How to perform Spectral Clustering on 3 circles dataset with three different classes-CodePudding

I want to perform spectral clustering on the 3 circles dataset that I have generated using make circles as shown in the figure. All the three circles are of different classes.

from sklearn.datasets import make_circles
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn.cluster import SpectralClustering
import matplotlib.pyplot as plt
import pylab as pl
import networkx as nx
X_small, y_small = make_circles(n_samples=(100,200), random_state=3, 
noise=0.07, factor = 0.7)
X_large, y_large = make_circles(n_samples=(100,200), random_state=3, 
noise=0.07, factor = 0.4)
y_large[y_large==1] = 2
df = pd.DataFrame(np.vstack([X_small,X_large]),columns=['x1','x2'])
df['label'] = np.hstack([y_small,y_large])
df.label.value_counts()
sns.scatterplot(data=df,x='x1',y='x2',hue='label',style='label',palette="bright")

CodePudding user response：

Since I can't flag this question as duplicate (the

Resulting clusters:

Edit: to use different markers to identify true classes (colors already indicating the clustering classes), as asked by OP in the comments. We unfortunately cannot use an array for markers (as for colors) to produce the plot in a single line of code, this is because marker does not accept a list as input (discussed here).

Edit2: added motivation for the use of np.expand_dims(...,axis=1) and some explanation for the plt.scatter() lines, as asked by OP in the comments.