在python中绘制sklearn集群

问题描述

我有以下通过亲和力传播获得的sklearn簇.

I have the following sklearn clusters obtained using affinity propagation.

import sklearn.cluster
import numpy as np

sims =  np.array([[0, 17, 10, 32, 32], [18, 0, 6, 20, 15], [10, 8, 0, 20, 21], [30, 16, 20, 0, 17], [30, 15, 21, 17, 0]])

affprop = sklearn.cluster.AffinityPropagation(affinity="precomputed", damping=0.5)
affprop.fit(sims)

cluster_centers_indices = affprop.cluster_centers_indices_
labels = affprop.labels_
#number of clusters
n_clusters_ = len(cluster_centers_indices)

现在，我想绘制群集的输出.我是sklearn的新手.请给我建议一个合适的方法来在python中绘制聚类.可以用熊猫数据框来做到这一点吗?

Now I want to plot the output of the clusters. I am new to sklearn. Please suggest me a suitable approach to plot the clusters in python. Is it possible to do this with pandas dataframes?

我使用了直接在sklearn中编码，如下所示，如@MohammedKashif所指出的那样.

I used the code in sklearn directly as follows as pointed by @MohammedKashif.

import sklearn.cluster

import numpy as np

sims =  np.array([[0, 17, 10, 32, 32], [18, 0, 6, 20, 15], [10, 8, 0, 20, 21], [30, 16, 20, 0, 17], [30, 15, 21, 17, 0]])

affprop = sklearn.cluster.AffinityPropagation(affinity="precomputed", damping=0.5)
affprop.fit(sims)

cluster_centers_indices = affprop.cluster_centers_indices_
print(cluster_centers_indices)
labels = affprop.labels_
n_clusters_ = len(cluster_centers_indices)
print(n_clusters_)

import matplotlib.pyplot as plt
from itertools import cycle

plt.close('all')
plt.figure(1)
plt.clf()

colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
for k, col in zip(range(n_clusters_), colors):
    class_members = labels == k
    cluster_center = sims[cluster_centers_indices[k]]
    plt.plot(sims[class_members, 0], sims[class_members, 1], col + '.')
    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
             markeredgecolor='k', markersize=14)
    for x in sims[class_members]:
        plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)

plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

但是，我得到的输出有点奇怪(第二个聚类点(绿色)在蓝线上.因此，我不认为它应该作为单独的聚类，也不应在蓝色中)簇).如果我在代码中有任何错误，请告诉我.

However, the output I get is bit weird as follows (The second cluster point (green) is on the blue line. Hence, I don't think it should be clustered as a separate one and should also be in the blue cluster). Please let me know if I have made any mistakes in the code.

编辑2

正如σηγ所指出的，我添加了:

As pointed by σηγ I added:

se = SpectralEmbedding(n_components=2, affinity='precomputed')
X = se.fit_transform(sims)
print(X)

但是，对于数组np.array([[0, 17, 10, 32, 32], [0, 17, 10, 32, 32], [0, 17, 10, 32, 33], [0, 17, 10, 32, 32], [0, 17, 10, 32, 32]])，它给了我3分，如下所示.这让我感到困惑，因为所有5个数组都代表一个点.

However, for the array np.array([[0, 17, 10, 32, 32], [0, 17, 10, 32, 32], [0, 17, 10, 32, 33], [0, 17, 10, 32, 32], [0, 17, 10, 32, 32]]) it gave me 3 points as shown below. That confuses me because all the 5 arrays represents one point.

请帮助我.

推荐答案

在上一个示例之后，我将尝试以下操作:

Following the previous example, I would try something like this:

import sklearn.cluster
from sklearn.manifold import SpectralEmbedding
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle

sims =  np.array([[0, 17, 10, 32, 32], [18, 0, 6, 20, 15], [10, 8, 0, 20, 21], [30, 16, 20, 0, 17], [30, 15, 21, 17, 0]])

affprop = sklearn.cluster.AffinityPropagation(affinity="precomputed", damping=0.5)
affprop.fit(sims)

cluster_centers_indices = affprop.cluster_centers_indices_
print(cluster_centers_indices)
labels = affprop.labels_
n_clusters_ = len(cluster_centers_indices)
print(n_clusters_)

se = SpectralEmbedding(n_components=2, affinity='precomputed')
X = se.fit_transform(sims)

plt.close('all')
plt.figure(1)
plt.clf()

colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
for k, col in zip(range(n_clusters_), colors):
    class_members = labels == k
    cluster_center = X[cluster_centers_indices[k]]
    plt.plot(X[class_members, 0], X[class_members, 1], col + '.')
    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
             markeredgecolor='k', markersize=14)
    for x in X[class_members]:
        plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)

plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

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