def test_cohesion(self):
distances = CondensedMatrix( [ 1., 2., 3., 4.,
5., 6., 7.,
8., 9.,
10.])
clusters = [Cluster(None, elements=[0,1,2]),
Cluster(None, elements=[3,4])]
clustering = Clustering(clusters)
calculator = CohesionCalculator()
# cohesion of cluster 1: 1/3 * 8
# cohesion of cluster 2: 1/2 * 10
# max_cohesion = 11 (1/5 * 55)
# final cohesion = 0.696945
self.assertAlmostEqual(1-0.696945,calculator.evaluate(clustering, distances),places = 4)
def test_cohesion(self):
distances = CondensedMatrix([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
clusters = [
Cluster(None, elements=[0, 1, 2]),
Cluster(None, elements=[3, 4])
]
clustering = Clustering(clusters)
calculator = CohesionCalculator()
# cohesion of cluster 1: 1/3 * 8
# cohesion of cluster 2: 1/2 * 10
# max_cohesion = 11 (1/5 * 55)
# final cohesion = 0.696945
self.assertAlmostEqual(1 - 0.696945,
calculator.evaluate(clustering, distances),
places=4)
def test_cohesion_with_noise(self):
# Element 2 is treated as noise
distances = CondensedMatrix([ 1., 2., 3., 4.,
5., 6., 7.,
8., 9.,
10.])
clusters = [Cluster(None, elements=[0,1]),
Cluster(None, elements=[3,4])]
clustering = Clustering(clusters)
calculator = CohesionCalculator()
#[ 1. 3. 4.
# 6. 7.
# 10.]
# cohesion of cluster 1: 1/2
# cohesion of cluster 2: 1/2 * 10
# max_cohesion: 7.75 = (1/4 * 31)
# final cohesion: 0.7096774193548387
self.assertAlmostEqual(1-0.7096774193548387, calculator.evaluate(clustering, distances), places = 4)
def evaluate(self, clustering, condensed_distance_matrix, cohesions = None):
all_clusters = clustering.clusters
my_cohesions_dic = {}
if (cohesions is None):
#Calculate_cohesions if not given
cohesion_calctor = CohesionCalculator()
for cluster in all_clusters:
my_cohesions_dic[cluster] = cohesion_calctor.evaluate_cluster(cluster, condensed_distance_matrix)
else:
for i, cluster in enumerate(all_clusters):
my_cohesions_dic[cluster] = cohesions[i]
total_separation = 0
for i, cluster in enumerate(all_clusters):
separation = self.cluster_separation(cluster, clustering, my_cohesions_dic[cluster], condensed_distance_matrix)
total_separation = total_separation + separation
return total_separation
def test_cohesion_with_noise(self):
# Element 2 is treated as noise
distances = CondensedMatrix([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
clusters = [
Cluster(None, elements=[0, 1]),
Cluster(None, elements=[3, 4])
]
clustering = Clustering(clusters)
calculator = CohesionCalculator()
#[ 1. 3. 4.
# 6. 7.
# 10.]
# cohesion of cluster 1: 1/2
# cohesion of cluster 2: 1/2 * 10
# max_cohesion: 7.75 = (1/4 * 31)
# final cohesion: 0.7096774193548387
self.assertAlmostEqual(1 - 0.7096774193548387,
calculator.evaluate(clustering, distances),
places=4)
