利用Pyhton画论文中的折线图

最新推荐文章于 2023-12-11 13:23:04 发布

原创最新推荐文章于 2023-12-11 13:23:04 发布 · 541 阅读

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CC 4.0 BY-SA版权

本文通过绘制用户CF和物品CF推荐算法的召回率随k值变化的曲线图，对比了两种算法在不同k值下的表现。实验结果显示，在特定k值范围内，用户CF算法的召回率略高于物品CF算法。

# encoding=utf-8
import matplotlib.pyplot as plt
from pylab import *                                 #支持中文
mpl.rcParams['font.sans-serif'] = ['SimHei']

names = ['20', '25', '30', '35', '40','45']
x = range(len(names))
y = [0.0718,0.0727,0.0748,0.0748,0.074,0.0749]
y1=[0.0668,0.0651,0.0671,0.0657,0.0664,0.0634]
#plt.plot(x, y, 'ro-')
#plt.plot(x, y1, 'bo-')
#pl.xlim(-1, 11)  # 限定横轴的范围
#pl.ylim(-1, 110)  # 限定纵轴的范围
plt.plot(x, y, marker='o', mec='r', mfc='w',label=u'userCF')
plt.plot(x, y1, marker='*', ms=10,label=u'itemCF')
plt.legend()  # 让图例生效
plt.xticks(x, names, rotation=45)
plt.margins(0)
plt.subplots_adjust(bottom=0.15)
plt.xlabel(u"k值") #X轴标签
plt.ylabel("RECALL") #Y轴标签
# plt.title("A simple plot") #标题

plt.show()

运行结果：