150 lines
4.8 KiB
Python
150 lines
4.8 KiB
Python
"""
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================================================================
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Comparing different hierarchical linkage methods on toy datasets
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================================================================
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This example shows characteristics of different linkage
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methods for hierarchical clustering on datasets that are
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"interesting" but still in 2D.
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The main observations to make are:
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- single linkage is fast, and can perform well on
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non-globular data, but it performs poorly in the
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presence of noise.
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- average and complete linkage perform well on
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cleanly separated globular clusters, but have mixed
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results otherwise.
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- Ward is the most effective method for noisy data.
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While these examples give some intuition about the
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algorithms, this intuition might not apply to very high
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dimensional data.
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"""
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print(__doc__)
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import time
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import warnings
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import cluster, datasets
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from sklearn.preprocessing import StandardScaler
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from itertools import cycle, islice
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np.random.seed(0)
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# %%
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# Generate datasets. We choose the size big enough to see the scalability
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# of the algorithms, but not too big to avoid too long running times
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n_samples = 1500
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noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5,
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noise=.05)
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noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)
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blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
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no_structure = np.random.rand(n_samples, 2), None
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# Anisotropicly distributed data
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random_state = 170
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X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)
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transformation = [[0.6, -0.6], [-0.4, 0.8]]
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X_aniso = np.dot(X, transformation)
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aniso = (X_aniso, y)
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# blobs with varied variances
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varied = datasets.make_blobs(n_samples=n_samples,
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cluster_std=[1.0, 2.5, 0.5],
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random_state=random_state)
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# %%
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# Run the clustering and plot
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# Set up cluster parameters
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plt.figure(figsize=(9 * 1.3 + 2, 14.5))
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plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,
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hspace=.01)
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plot_num = 1
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default_base = {'n_neighbors': 10,
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'n_clusters': 3}
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datasets = [
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(noisy_circles, {'n_clusters': 2}),
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(noisy_moons, {'n_clusters': 2}),
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(varied, {'n_neighbors': 2}),
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(aniso, {'n_neighbors': 2}),
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(blobs, {}),
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(no_structure, {})]
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for i_dataset, (dataset, algo_params) in enumerate(datasets):
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# update parameters with dataset-specific values
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params = default_base.copy()
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params.update(algo_params)
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X, y = dataset
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# normalize dataset for easier parameter selection
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X = StandardScaler().fit_transform(X)
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# ============
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# Create cluster objects
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# ============
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ward = cluster.AgglomerativeClustering(
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n_clusters=params['n_clusters'], linkage='ward')
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complete = cluster.AgglomerativeClustering(
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n_clusters=params['n_clusters'], linkage='complete')
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average = cluster.AgglomerativeClustering(
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n_clusters=params['n_clusters'], linkage='average')
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single = cluster.AgglomerativeClustering(
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n_clusters=params['n_clusters'], linkage='single')
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clustering_algorithms = (
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('Single Linkage', single),
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('Average Linkage', average),
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('Complete Linkage', complete),
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('Ward Linkage', ward),
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)
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for name, algorithm in clustering_algorithms:
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t0 = time.time()
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# catch warnings related to kneighbors_graph
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with warnings.catch_warnings():
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warnings.filterwarnings(
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"ignore",
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message="the number of connected components of the " +
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"connectivity matrix is [0-9]{1,2}" +
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" > 1. Completing it to avoid stopping the tree early.",
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category=UserWarning)
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algorithm.fit(X)
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t1 = time.time()
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if hasattr(algorithm, 'labels_'):
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y_pred = algorithm.labels_.astype(int)
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else:
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y_pred = algorithm.predict(X)
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plt.subplot(len(datasets), len(clustering_algorithms), plot_num)
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if i_dataset == 0:
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plt.title(name, size=18)
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colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
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'#f781bf', '#a65628', '#984ea3',
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'#999999', '#e41a1c', '#dede00']),
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int(max(y_pred) + 1))))
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plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])
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plt.xlim(-2.5, 2.5)
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plt.ylim(-2.5, 2.5)
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plt.xticks(())
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plt.yticks(())
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plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),
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transform=plt.gca().transAxes, size=15,
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horizontalalignment='right')
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plot_num += 1
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plt.show()
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