将图例添加到散点图 (PCA)

Question

我是 python 的新手，发现了这个出色的 PCA 双标图建议 ()。现在我尝试为不同的目标添加一个图例。但是命令 plt.legend() 不起作用。

有简单的方法吗？例如，虹膜数据与上面 link 中的双标代码。

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()

欢迎对 PCA 双标图提出任何建议！还有其他代码，如果通过其他方式更容易添加图例！

Answer 1

我最近提出了一种向散点图添加图例的简单方法，请参阅 GitHub PR。这仍在讨论中。

同时，您需要根据 y 中的唯一标签手动创建图例。对于它们中的每一个，您将创建一个 Line2D 对象，其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.

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)

Answer 2

尝试 pca library。这将绘制解释方差，并创建双标图。

pip install pca

from pca import pca

# Initialize to reduce the data up to the number of componentes that explains 95% of the variance.
model = pca(n_components=0.95)

# Or reduce the data towards 2 PCs
model = pca(n_components=2)

# Load example dataset
import pandas as pd
import sklearn
from sklearn.datasets import load_iris
X = pd.DataFrame(data=load_iris().data, columns=load_iris().feature_names, index=load_iris().target)

# Fit transform
results = model.fit_transform(X)

# Plot explained variance
fig, ax = model.plot()

# Scatter first 2 PCs
fig, ax = model.scatter()

# Make biplot with the number of features
fig, ax = model.biplot(n_feat=4)

结果是一个字典，其中包含许多关于 PC、负载等的统计信息

将图例添加到散点图 (PCA)

Add legend to scatter plot (PCA)

python

matplotlib

legend

pca

biplot