110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
"""
|
|
===========================================================
|
|
Hierarchical clustering: structured vs unstructured ward
|
|
===========================================================
|
|
|
|
Example builds a swiss roll dataset and runs
|
|
hierarchical clustering on their position.
|
|
|
|
For more information, see :ref:`hierarchical_clustering`.
|
|
|
|
In a first step, the hierarchical clustering is performed without connectivity
|
|
constraints on the structure and is solely based on distance, whereas in
|
|
a second step the clustering is restricted to the k-Nearest Neighbors
|
|
graph: it's a hierarchical clustering with structure prior.
|
|
|
|
Some of the clusters learned without connectivity constraints do not
|
|
respect the structure of the swiss roll and extend across different folds of
|
|
the manifolds. On the opposite, when opposing connectivity constraints,
|
|
the clusters form a nice parcellation of the swiss roll.
|
|
|
|
"""
|
|
|
|
# Authors : Vincent Michel, 2010
|
|
# Alexandre Gramfort, 2010
|
|
# Gael Varoquaux, 2010
|
|
# License: BSD 3 clause
|
|
|
|
import time as time
|
|
|
|
import matplotlib.pyplot as plt
|
|
|
|
# The following import is required
|
|
# for 3D projection to work with matplotlib < 3.2
|
|
import mpl_toolkits.mplot3d # noqa: F401
|
|
|
|
import numpy as np
|
|
|
|
from sklearn.cluster import AgglomerativeClustering
|
|
from sklearn.datasets import make_swiss_roll
|
|
|
|
# #############################################################################
|
|
# Generate data (swiss roll dataset)
|
|
n_samples = 1500
|
|
noise = 0.05
|
|
X, _ = make_swiss_roll(n_samples, noise=noise)
|
|
# Make it thinner
|
|
X[:, 1] *= 0.5
|
|
|
|
# #############################################################################
|
|
# Compute clustering
|
|
print("Compute unstructured hierarchical clustering...")
|
|
st = time.time()
|
|
ward = AgglomerativeClustering(n_clusters=6, linkage="ward").fit(X)
|
|
elapsed_time = time.time() - st
|
|
label = ward.labels_
|
|
print("Elapsed time: %.2fs" % elapsed_time)
|
|
print("Number of points: %i" % label.size)
|
|
|
|
# #############################################################################
|
|
# Plot result
|
|
fig = plt.figure()
|
|
ax = fig.add_subplot(111, projection="3d", elev=7, azim=-80)
|
|
ax.set_position([0, 0, 0.95, 1])
|
|
for l in np.unique(label):
|
|
ax.scatter(
|
|
X[label == l, 0],
|
|
X[label == l, 1],
|
|
X[label == l, 2],
|
|
color=plt.cm.jet(float(l) / np.max(label + 1)),
|
|
s=20,
|
|
edgecolor="k",
|
|
)
|
|
plt.title("Without connectivity constraints (time %.2fs)" % elapsed_time)
|
|
|
|
# #############################################################################
|
|
# Define the structure A of the data. Here a 10 nearest neighbors
|
|
from sklearn.neighbors import kneighbors_graph
|
|
|
|
connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)
|
|
|
|
# #############################################################################
|
|
# Compute clustering
|
|
print("Compute structured hierarchical clustering...")
|
|
st = time.time()
|
|
ward = AgglomerativeClustering(
|
|
n_clusters=6, connectivity=connectivity, linkage="ward"
|
|
).fit(X)
|
|
elapsed_time = time.time() - st
|
|
label = ward.labels_
|
|
print("Elapsed time: %.2fs" % elapsed_time)
|
|
print("Number of points: %i" % label.size)
|
|
|
|
# #############################################################################
|
|
# Plot result
|
|
fig = plt.figure()
|
|
ax = fig.add_subplot(111, projection="3d", elev=7, azim=-80)
|
|
ax.set_position([0, 0, 0.95, 1])
|
|
for l in np.unique(label):
|
|
ax.scatter(
|
|
X[label == l, 0],
|
|
X[label == l, 1],
|
|
X[label == l, 2],
|
|
color=plt.cm.jet(float(l) / np.max(label + 1)),
|
|
s=20,
|
|
edgecolor="k",
|
|
)
|
|
plt.title("With connectivity constraints (time %.2fs)" % elapsed_time)
|
|
|
|
plt.show()
|