89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
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"""
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=============================================================================
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Various Agglomerative Clustering on a 2D embedding of digits
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=============================================================================
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An illustration of various linkage option for agglomerative clustering on
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a 2D embedding of the digits dataset.
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The goal of this example is to show intuitively how the metrics behave, and
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not to find good clusters for the digits. This is why the example works on a
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2D embedding.
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What this example shows us is the behavior "rich getting richer" of
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agglomerative clustering that tends to create uneven cluster sizes.
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This behavior is pronounced for the average linkage strategy,
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that ends up with a couple of clusters with few datapoints.
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The case of single linkage is even more pathologic with a very
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large cluster covering most digits, an intermediate size (clean)
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cluster with most zero digits and all other clusters being drawn
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from noise points around the fringes.
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The other linkage strategies lead to more evenly distributed
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clusters that are therefore likely to be less sensible to a
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random resampling of the dataset.
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"""
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# Authors: Gael Varoquaux
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# License: BSD 3 clause (C) INRIA 2014
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from time import time
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import numpy as np
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from matplotlib import pyplot as plt
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from sklearn import datasets, manifold
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digits = datasets.load_digits()
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X, y = digits.data, digits.target
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n_samples, n_features = X.shape
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np.random.seed(0)
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# ----------------------------------------------------------------------
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# Visualize the clustering
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def plot_clustering(X_red, labels, title=None):
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x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0)
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X_red = (X_red - x_min) / (x_max - x_min)
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plt.figure(figsize=(6, 4))
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for digit in digits.target_names:
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plt.scatter(
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*X_red[y == digit].T,
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marker=f"${digit}$",
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s=50,
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c=plt.cm.nipy_spectral(labels[y == digit] / 10),
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alpha=0.5,
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)
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plt.xticks([])
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plt.yticks([])
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if title is not None:
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plt.title(title, size=17)
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plt.axis("off")
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plt.tight_layout(rect=[0, 0.03, 1, 0.95])
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# ----------------------------------------------------------------------
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# 2D embedding of the digits dataset
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print("Computing embedding")
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X_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X)
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print("Done.")
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from sklearn.cluster import AgglomerativeClustering
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for linkage in ("ward", "average", "complete", "single"):
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clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10)
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t0 = time()
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clustering.fit(X_red)
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print("%s :\t%.2fs" % (linkage, time() - t0))
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plot_clustering(X_red, clustering.labels_, "%s linkage" % linkage)
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plt.show()
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