树状图

将树状图可视化为 SVG 图像。

from IPython.display import SVG

import numpy as np

from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.hierarchy import Paris
from sknetwork.visualization import visualize_graph, visualize_bigraph
from sknetwork.visualization import visualize_dendrogram

图形

graph = karate_club(metadata=True)
adjacency = graph.adjacency
position = graph.position
labels = graph.labels

# graph
image = visualize_graph(adjacency, position, labels=labels)
SVG(image)

# hierarchical clustering
paris = Paris()
dendrogram = paris.fit_transform(adjacency)

# visualization
image = visualize_dendrogram(dendrogram)
SVG(image)

# add names, set colors
n = adjacency.shape[0]
image = visualize_dendrogram(dendrogram, names=np.arange(n), n_clusters=5, color='gray')
SVG(image)

# export
visualize_dendrogram(dendrogram, filename='dendrogram_karate_club')

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有向图

graph = painters(metadata=True)
adjacency = graph.adjacency
names = graph.names
position = graph.position

# graph
image = visualize_graph(adjacency, position, names)
SVG(image)

# hierarchical clustering
paris = Paris()
dendrogram = paris.fit_transform(adjacency)

# visualization
image = visualize_dendrogram(dendrogram, names, n_clusters=3, rotate=True)
SVG(image)

二部图

graph = movie_actor(metadata=True)
biadjacency = graph.biadjacency
names_row = graph.names_row
names_col = graph.names_col

# graph
image = visualize_bigraph(biadjacency, names_row, names_col)
SVG(image)

# hierarchical clustering
paris = Paris()
paris.fit(biadjacency)
dendrogram_row = paris.dendrogram_row_
dendrogram_col = paris.dendrogram_col_
dendrogram_full = paris.dendrogram_full_

# visualization
image = visualize_dendrogram(dendrogram_row, names_row, n_clusters=3, rotate=True)
SVG(image)

image = visualize_dendrogram(dendrogram_col, names_col, n_clusters=3, rotate=True)
SVG(image)