传播

此笔记本演示了使用标签传播对图节点进行分类。

[1]:

from IPython.display import SVG

[2]:

import numpy as np

[3]:

from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.classification import Propagation
from sknetwork.visualization import svg_graph, visualize_bigraph

图

[4]:

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

[5]:

labels = {i: labels_true[i] for i in [0, 33]}

[6]:

propagation = Propagation()
labels_pred = propagation.fit_predict(adjacency, labels)

[7]:

image = svg_graph(adjacency, position, labels=labels_pred, seeds=labels)
SVG(image)

[7]:

../../_images/tutorials_classification_propagation_9_0.svg

[8]:

# probability distribution over labels
label = 1
probs = propagation.predict_proba()
scores = probs[:,label]

[9]:

image = svg_graph(adjacency, position, scores=scores, seeds=labels)
SVG(image)

[9]:

../../_images/tutorials_classification_propagation_11_0.svg

有向图

[10]:

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

[11]:

rembrandt = 5
klimt = 6
cezanne = 11
labels = {cezanne: 0, rembrandt: 1, klimt: 2}

[12]:

propagation = Propagation()
labels_pred = propagation.fit_predict(adjacency, labels)

[13]:

image = svg_graph(adjacency, position, names, labels=labels_pred, seeds=labels)
SVG(image)

[13]:

../../_images/tutorials_classification_propagation_16_0.svg

[14]:

# probability distribution over labels
probs = propagation.predict_proba(())
scores = probs[:,0]

[15]:

image = svg_graph(adjacency, position, names, scores=scores, seeds=[cezanne])
SVG(image)

[15]:

../../_images/tutorials_classification_propagation_18_0.svg

二部图

[16]:

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

[17]:

inception = 0
drive = 3
budapest = 8

[18]:

labels_row = {inception: 0, drive: 1, budapest: 2}

[19]:

propagation = Propagation()
propagation.fit(biadjacency, labels_row)
labels_row_pred = propagation.labels_row_
labels_col_pred = propagation.labels_col_

[20]:

image = visualize_bigraph(biadjacency, names_row, names_col, labels_row_pred, labels_col_pred, seeds_row=labels_row)
SVG(image)

[20]:

../../_images/tutorials_classification_propagation_24_0.svg

[21]:

# probability distribution over labels
probs_row = propagation.predict_proba()
probs_col = propagation.predict_proba(columns=True)

[22]:

scores_row = probs_row[:,1]
scores_col = probs_col[:,1]

[23]:

image = visualize_bigraph(biadjacency, names_row, names_col, scores_row=scores_row, scores_col=scores_col,
                    seeds_row=labels_row)
SVG(image)

[23]:

../../_images/tutorials_classification_propagation_27_0.svg