130 lines
3.9 KiB
Python
130 lines
3.9 KiB
Python
"""
|
|
===================================
|
|
Demo of DBSCAN clustering algorithm
|
|
===================================
|
|
|
|
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core
|
|
samples in regions of high density and expands clusters from them. This
|
|
algorithm is good for data which contains clusters of similar density.
|
|
|
|
See the :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py` example
|
|
for a demo of different clustering algorithms on 2D datasets.
|
|
|
|
"""
|
|
|
|
# %%
|
|
# Data generation
|
|
# ---------------
|
|
#
|
|
# We use :class:`~sklearn.datasets.make_blobs` to create 3 synthetic clusters.
|
|
|
|
from sklearn.datasets import make_blobs
|
|
from sklearn.preprocessing import StandardScaler
|
|
|
|
centers = [[1, 1], [-1, -1], [1, -1]]
|
|
X, labels_true = make_blobs(
|
|
n_samples=750, centers=centers, cluster_std=0.4, random_state=0
|
|
)
|
|
|
|
X = StandardScaler().fit_transform(X)
|
|
|
|
# %%
|
|
# We can visualize the resulting data:
|
|
|
|
import matplotlib.pyplot as plt
|
|
|
|
plt.scatter(X[:, 0], X[:, 1])
|
|
plt.show()
|
|
|
|
# %%
|
|
# Compute DBSCAN
|
|
# --------------
|
|
#
|
|
# One can access the labels assigned by :class:`~sklearn.cluster.DBSCAN` using
|
|
# the `labels_` attribute. Noisy samples are given the label math:`-1`.
|
|
|
|
import numpy as np
|
|
|
|
from sklearn import metrics
|
|
from sklearn.cluster import DBSCAN
|
|
|
|
db = DBSCAN(eps=0.3, min_samples=10).fit(X)
|
|
labels = db.labels_
|
|
|
|
# Number of clusters in labels, ignoring noise if present.
|
|
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
|
|
n_noise_ = list(labels).count(-1)
|
|
|
|
print("Estimated number of clusters: %d" % n_clusters_)
|
|
print("Estimated number of noise points: %d" % n_noise_)
|
|
|
|
# %%
|
|
# Clustering algorithms are fundamentally unsupervised learning methods.
|
|
# However, since :class:`~sklearn.datasets.make_blobs` gives access to the true
|
|
# labels of the synthetic clusters, it is possible to use evaluation metrics
|
|
# that leverage this "supervised" ground truth information to quantify the
|
|
# quality of the resulting clusters. Examples of such metrics are the
|
|
# homogeneity, completeness, V-measure, Rand-Index, Adjusted Rand-Index and
|
|
# Adjusted Mutual Information (AMI).
|
|
#
|
|
# If the ground truth labels are not known, evaluation can only be performed
|
|
# using the model results itself. In that case, the Silhouette Coefficient comes
|
|
# in handy.
|
|
#
|
|
# For more information, see the
|
|
# :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`
|
|
# example or the :ref:`clustering_evaluation` module.
|
|
|
|
print(f"Homogeneity: {metrics.homogeneity_score(labels_true, labels):.3f}")
|
|
print(f"Completeness: {metrics.completeness_score(labels_true, labels):.3f}")
|
|
print(f"V-measure: {metrics.v_measure_score(labels_true, labels):.3f}")
|
|
print(f"Adjusted Rand Index: {metrics.adjusted_rand_score(labels_true, labels):.3f}")
|
|
print(
|
|
"Adjusted Mutual Information:"
|
|
f" {metrics.adjusted_mutual_info_score(labels_true, labels):.3f}"
|
|
)
|
|
print(f"Silhouette Coefficient: {metrics.silhouette_score(X, labels):.3f}")
|
|
|
|
# %%
|
|
# Plot results
|
|
# ------------
|
|
#
|
|
# Core samples (large dots) and non-core samples (small dots) are color-coded
|
|
# according to the assigned cluster. Samples tagged as noise are represented in
|
|
# black.
|
|
|
|
unique_labels = set(labels)
|
|
core_samples_mask = np.zeros_like(labels, dtype=bool)
|
|
core_samples_mask[db.core_sample_indices_] = True
|
|
|
|
colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]
|
|
for k, col in zip(unique_labels, colors):
|
|
if k == -1:
|
|
# Black used for noise.
|
|
col = [0, 0, 0, 1]
|
|
|
|
class_member_mask = labels == k
|
|
|
|
xy = X[class_member_mask & core_samples_mask]
|
|
plt.plot(
|
|
xy[:, 0],
|
|
xy[:, 1],
|
|
"o",
|
|
markerfacecolor=tuple(col),
|
|
markeredgecolor="k",
|
|
markersize=14,
|
|
)
|
|
|
|
xy = X[class_member_mask & ~core_samples_mask]
|
|
plt.plot(
|
|
xy[:, 0],
|
|
xy[:, 1],
|
|
"o",
|
|
markerfacecolor=tuple(col),
|
|
markeredgecolor="k",
|
|
markersize=6,
|
|
)
|
|
|
|
plt.title(f"Estimated number of clusters: {n_clusters_}")
|
|
plt.show()
|