将图例添加到散点图(PCA) [英] Add legend to scatter plot (PCA)

问题描述

我是python的新手，发现了这个出色的PCA biplot建议(

I am a newbie with python and found this excellent PCA biplot suggestion (Plot PCA loadings and loading in biplot in sklearn (like R's autoplot)). Now I tried to add a legend to the plot for the different targets. But the command plt.legend() doesn't work.

有一种简单的方法吗? 例如，上面链接中的虹膜数据和双足点代码.

Is there an easy way to do it? As an example, the iris data with the biplot code from the link above.

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.decomposition import PCA
import pandas as pd
from sklearn.preprocessing import StandardScaler

iris = datasets.load_iris()
X = iris.data
y = iris.target
#In general a good idea is to scale the data
scaler = StandardScaler()
scaler.fit(X)
X=scaler.transform(X)    

pca = PCA()
x_new = pca.fit_transform(X)

def myplot(score,coeff,labels=None):
    xs = score[:,0]
    ys = score[:,1]
    n = coeff.shape[0]
    scalex = 1.0/(xs.max() - xs.min())
    scaley = 1.0/(ys.max() - ys.min())
    plt.scatter(xs * scalex,ys * scaley, c = y)
    for i in range(n):
        plt.arrow(0, 0, coeff[i,0], coeff[i,1],color = 'r',alpha = 0.5)
        if labels is None:
            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, "Var"+str(i+1), color = 'g', ha = 'center', va = 'center')
        else:
            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color = 'g', ha = 'center', va = 'center')
plt.xlim(-1,1)
plt.ylim(-1,1)
plt.xlabel("PC{}".format(1))
plt.ylabel("PC{}".format(2))
plt.grid()

#Call the function. Use only the 2 PCs.
myplot(x_new[:,0:2],np.transpose(pca.components_[0:2, :]))
plt.show()

欢迎您提出任何有关PCA双刃剑的建议！ 还有其他代码，如果添加图例更容易！

Any suggestions for PCA biplots are welcome! Also other codes, if the adding of the legend is easier in another way!

推荐答案

我最近提出了一种向图例添加图例的简单方法，请参见

I recently proposed an easy way to add a legend to a scatter, see GitHub PR. This is still being discussed.

同时，您需要根据y中的唯一标签手动创建图例.对于每个对象，您都将创建一个Line2D对象，该对象的标记与散点图中使用的标记相同，并将它们作为参数提供给plt.legend.

In the meantime you need to manually create your legend from the unique labels in y. For each of them you'd create a Line2D object with the same marker as is used in the scatter plot and supply them as argument to plt.legend.

scatter = plt.scatter(xs * scalex,ys * scaley, c = y)
labels = np.unique(y)
handles = [plt.Line2D([],[],marker="o", ls="", 
                      color=scatter.cmap(scatter.norm(yi))) for yi in labels]
plt.legend(handles, labels)

这篇关于将图例添加到散点图(PCA)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！