X = masker.fit_transform(dataset.func[0])
# Encode labels as integers
df = pd.read_csv(dataset.session_target[0], sep=" ")
target, labels = pd.factorize(df.labels.values)
y = pd.DataFrame({l: (target == i).astype(int) for i, l in enumerate(labels)})
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=TSNE(2, random_state=1))
graph = mapper.map(lens, X=X, cover=Cover(20, 0.5), clusterer=DBSCAN(eps=20.))
# Visualize the stages of Mapper
fig, axes = visualize_mapper_stages(dataset,
y=target,
lens=lens,
graph=graph,
cover=mapper.cover,
node_size=20,
edge_size=0.5,
edge_color='gray',
layout="kamada_kawai",
figsize=(16, 4))
plt.savefig("mapper_stages.png", dpi=600, background='transparent')
plt.show()
# Visualize the shape graph using DyNeuSR's DyNeuGraph
dG = DyNeuGraph(G=graph, y=y)
dG.visualize('dyneusr_haxby_decoding.html', port=8800)
webbrowser.open(dG.HTTP.url)
X = dataset.data
y = dataset.target
# Define projections to compare
projections = ([0], [0, 1], [1, 2], [0, 2])
# Compare different sets of columns as lenses
for projection in projections:
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=projection)
graph = mapper.map(lens, X, nr_cubes=4, overlap_perc=0.3)
# Visualize the stages of Mapper
fig, axes = visualize_mapper_stages(dataset,
lens=lens,
graph=graph,
cover=mapper.cover,
layout="spectral",
figsize=(16, 4))
# Save each figure
plt.savefig("mapper_lens_{}.png".format("_".join(
str(_) for _ in projection)),
dpi=600,
background='transparent')
# Show all figures
plt.show()