如何在 3D 绘图（熊猫）中指定 kmeans 簇的颜色？

Posted 2023-03-12

【中文标题】如何在 3D 绘图（熊猫）中指定 kmeans 簇的颜色？

【英文标题】：How can I specify the color of the kmeans clusters in 3D plot (Pandas)?

【发布时间】：2019-01-30 04:10:10

【问题描述】：

我无法用所需的彩色集群绘制图形。 每个点都属于一个特定的集群，因此每个集群应该有特定的颜色，但如图所示，我无法获得所需的颜色。如何修改代码以获得预期的结果和漂亮的集群。

pca_ = PCA(n_components=3)
X_Demo_fit_pca = pca_.fit_transform(Demo_df_Processed)

kmeans_PCA = KMeans(n_clusters=4, init='k-means++', max_iter= 300, n_init= 10, random_state= 3)

y_kmeans_PCA = kmeans_PCA.fit_predict(X_Demo_fit_pca)
y_kmeans_PCA
Demo_df_Processed.head()



fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X_Demo_fit_pca[:,0],X_Demo_fit_pca[:,1],X_Demo_fit_pca[:,2], c=y,
            edgecolor='k', s=40, alpha = 0.5)



ax.set_title("First three PCA directions")
ax.set_xlabel("Educational_Degree")
ax.set_ylabel("Gross_Monthly_Salary")
ax.set_zlabel("Claim_Rate")
ax.dist = 10

ax.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], kmeans.cluster_centers_[:,2], 
           s = 100, c = 'r', label = 'Centroid')

plt.autoscale(enable=True, axis='x', tight=True)


plt.show()

Kmeans Clustering Image

【问题讨论】：

你的第一个ax.scatter()需要c=y_kmeans_PCA

【参考方案1】：

有了一些随机生成的数据和代码中的一些更改，我认为这就是您想要的（特定集群中的每个数据点具有相同的颜色）：

import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# randomly generate some data with 4 distinct clusters, use your own data here    
Demo_df_Processed = pd.DataFrame(np.random.randint(-2100,-2000,size=(100, 4)), columns=list('ABCD'))
Demo_df_Processed = Demo_df_Processed.append(pd.DataFrame(np.random.randint(-600,-500,size=(100, 4)), columns=list('ABCD')))
Demo_df_Processed = Demo_df_Processed.append(pd.DataFrame(np.random.randint(500,600,size=(100, 4)), columns=list('ABCD')))
Demo_df_Processed = Demo_df_Processed.append(pd.DataFrame(np.random.randint(2000,2100,size=(100, 4)), columns=list('ABCD')))

pca_ = PCA(n_components=3)
X_Demo_fit_pca = pca_.fit_transform(Demo_df_Processed)

kmeans_PCA = KMeans(n_clusters=4, init='k-means++', max_iter= 300, n_init= 10, random_state= 3)

y_kmeans_PCA = kmeans_PCA.fit_predict(X_Demo_fit_pca)
y_kmeans_PCA

fig = plt.figure(figsize=(20,10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X_Demo_fit_pca[:,0],X_Demo_fit_pca[:,1],X_Demo_fit_pca[:,2], 
            c=y_kmeans_PCA, cmap='viridis',
            edgecolor='k', s=40, alpha = 0.5)


ax.set_title("First three PCA directions")
ax.set_xlabel("Educational_Degree")
ax.set_ylabel("Gross_Monthly_Salary")
ax.set_zlabel("Claim_Rate")
ax.dist = 10

ax.scatter(kmeans_PCA.cluster_centers_[:,0], kmeans_PCA.cluster_centers_[:,1], 
           kmeans_PCA.cluster_centers_[:,2], 
           s = 300, c = 'r', marker='*', label = 'Centroid')

plt.autoscale(enable=True, axis='x', tight=True)    

plt.show()