## Jupyter Snippet CB2nd 08_clustering

Jupyter Snippet CB2nd 08_clustering

# 8.8. Detecting hidden structures in a dataset with clustering

from itertools import permutations
import numpy as np
import sklearn
import sklearn.decomposition as dec
import sklearn.cluster as clu
import sklearn.datasets as ds
import sklearn.model_selection as ms
import matplotlib.pyplot as plt
%matplotlib inline
X, y = ds.make_blobs(n_samples=200,
n_features=2,
centers=3,
cluster_std=1.5,
)
def relabel(cl):
"""Relabel a clustering with three clusters
to match the original classes."""
if np.max(cl) != 2:
return cl
perms = np.array(list(permutations((0, 1, 2))))
i = np.argmin([np.sum(np.abs(perm[cl] - y))
for perm in perms])
p = perms[i]
return p[cl]
def display_clustering(labels, title):
"""Plot the data points with the cluster
colors."""

# We relabel the classes when there are 3 clusters
labels = relabel(labels)
fig, axes = plt.subplots(1, 2, figsize=(8, 3),
sharey=True)
# Display the points with the true labels on the
# left, and with the clustering labels on the
# right.
for ax, c, title in zip(
axes,
[y, labels],
["True labels", title]):
ax.scatter(X[:, 0], X[:, 1], c=c, s=30,
linewidths=0, cmap=plt.cm.rainbow)
ax.set_title(title)
km = clu.KMeans()
km.fit(X)
display_clustering(km.labels_, "KMeans")

km = clu.KMeans(n_clusters=3)
km.fit(X)
display_clustering(km.labels_, "KMeans(3)")

fig, axes = plt.subplots(2, 3,
figsize=(10, 7),
sharex=True,
sharey=True)

axes[0, 0].scatter(X[:, 0], X[:, 1],
c=y, s=30,
linewidths=0,
cmap=plt.cm.rainbow)
axes[0, 0].set_title("True labels")

for ax, est in zip(axes.flat[1:], [
clu.SpectralClustering(3),
clu.AgglomerativeClustering(3),
clu.MeanShift(),
clu.AffinityPropagation(),
clu.DBSCAN(),
]):
est.fit(X)
c = relabel(est.labels_)
ax.scatter(X[:, 0], X[:, 1], c=c, s=30,
linewidths=0, cmap=plt.cm.rainbow)
ax.set_title(est.__class__.__name__)

# Fix the spacing between subplots.
fig.tight_layout()