We will now perform hierarchical clustering on the toy datasets that we loaded in step 1. We will use different linkage methods such as single, average, complete, and ward to build our clusters.
## Set up cluster parameters
plt.figure(figsize=(9 * 1.3 + 2, 14.5))
plt.subplots_adjust(
left=0.02, right=0.98, bottom=0.001, top=0.96, wspace=0.05, hspace=0.01
)
plot_num = 1
default_base = {"n_neighbors": 10, "n_clusters": 3}
datasets = [
(noisy_circles, {"n_clusters": 2}),
(noisy_moons, {"n_clusters": 2}),
(varied, {"n_neighbors": 2}),
(aniso, {"n_neighbors": 2}),
(blobs, {}),
(no_structure, {}),
]
for i_dataset, (dataset, algo_params) in enumerate(datasets):
## update parameters with dataset-specific values
params = default_base.copy()
params.update(algo_params)
X, y = dataset
## normalize dataset for easier parameter selection
X = StandardScaler().fit_transform(X)
## ============
## Create cluster objects
## ============
ward = cluster.AgglomerativeClustering(
n_clusters=params["n_clusters"], linkage="ward"
)
complete = cluster.AgglomerativeClustering(
n_clusters=params["n_clusters"], linkage="complete"
)
average = cluster.AgglomerativeClustering(
n_clusters=params["n_clusters"], linkage="average"
)
single = cluster.AgglomerativeClustering(
n_clusters=params["n_clusters"], linkage="single"
)
clustering_algorithms = (
("Single Linkage", single),
("Average Linkage", average),
("Complete Linkage", complete),
("Ward Linkage", ward),
)
for name, algorithm in clustering_algorithms:
t0 = time.time()
## catch warnings related to kneighbors_graph
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="the number of connected components of the "
+ "connectivity matrix is [0-9]{1,2}"
+ " > 1. Completing it to avoid stopping the tree early.",
category=UserWarning,
)
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, "labels_"):
y_pred = algorithm.labels_.astype(int)
else:
y_pred = algorithm.predict(X)
plt.subplot(len(datasets), len(clustering_algorithms), plot_num)
if i_dataset == 0:
plt.title(name, size=18)
colors = np.array(
list(
islice(
cycle(
[
"#377eb8",
"#ff7f00",
"#4daf4a",
"#f781bf",
"#a65628",
"#984ea3",
"#999999",
"#e41a1c",
"#dede00",
]
),
int(max(y_pred) + 1),
)
)
)
plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])
plt.xlim(-2.5, 2.5)
plt.ylim(-2.5, 2.5)
plt.xticks(())
plt.yticks(())
plt.text(
0.99,
0.01,
("%.2fs" % (t1 - t0)).lstrip("0"),
transform=plt.gca().transAxes,
size=15,
horizontalalignment="right",
)
plot_num += 1
plt.show()