使用热图先试试iris数据集
In [1]:
# 加载库
from sklearn.datasets import load_iris
from sklearn.preprocessing import scale
import numpy as np
import matplotlib.pyplot as plt
In [2]:
# 加载iris数据集
data = load_iris()
x = data['data']
y = data['target']
col_names = data['feature_names']
In [3]:
# 根据平均值对数据进行缩放
x = scale(x,with_std=False)
x_ = x[1:26]
y_labels = range(1,26)
In [5]:
# 绘制热图
plt.close('all')
plt.figure(1)
fig,ax = plt.subplots()
ax.pcolor(x_,cmap=plt.cm.Greens,edgecolor='k')
ax.set_xticks(np.arange(0,x_.shape[1])+0.5)
ax.set_yticks(np.arange(0,x_.shape[0])+0.5)
ax.xaxis.tick_top()
ax.yaxis.tick_left()
ax.set_xticklabels(col_names,minor=False,fontsize=10)
ax.set_yticklabels(y_labels,minor=False,fontsize=10)
plt.show()
热图的另一种有趣用法是将变量通过各自的类分离开来
In [8]:
x1 = x[0:50]
x2 = x[50:100]
x3 = x[100:150]
x1 = scale(x1,with_std=False)
x2 = scale(x2,with_std=False)
x3 = scale(x3,with_std=False)
plt.close('all')
plt.figure(2)
fig,(ax1,ax2,ax3) = plt.subplots(3,sharex=True,sharey=True)
y_labels = range(1,51)
ax1.set_xticks(np.arange(0,x1.shape[1])+0.5)
ax1.set_yticks(np.arange(0,50,10))
ax1.xaxis.tick_bottom()
ax1.set_xticklabels(col_names,minor=False,fontsize=2)
ax1.pcolor(x1,cmap=plt.cm.Greens,edgecolor='k')
ax1.set_title(data['target_names'][0])
ax2.pcolor(x2,cmap=plt.cm.Greens,edgecolor='k')
ax2.set_title(data['target_names'][1])
ax3.pcolor(x3,cmap=plt.cm.Greens,edgecolor='k')
ax3.set_title(data['target_names'][2])
plt.show()