sklearn/examples/cluster/plot_linkage_comparison.py

"""
================================================================
Comparing different hierarchical linkage methods on toy datasets
================================================================

This example shows characteristics of different linkage
methods for hierarchical clustering on datasets that are
"interesting" but still in 2D.

The main observations to make are:

- single linkage is fast, and can perform well on
  non-globular data, but it performs poorly in the
  presence of noise.
- average and complete linkage perform well on
  cleanly separated globular clusters, but have mixed
  results otherwise.
- Ward is the most effective method for noisy data.

While these examples give some intuition about the
algorithms, this intuition might not apply to very high
dimensional data.

"""

import time
import warnings
from itertools import cycle, islice

import matplotlib.pyplot as plt
import numpy as np

from sklearn import cluster, datasets
from sklearn.preprocessing import StandardScaler

# %%
# Generate datasets. We choose the size big enough to see the scalability
# of the algorithms, but not too big to avoid too long running times

n_samples = 1500
noisy_circles = datasets.make_circles(
    n_samples=n_samples, factor=0.5, noise=0.05, random_state=170
)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05, random_state=170)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=170)
rng = np.random.RandomState(170)
no_structure = rng.rand(n_samples, 2), None

# Anisotropicly distributed data
X, y = datasets.make_blobs(n_samples=n_samples, random_state=170)
transformation = [[0.6, -0.6], [-0.4, 0.8]]
X_aniso = np.dot(X, transformation)
aniso = (X_aniso, y)

# blobs with varied variances
varied = datasets.make_blobs(
    n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=170
)

# %%
# Run the clustering and plot

# 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()