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FEA OPTICS: add extract_xi method (#12077)
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""" | ||
=================================== | ||
Demo of OPTICS clustering algorithm | ||
=================================== | ||
Finds core samples of high density and expands clusters from them. | ||
This example uses data that is generated so that the clusters have | ||
different densities. | ||
The :class:`sklearn.cluster.OPTICS` is first used with its Xi cluster detection | ||
method, and then setting specific thresholds on the reachability, which | ||
corresponds to :class:`sklearn.cluster.DBSCAN`. We can see that the different | ||
clusters of OPTICS's Xi method can be recovered with different choices of | ||
thresholds in DBSCAN. | ||
""" | ||
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# Authors: Shane Grigsby <refuge@rocktalus.com> | ||
# Adrin Jalali <adrin.jalali@gmail.com> | ||
# License: BSD 3 clause | ||
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from sklearn.cluster import OPTICS, cluster_optics_dbscan | ||
import matplotlib.gridspec as gridspec | ||
import matplotlib.pyplot as plt | ||
import numpy as np | ||
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# Generate sample data | ||
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np.random.seed(0) | ||
n_points_per_cluster = 250 | ||
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C1 = [-5, -2] + .8 * np.random.randn(n_points_per_cluster, 2) | ||
C2 = [4, -1] + .1 * np.random.randn(n_points_per_cluster, 2) | ||
C3 = [1, -2] + .2 * np.random.randn(n_points_per_cluster, 2) | ||
C4 = [-2, 3] + .3 * np.random.randn(n_points_per_cluster, 2) | ||
C5 = [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2) | ||
C6 = [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2) | ||
X = np.vstack((C1, C2, C3, C4, C5, C6)) | ||
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clust = OPTICS(min_samples=50, xi=.05, min_cluster_size=.05) | ||
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# Run the fit | ||
clust.fit(X) | ||
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labels_050 = cluster_optics_dbscan(reachability=clust.reachability_, | ||
core_distances=clust.core_distances_, | ||
ordering=clust.ordering_, eps=0.5) | ||
labels_200 = cluster_optics_dbscan(reachability=clust.reachability_, | ||
core_distances=clust.core_distances_, | ||
ordering=clust.ordering_, eps=2) | ||
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space = np.arange(len(X)) | ||
reachability = clust.reachability_[clust.ordering_] | ||
labels = clust.labels_[clust.ordering_] | ||
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plt.figure(figsize=(10, 7)) | ||
G = gridspec.GridSpec(2, 3) | ||
ax1 = plt.subplot(G[0, :]) | ||
ax2 = plt.subplot(G[1, 0]) | ||
ax3 = plt.subplot(G[1, 1]) | ||
ax4 = plt.subplot(G[1, 2]) | ||
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# Reachability plot | ||
colors = ['g.', 'r.', 'b.', 'y.', 'c.'] | ||
for klass, color in zip(range(0, 5), colors): | ||
Xk = space[labels == klass] | ||
Rk = reachability[labels == klass] | ||
ax1.plot(Xk, Rk, color, alpha=0.3) | ||
ax1.plot(space[labels == -1], reachability[labels == -1], 'k.', alpha=0.3) | ||
ax1.plot(space, np.full_like(space, 2., dtype=float), 'k-', alpha=0.5) | ||
ax1.plot(space, np.full_like(space, 0.5, dtype=float), 'k-.', alpha=0.5) | ||
ax1.set_ylabel('Reachability (epsilon distance)') | ||
ax1.set_title('Reachability Plot') | ||
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# OPTICS | ||
colors = ['g.', 'r.', 'b.', 'y.', 'c.'] | ||
for klass, color in zip(range(0, 5), colors): | ||
Xk = X[clust.labels_ == klass] | ||
ax2.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3) | ||
ax2.plot(X[clust.labels_ == -1, 0], X[clust.labels_ == -1, 1], 'k+', alpha=0.1) | ||
ax2.set_title('Automatic Clustering\nOPTICS') | ||
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# DBSCAN at 0.5 | ||
colors = ['g', 'greenyellow', 'olive', 'r', 'b', 'c'] | ||
for klass, color in zip(range(0, 6), colors): | ||
Xk = X[labels_050 == klass] | ||
ax3.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3, marker='.') | ||
ax3.plot(X[labels_050 == -1, 0], X[labels_050 == -1, 1], 'k+', alpha=0.1) | ||
ax3.set_title('Clustering at 0.5 epsilon cut\nDBSCAN') | ||
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# DBSCAN at 2. | ||
colors = ['g.', 'm.', 'y.', 'c.'] | ||
for klass, color in zip(range(0, 4), colors): | ||
Xk = X[labels_200 == klass] | ||
ax4.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3) | ||
ax4.plot(X[labels_200 == -1, 0], X[labels_200 == -1, 1], 'k+', alpha=0.1) | ||
ax4.set_title('Clustering at 2.0 epsilon cut\nDBSCAN') | ||
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plt.tight_layout() | ||
plt.show() |
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