def define_clusts(similarity_matrix, threshold=0.05, max_iter=200,
method='ap'):
"""Define clusters given the similarity matrix and the threshold."""
n, labels = connected_components(similarity_matrix, directed=False)
prev_max_clust = 0
print("connected components: %d" % n)
clusters = labels.copy()
if method == 'dbscan':
ap = DBSCAN(metric='precomputed', min_samples=1, eps=.2, n_jobs=-1)
if method == 'ap':
ap = AffinityPropagation(affinity='precomputed', max_iter=max_iter,
preference='median')
for i in range(n):
idxs = np.where(labels == i)[0]
if idxs.shape[0] > 1:
sm = similarity_matrix[idxs][:, idxs]
sm += sm.T + scipy.sparse.eye(sm.shape[0])
# Hierarchical clustering
if method == 'hc':
dists = squareform(1 - sm.toarray())
links = fastcluster.linkage(dists, method='ward')
try:
clusters_ = fcluster(links, threshold, 'distance')
except ValueError as err:
logging.critical(err)
clusters_ = np.zeros(1, dtype=int)
# DBSCAN
elif method == 'dbscan':
db = ap.fit(1. - sm.toarray())
# Number of clusters in labels, ignoring noise if present.
clusters_ = db.labels_
# n_clusters_ = len(set(clusters_)) - int(0 in clusters_)
# AffinityPropagation
# ap = AffinityPropagation(affinity='precomputed')
elif method == 'ap':
db = ap.fit(sm)
clusters_ = db.labels_
else:
raise ValueError("clustering method %s unknown" % method)
if np.min(clusters_) == 0:
clusters_ += 1
clusters_ += prev_max_clust
clusters[idxs] = clusters_
prev_max_clust = max(clusters_)
else: # connected component contains just 1 element
prev_max_clust += 1
clusters[idxs] = prev_max_clust
return np.array(extra.flatten(clusters))
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