pyclustering 当矩阵具有三个以上的维度时可视化 xmeans

Question

我正在尝试使用 pyclustering 库中的 xmeans 对一些数据进行聚类和可视化。我直接从文档中的example复制代码，

from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.xmeans import xmeans
from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import SIMPLE_SAMPLES
sample = X # read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)    
# Prepare initial centers - amount of initial centers defines amount of clusters from which X-Means will
# start analysis.
amount_initial_centers = 2
initial_centers = kmeans_plusplus_initializer(sample, amount_initial_centers).initialize()
# Create instance of X-Means algorithm. The algorithm will start analysis from 2 clusters, the maximum
# number of clusters that can be allocated is 20.
xmeans_instance = xmeans(sample, initial_centers, 20)
xmeans_instance.process()
# Extract clustering results: clusters and their centers
clusters = xmeans_instance.get_clusters()
centers = xmeans_instance.get_centers()
# Print total sum of metric errors
print("Total WCE:", xmeans_instance.get_total_wce())
# Visualize clustering results
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.append_cluster(centers, None, marker='*', markersize=10)
visualizer.show()

唯一的区别是我分配给 sample 矩阵 X 的值而不是加载样本数据集。

当我尝试可视化聚类结果时出现此错误：

Only objects with size dimension 1 (1D plot), 2 (2D plot) or 3 (3D plot) can be displayed. For multi-dimensional data use 'cluster_visualizer_multidim'.

我的X矩阵是这样生成的：

features = ["I", "Iu", other 7 column names]
data = df[features]
...
X = scaler.fit_transform(data)

有没有办法可视化集群并一次只绘制 two/three 个特征？

我在文档中找不到任何内容。

我试过这个：

visualizer.append_clusters(clusters, sample[:,[0,1]])

为了只可视化前两个特征而得到这个错误

Only clusters with the same dimension of objects can be displayed on canvas.

编辑：

我按照 annoviko 的回答中的建议更新了代码，但现在我收到以下错误：

ValueError                                Traceback (most recent call last)
<ipython-input-69-6fd7d2ce5fcd> in <module>
     20 visualizer.append_clusters(clusters, X)
     21 visualizer.append_cluster(centers, None, marker='*', markersize=10)
---> 22 visualizer.show(pair_filter=[[0, 1], [0, 2]])

/usr/local/lib/python3.8/site-packages/pyclustering/cluster/__init__.py in show(self, pair_filter, **kwargs)
    224             raise ValueError("There is no non-empty clusters for visualization.")
    225 
--> 226         cluster_data = self.__clusters[0].data or self.__clusters[0].cluster
    227         dimension = len(cluster_data[0])
    228 

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

它由 visualizer.show() 引发，即使我从函数调用中删除 pair_filter 也会发生。

Answer 1

符合你得到的错误：

Only objects with size dimension 1 (1D plot), 2 (2D plot) or 3 (3D plot) can be displayed. For multi-dimensional data use 'cluster_visualizer_multidim'.

你必须像上面提到的那样使用 cluster_visualizer_multidim。有一个带有示例的文档（pyclustering 0.10.1）：https://pyclustering.github.io/docs/0.10.1/html/dc/d6b/classpyclustering_1_1cluster_1_1cluster__visualizer__multidim.html

比如你有一个数据(D > 3)，你想显示(x0, x1)和(x0, x2)，那么你可以这样显示：

visualizer = cluster_visualizer_multidim()
visualizer.append_clusters(clusters, sample_4d)
visualizer.show(pair_filter=[[0, 1], [0, 2]])

其中 pair_filter 指定应显示哪些功能。在上面的示例中，它将仅显示 (x0, x1) - [0, 1] 和 (x0, x2) - [0, 2].

因此，在您必须仅显示前两个功能的特定情况下，它应该是：

visualizer = cluster_visualizer_multidim()
visualizer.append_clusters(clusters, sample)
visualizer.show(pair_filter=[[0, 1]])

我认为我必须使错误更具可读性，并建议在第一句中使用另一个 class。让我知道它是否有帮助（如果它仍然与您相关）。

pyclustering 当矩阵具有三个以上的维度时可视化 xmeans

pyclustering visualising xmeans when the matrix has more than three dimensions

python

plot

cluster-analysis

k-means

xmeans