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| import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pylab import *
plt.figure(figsize=(8.3,6))
label_font = {'family':'Arial','weight':'normal','size':22}
legend_font = {'family':'Arial','weight':'normal','size':15}
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.yticks(fontproperties = 'Arial', size = 24)
plt.xticks(fontproperties = 'Arial', size = 20)
plt.ylabel('$\mathregular{R^{2}}$'+' coefficient of determination',label_font)
plt.xlabel('Number of features',label_font)
tick_params(which='major',width=2,length=6)
ax=plt.gca()
bwith = 2
ax.spines['bottom'].set_linewidth(bwith)
ax.spines['top'].set_linewidth(bwith)
ax.spines['left'].set_linewidth(bwith)
ax.spines['right'].set_linewidth(bwith)
plt.ylim([0,1])
my_x_ticks = np.arange(1, 6, 1)
plt.xticks(my_x_ticks)
plt.title('Random forest regression',fontsize=20)
d = [1,2,3,4,5]
r2_rf = [0.822,0.888,0.964,0.964,0.964]
r2tr_rf = [0.8264,0.892,0.9629,0.964,0.9604]
r2te_rf = [0.5456,0.5919,0.7318,0.7362,0.7203]
plt.plot(d,r2_rf,'o',ls='solid',lw=1,markeredgecolor='black',markersize=8,label='Entire dataset(50 cases) without cross-validation')
plt.plot(d,r2tr_rf,'s',ls='solid',lw=1,markeredgecolor='black',markersize=8,label='Trainning dataset(45 cases)')
plt.plot(d,r2te_rf,'^',ls='solid',lw=1,markeredgecolor='black',markersize=8,label='Test dataset(5 cases)')
plt.legend(loc='best',prop =legend_font,fancybox=False,edgecolor='black')
plt.tight_layout()
plt.savefig('rfr.pdf',dpi = 300)
plt.show()
|
