python-科研绘图系列（5）-用TSNE降维并可视化
1. 用TSNE 降维
import numpy as np
import matplotlib.pyplot as plt
from sklearn import manifold
import pickle
import time
import pandas as pd
def visual(X):
tsne = manifold.TSNE(n_components=2,init='pca', random_state=501)
X_tsne = tsne.fit_transform(X)
print("Org data dimension is {}. Embedded data dimension is {}".format(X.shape[-1], X_tsne.shape[-1]))
#'''嵌入空间可视化'''
x_min, x_max = X_tsne.min(0), X_tsne.max(0)
X_norm = (X_tsne - x_min) / (x_max - x_min)
return X_norm
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n_components=2 控制降维后的维度
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init=‘pca’ 用PCA 方法降维
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random_state=501 代表随机种子
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输入：需要降维的矩阵 X shape=(m,n) m:代表样本数量，n:代表样本长度
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返回：降维后的矩阵X_norm shape=(m,2)
2.画图
import numpy as np
import matplotlib.pyplot as plt
from sklearn import manifold
import pandas as pd
S_X1=np.random.random((200,200))#原始特征
class_num=10
#####生成标签
y_s=np.zeros((20*class_num))
for i in range(20* class_num):
y_s[i] = i // 20
###################
maker=['o','v','^','s','p','*','<','>','D','d','h','H']#设置散点形状
colors = ['black','tomato','yellow','cyan','blue', 'lime', 'r', 'violet','m','peru','olivedrab','hotpink']#设置散点颜色
Label_Com = ['S-1', 'T-1', 'S-2', 'T-2', 'S-3',
'T-3', 'S-4', 'T-4','S-5','T-5', 'S-6', 'T-6', 'S-7','T-7','S-8', 'T-8','S-9','T-9',
'S-10','T-10','S-11', 'T-11', 'S-12','T-12'] ##图例名称
### 设置字体格式
font1 = {'family' : 'Times New Roman',
'weight' : 'bold',
'size' : 32,
}
def visual(X):
tsne = manifold.TSNE(n_components=2,init='pca', random_state=501)
X_tsne = tsne.fit_transform(X)
print("Org data dimension is {}. Embedded data dimension is {}".format(X.shape[-1], X_tsne.shape[-1]))
#'''嵌入空间可视化'''
x_min, x_max = X_tsne.min(0), X_tsne.max(0)
X_norm = (X_tsne - x_min) / (x_max - x_min)
return X_norm
def plot_with_labels(S_lowDWeights,Trure_labels,name):
plt.cla()#清除当前图形中的当前活动轴,所以可以重复利用
# 降到二维了，分别给x和y
True_labels=Trure_labels.reshape((-1,1))
S_data=np.hstack((S_lowDWeights,True_labels))
S_data=pd.DataFrame({'x':S_data[:,0],'y':S_data[:,1],'label':S_data[:,2]})
for index in range(class_num):
X= S_data.loc[S_data['label'] == index]['x']
Y=S_data.loc[S_data['label'] == index]['y']
plt.scatter(X,Y,cmap='brg', s=100, marker=maker[0], c='', edgecolors=colors[index],alpha=0.65)
plt.xticks([]) # 去掉横坐标值
plt.yticks([]) # 去掉纵坐标值
#
plt.title(name,fontsize=32,fontweight='normal',pad=20)
fig = plt.figure(figsize=(10,10))
ax1 = fig.add_subplot(111)
plot_with_labels(visual(S_X1),y_s,'(a)')
plt.subplots_adjust(left=None, bottom=0.15, right=None, top=None,
wspace=0.1, hspace=0.15)
plt.legend(scatterpoints=1,labels = Label_Com, loc='best',labelspacing=0.4,columnspacing=0.4,markerscale=2,bbox_to_anchor=(0.9, 0),ncol=12,prop=font1,handletextpad=0.1)
# plt.savefig('./'+str(sour)+str(tar)+'.png', format='png',dpi=300, bbox_inches='tight')
plt.show(fig)
3.可视化结果
