When experimenting with using the Voronoi Tesselation to identify which machines are picked up by certain points, it was easy to extend the idea to visualising clustering through a voronoi.
Using the voronoi_finite_polygons_2d method from pycobra.visualisation, it's easy to do this
In [1]:
%matplotlib inline
import numpy as np
from pycobra.cobra import Cobra
from pycobra.visualisation import Visualisation
from pycobra.diagnostics import Diagnostics
import matplotlib.pyplot as plt
from sklearn import cluster
Let's make some blobs so clustering is easy.
In [2]:
from sklearn.datasets.samples_generator import make_blobs
X, Y = make_blobs(n_samples=200, centers=2, n_features=2)
Y = np.power(X[:,0], 2) + np.power(X[:,1], 2)
We set up a few scikit-learn clustering machines which we'd like to visualise the results of.
In [3]:
two_means = cluster.KMeans(n_clusters=2)
spectral = cluster.SpectralClustering(n_clusters=2, eigen_solver='arpack', affinity="nearest_neighbors")
dbscan = cluster.DBSCAN(eps=.6)
affinity_propagation = cluster.AffinityPropagation(damping=.9, preference=-200)
birch = cluster.Birch(n_clusters=2)
In [4]:
from pycobra.visualisation import voronoi_finite_polygons_2d
from scipy.spatial import Voronoi, voronoi_plot_2d
Helper function to implement the Voronoi.
In [5]:
def plot_cluster_voronoi(data, algo):
# passing input space to set up voronoi regions.
points = np.hstack((np.reshape(data[:,0], (len(data[:,0]), 1)), np.reshape(data[:,1], (len(data[:,1]), 1))))
vor = Voronoi(points)
# use helper Voronoi
regions, vertices = voronoi_finite_polygons_2d(vor)
fig, ax = plt.subplots()
plot = ax.scatter([], [])
indice = 0
for region in regions:
ax.plot(data[:,0][indice], data[:,1][indice], 'ko')
polygon = vertices[region]
# if it isn't gradient based we just color red or blue depending on whether that point uses the machine in question
color = algo.labels_[indice]
# we assume only two
if color == 0:
color = 'r'
else:
color = 'b'
ax.fill(*zip(*polygon), alpha=0.4, color=color, label="")
indice += 1
ax.axis('equal')
plt.xlim(vor.min_bound[0] - 0.1, vor.max_bound[0] + 0.1)
plt.ylim(vor.min_bound[1] - 0.1, vor.max_bound[1] + 0.1)
In [6]:
two_means.fit(X)
plot_cluster_voronoi(X, two_means)
In [7]:
dbscan.fit(X)
plot_cluster_voronoi(X, dbscan)
In [8]:
spectral.fit(X)
plot_cluster_voronoi(X, spectral)
In [9]:
affinity_propagation.fit(X)
plot_cluster_voronoi(X, affinity_propagation)
In [10]:
birch.fit(X)
plot_cluster_voronoi(X, birch)
This is just an example of the things you can do with Voronoi Tesselations - it's an interesting way to look at your data!
Licensed under the MIT License - https://opensource.org/licenses/MIT