Nearest neighbors
This notebook illustrates the classification of the nodes of a graph by the k-nearest neighbors algorithm, using the labels known for some nodes.
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from IPython.display import SVG
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import numpy as np
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from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.classification import NNClassifier, get_accuracy_score
from sknetwork.embedding import Spectral
from sknetwork.visualization import svg_graph, visualize_bigraph
Graphs
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graph = karate_club(metadata=True)
adjacency = graph.adjacency
position = graph.position
labels_true = graph.labels
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labels = {i: labels_true[i] for i in [0, 33]}
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# Nearest neighbors with cosine similarity
classifier = NNClassifier(n_neighbors=1)
labels_pred = classifier.fit_predict(adjacency, labels)
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accuracy = get_accuracy_score(labels_true, labels_pred)
np.round(accuracy, 2)
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0.82
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image = svg_graph(adjacency, position, labels=labels_pred, seeds=labels)
SVG(image)
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# Nearest neighbors in embedding space
classifier = NNClassifier(n_neighbors=1, embedding_method=Spectral(2))
labels_pred = classifier.fit_predict(adjacency, labels)
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accuracy = get_accuracy_score(labels_true, labels_pred)
np.round(accuracy, 2)
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0.97
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image = svg_graph(adjacency, position, labels=labels_pred, seeds=labels)
SVG(image)
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# distribution over labels
labels = {i: labels_true[i] for i in [0, 1, 30, 33]}
classifier = NNClassifier(n_neighbors=3, embedding_method=Spectral(2))
classifier.fit(adjacency, labels)
probs = classifier.predict_proba()
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scores = probs[:,1]
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image = svg_graph(adjacency, position, scores=scores, seeds=labels)
SVG(image)
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Directed graphs
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graph = painters(metadata=True)
adjacency = graph.adjacency
position = graph.position
names = graph.names
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rembrandt = 5
klimt = 6
cezanne = 11
labels = {cezanne: 0, rembrandt: 1, klimt: 2}
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classifier = NNClassifier(n_neighbors=2, embedding_method=Spectral(3))
labels_pred = classifier.fit_predict(adjacency, labels)
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image = svg_graph(adjacency, position, names, labels=labels_pred, seeds=labels)
SVG(image)
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# probability distribution over labels (here for scores for the label of Cezanne)
probs = classifier.predict_proba()
scores = probs[:,0]
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image = svg_graph(adjacency, position, names, scores=scores, seeds=[cezanne])
SVG(image)
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Bipartite graphs
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graph = movie_actor(metadata=True)
biadjacency = graph.biadjacency
names_row = graph.names_row
names_col = graph.names_col
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inception = 0
drive = 3
budapest = 8
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labels_row = {inception: 0, drive: 1, budapest: 2}
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classifier = NNClassifier(n_neighbors=2, embedding_method=Spectral(3))
classifier.fit(biadjacency, labels_row)
labels_row_pred = classifier.labels_row_
labels_col_pred = classifier.labels_col_
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inception = 0
drive = 3
budapest = 8
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labels_row = {inception: 0, drive: 1, budapest: 2}
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classifier = NNClassifier(n_neighbors=2, embedding_method=Spectral(3))
classifier.fit(biadjacency, labels_row)
labels_row_pred = classifier.labels_row_
labels_col_pred = classifier.labels_col_
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image = visualize_bigraph(biadjacency, names_row, names_col, labels_row_pred, labels_col_pred, seeds_row=labels_row)
SVG(image)
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# probability distribution over labels
probs_row = classifier.predict_proba()
probs_col = classifier.predict_proba(columns=True)
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scores_row = probs_row[:,1]
scores_col = probs_col[:,1]
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image = visualize_bigraph(biadjacency, names_row, names_col, scores_row=scores_row, scores_col=scores_col,
seeds_row=labels_row)
SVG(image)
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