" MAE = mean_absolute_error(x, y)\n",
" EV = explained_variance_score(x, y)\n",
" print('==========算法评价指标==========')\n",
" print('BIAS:', '%.3f' % (BIAS))\n",
" print('Explained Variance(EV):', '%.3f' % (EV))\n",
" print('Mean Absolute Error(MAE):', '%.3f' % (MAE))\n",
" print('Mean squared error(MSE):', '%.3f' % (MSE))\n",
" print('Root Mean Squard Error(RMSE):', '%.3f' % (RMSE))\n",
" print('R_squared:', '%.3f' % (R2))\n",
" # ===========Calculate the point density==========\n",
" xy = np.vstack([x, y])\n",
" z = stats.gaussian_kde(xy)(xy)\n",
" # ===========Sort the points by density, so that the densest points are plotted last===========\n",
" idx = z.argsort()\n",
" x, y, z = x[idx], y[idx], z[idx]\n",
" def best_fit_slope_and_intercept(xs, ys):\n",
" m = (((mean(xs) * mean(ys)) - mean(xs * ys)) / ((mean(xs) * mean(xs)) - mean(xs * xs)))\n",
" b = mean(ys) - m * mean(xs)\n",
" return m, b\n",
" m, b = best_fit_slope_and_intercept(x, y)\n",
" regression_line = []\n",
" for a in x:\n",
" regression_line.append((m * a) + b)\n",
" fig,ax=plt.subplots(figsize=(12,9),dpi=600)\n",
" scatter=ax.scatter(x,y,marker='o',c=z, edgecolors='b',s=15,label='LST',cmap='Spectral_r')\n",
" cbar=plt.colorbar(scatter,shrink=1,orientation='vertical',extend='both',pad=0.015,aspect=30,label='frequency', )\n",
" plt.plot([0,35],[0,35],'black',lw=1.5) # 画的1:1线，线的颜色为black，线宽为0.8\n",
" plt.plot(x,regression_line,'red',lw=1.5) # 预测与实测数据之间的回归线\n",
" plt.axis([0,35,0,35]) # 设置线的范围\n",
" plt.title(\"总孔体积拟合结果 $10^2 cm^3/g$\", fontdict={\"fontsize\":16})\n",
" plt.xlabel('预测值', fontdict={\"fontsize\":16})\n",
" plt.ylabel('真实值', fontdict={\"fontsize\":16})\n",
" plt.text(0.5,34, '$N=%.f$' % len(y), fontdict={\"fontsize\":16}) # text的位置需要根据x,y的大小范围进行调整。\n",
" plt.text(0.5,33, '$R^2=%.3f$' % R2, fontdict={\"fontsize\":16})\n",
" plt.text(0.5,32, '$BIAS=%.4f$' % BIAS, fontdict={\"fontsize\":16})\n",
" plt.text(0.5,31, '$RMSE=%.3f$' % RMSE, fontdict={\"fontsize\":16})\n",
" plt.xlim(0,35) # 设置x坐标轴的显示范围\n",
" plt.ylim(0,35) # 设置y坐标轴的显示范围\n",
" plt.savefig('./总孔体积.png',dpi=300, bbox_inches='tight',pad_inches=0)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9e1279b5-4b18-4f57-bdc0-197ba84cda33",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2d5bcdf5-cc45-40b7-8000-a0e41b18ef93",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('./rst/总孔体积_比表.csv')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8908a8b3-6bd5-4d71-b012-cbdd690b6a31",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" 真实值 | \n",
" 预测值 | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 184.000000 | \n",
" 184.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 267.871020 | \n",
" 272.776239 | \n",
"
\n",
" \n",
" | std | \n",
" 696.475264 | \n",
" 693.395059 | \n",
"
\n",
" \n",
" | min | \n",
" 0.060000 | \n",
" 0.069085 | \n",
"
\n",
" \n",
" | 25% | \n",
" 0.539250 | \n",
" 0.570501 | \n",
"
\n",
" \n",
" | 50% | \n",
" 0.877000 | \n",
" 0.889113 | \n",
"
\n",
" \n",
" | 75% | \n",
" 1.673250 | \n",
" 1.551479 | \n",
"
\n",
" \n",
" | max | \n",
" 3322.000000 | \n",
" 3225.575700 | \n",
"
\n",
" \n",
"
\n",
"