我有一个包含 9 列的数据集。 7 个特征用于特征选择，其中一个用于分类。 我使用 tsne 库进行特征选择，以查看我的数据可以分类多少。tsne 的结果如图所示。
但是，我想以另一种方式可视化我的数据。我想根据 f1 (id) 列为每个观察值设置颜色。例如 ：
f1(id) f2 f3 ... f9(class label)
1 66 77 ... A
1 44 88 ... A
2 33 55 ... B
2 77 88 .. B
colors come from f1 and shapes come from f9
. I do not know how to do it! I would appreciate for your comments or give me some references to learn more about visualization part.
this is my code:
plt.scatter(visualize_x, visualize_y, c= y,marker='^', cmap=plt.cm.get_cmap("jet", 10))
这是你所追求的类型吗？
from matplotlib import pyplot as plt
#generate a list of markers and another of colors
markers = ["." , "," , "o" , "v" , "^" , "<", ">"]
colors = ['r','g','b','c','m', 'y', 'k']
#make a sample dataset
x = np.arange(0,10) #test x values.. every feature gets the same x values but you can generalize this
y = [s*x for s in np.arange(7)] #generate 7 arrays of y values
for i in range(7): #for each of the 7 features
mi = markers[i] #marker for ith feature
xi = x #x array for ith feature .. here is where you would generalize different x for every feature
yi = y[i] #y array for ith feature
ci = colors[i] #color for ith feature
plt.scatter(xi,yi,marker=mi, color=ci)
plt.show()