def test_similarity_to_distance():
matrix = numpy.array([[0, 1, 2], [1, 0, 3], [2, 3, 0]], dtype=numpy.float)
output = utils.similarity_to_distance(matrix, 20.0)
expected = numpy.array([[0, 20.0, 10.0], [20.0, 0, 6.66666667],
[10.0, 6.666666667, 0]])
assert output.shape == expected.shape
assert all(output.ravel() - expected.ravel() < 1e4)
def hierarchical_dbscan(similarities=None, distance_cutoff=200):
""" cluster using the Hierarchical DBSCAN algorithm """
if similarities is None:
return None
distance_matrix = utils.similarity_to_distance(similarities,
distance_cutoff)
hdbscan_clusterer = hdbscan.HDBSCAN(metric="precomputed", min_samples=3)
labels = hdbscan_clusterer.fit_predict(distance_matrix)
return labels
def dbscan(similarities=None, threshold=None, distance_cutoff=200):
""" cluster using DBSCAN algorithm """
if similarities is None:
return None
distance_matrix = utils.similarity_to_distance(similarities,
distance_cutoff)
threshold = threshold or 2.8
dbscan_clusterer = sklearn.cluster.DBSCAN(eps=threshold,
min_samples=3,
metric="precomputed",
algorithm="brute")
labels = dbscan_clusterer.fit_predict(distance_matrix)
return labels
def hierarchical_clustering(similarities=None,
method='complete',
threshold=None,
distance_cutoff=200):
""" create a flat clustering based on hierarchical clustering methods and a threshold """
if similarities is None:
return None
distance_matrix = utils.similarity_to_distance(similarities,
distance_cutoff)
linkage = sch.linkage(distance_matrix, method=method)
threshold = threshold or 0.7 * linkage[:, 2].max()
labels = sch.fcluster(linkage, threshold, criterion='distance')
return labels
args = parse_arguments()
edgelist = None
if args.edgelist:
print("edgelist filename=" + args.edgelist)
edgelist = utils.read_edgelist_file(args.edgelist)
matrix = None
if args.matrix:
print("matrix filename=" + args.matrix)
matrix = utils.read_distance_matrix_file(args.matrix)
if args.convert:
print("convert=" + args.convert)
if args.edgelist:
edgelist['d'] = utils.similarity_to_distance(edgelist['d'], float(args.convert))
if args.matrix:
matrix = utils.similarity_to_distance(matrix, float(args.convert))
if args.names:
print("names filenname=" + args.names)
print("clustering_algorithm=" + args.clustering_algorithm)
if args.clustering_algorithm == 'hierarchical':
clustering = hierarchical_clustering(edgelist=edgelist, distance_matrix=matrix)
elif args.clustering_algorithm == 'dbscan':
clustering = dbscan(edgelist=edgelist, distance_matrix=matrix)
elif args.clustering_algorithm == 'spectral':
clustering = spectral(edgelist=edgelist, distance_matrix=matrix)
elif args.clustering_algorithm == 'agglomerative':
args = parse_arguments()
edgelist = None
if args.edgelist:
print("edgelist filename=" + args.edgelist)
edgelist = utils.read_edgelist_file(args.edgelist)
matrix = None
if args.matrix:
print("matrix filename=" + args.matrix)
matrix = utils.read_distance_matrix_file(args.matrix)
if args.convert:
print("convert=" + args.convert)
if args.edgelist:
edgelist['d'] = utils.similarity_to_distance(
edgelist['d'], float(args.convert))
if args.matrix:
matrix = utils.similarity_to_distance(matrix, float(args.convert))
if args.names:
print("names filenname=" + args.names)
print("clustering_algorithm=" + args.clustering_algorithm)
if args.clustering_algorithm == 'hierarchical':
clustering = hierarchical_clustering(edgelist=edgelist,
distance_matrix=matrix)
elif args.clustering_algorithm == 'dbscan':
clustering = dbscan(edgelist=edgelist, distance_matrix=matrix)
elif args.clustering_algorithm == 'hdbscan':
clustering = hierarchical_dbscan(edgelist=edgelist,
distance_matrix=matrix)
def test_similarity_to_distance():
matrix = numpy.array([[0, 1, 2],[1,0,3],[2,3,0]], dtype=numpy.float)
output = utils.similarity_to_distance(matrix, 20.0)
expected = numpy.array([[0, 20.0, 10.0],[20.0,0,6.66666667],[10.0,6.666666667,0]])
assert output.shape == expected.shape
assert all(output.ravel() - expected.ravel() < 1e4)