def parcellation_ward_spatial(func_data, n_clusters, graph=None):
"""
Make parcellation based upon ward hierarchical clustering from scikit-learn
Inputs: - func_data: functional data: array of shape
(nb_positions, dim_feature_1, [dim_feature2, ...])
- n_clusters: chosen number of clusters to create
- graph: adjacency list defining neighbours
(if None, no connectivity defined: clustering is spatially
independent)
Output: parcellation labels
"""
try:
# sklearn version < 0.17
from sklearn.cluster import Ward as AgglomerativeClustering
except ImportError:
from sklearn.cluster import AgglomerativeClustering
from pyhrf.graph import graph_to_sparse_matrix, sub_graph
if graph is not None:
labels = np.zeros(len(graph), dtype=np.int32)
ccs = connected_components(graph)
n_tot = len(graph) * 1.
ncs = np.zeros(len(ccs), dtype=np.int32)
for icc, cc in enumerate(ccs):
nc = int(np.round(n_clusters * len(cc) / n_tot))
if nc == 0:
nc = 1
ncs[icc] = nc
max_nc = np.argmax(ncs)
ncs[max_nc] = n_clusters - sum(ncs[0:max_nc]) - sum(ncs[max_nc + 1:])
assert sum(ncs) == n_clusters
assert (ncs > 0).all()
logger.info('Found %d connected components (CC) of sizes: %s', len(ccs),
' ,'.join([str(len(cc)) for cc in ccs]))
logger.info('Nb of clusters to search in each CC: %s',
' ,'.join(map(str, ncs)))
for nc, cc in zip(ncs, ccs):
if len(cc) < 2:
continue
if len(cc) < len(graph):
cc_graph, _ = sub_graph(graph, cc)
else:
cc_graph = graph
cc_connectivity = graph_to_sparse_matrix(cc_graph)
cc_data = func_data[cc]
logger.info('Launch spatial Ward (nclusters=%d) on data of shape %s',
nc, str(cc_data.shape))
ward_object = AgglomerativeClustering(
n_clusters=nc, connectivity=cc_connectivity
).fit(cc_data)
labels[cc] += ward_object.labels_ + 1 + labels.max()
else:
ward_object = AgglomerativeClustering(
n_clusters=n_clusters
).fit(func_data) # connectivity=None
labels = ward_object.labels_ + 1
return labels
def parcellation_ward_spatial(func_data, n_clusters, graph=None):
"""
Make parcellation based upon ward hierarchical clustering from scikit-learn
Inputs: - func_data: functional data: array of shape
(nb_positions, dim_feature_1, [dim_feature2, ...])
- n_clusters: chosen number of clusters to create
- graph: adjacency list defining neighbours
(if None, no connectivity defined: clustering is spatially
independent)
Output: parcellation labels
"""
from sklearn.cluster import Ward
from pyhrf.graph import graph_to_sparse_matrix, sub_graph
#from pyhrf.tools import cartesian
# print 'graph:'
# print graph
if graph is not None:
#print 'connectivity;'
#print connectivity.todense()
labels = np.zeros(len(graph), dtype=np.int32)
ccs = connected_components(graph)
n_tot = len(graph) * 1.
ncs = np.zeros(len(ccs), dtype=np.int32)
for icc,cc in enumerate(ccs):
nc = int(np.round(n_clusters * len(cc)/n_tot))
if nc == 0:
nc = 1
ncs[icc] = nc
max_nc = np.argmax(ncs)
ncs[max_nc] = n_clusters - sum(ncs[0:max_nc]) - sum(ncs[max_nc+1:])
assert sum(ncs) == n_clusters
assert (ncs > 0).all()
pyhrf.verbose(1, 'Found %d connected components (CC) of sizes: %s' \
%(len(ccs), ' ,'.join([str(len(cc)) for cc in ccs])))
pyhrf.verbose(1, 'Nb of clusters to search in each CC: %s' \
%' ,'.join(map(str,ncs)))
for nc,cc in zip(ncs,ccs):
#print 'cc:', len(cc)
#print 'cartesian:', list(cartesian(cc,cc))
if len(cc) < 2:
continue
if len(cc) < len(graph):
cc_graph,_ = sub_graph(graph, cc)
else:
cc_graph = graph
cc_connectivity = graph_to_sparse_matrix(cc_graph)
#print 'compute subslice ...'
# sub_slice = tuple(np.array(list(cartesian(cc,cc))).T) #indexes of the subpart of the connectivity matrix
#print 'sub_slice:'
#print sub_slice
#print 'subslice connectivity matrix ...'
#cc_connectivity = connectivity.tolil()[sub_slice].reshape((len(cc),len(cc))).tocoo() #indexes applied to the matrix connectivity (coo unsubscriptable)
#print 'cc_connectivity'
#print cc_connectivity.todense()
cc_data = func_data[cc]
pyhrf.verbose(2, 'Launch spatial Ward (nclusters=%d) '\
' on data of shape %s' %(nc, str(cc_data.shape)))
ward_object = Ward(n_clusters=nc, connectivity=cc_connectivity).fit(cc_data)
labels[cc] += ward_object.labels_ + 1 + labels.max()
else:
ward_object = Ward(n_clusters=n_clusters).fit(func_data) # connectivity=None
labels = ward_object.labels_ + 1
return labels
def parcellation_ward_spatial(func_data, n_clusters, graph=None):
"""Make parcellation based upon ward hierarchical clustering from scikit-learn
Parameters
----------
func_data: array of shape (nb_positions, dim_feature_1, [dim_feature2, ...])
functional data:
n_clusters
chosen number of clusters to create
graph
adjacency list defining neighbours. if None, no connectivity defined: clustering is spatially independent
Returns
-------
parcellation labels
"""
try:
# sklearn version < 0.17
from sklearn.cluster import Ward as AgglomerativeClustering
except ImportError:
from sklearn.cluster import AgglomerativeClustering
from pyhrf.graph import graph_to_sparse_matrix, sub_graph
if graph is not None:
labels = np.zeros(len(graph), dtype=np.int32)
ccs = connected_components(graph)
n_tot = len(graph) * 1.
ncs = np.zeros(len(ccs), dtype=np.int32)
for icc, cc in enumerate(ccs):
nc = int(np.round(n_clusters * len(cc) / n_tot))
if nc == 0:
nc = 1
ncs[icc] = nc
max_nc = np.argmax(ncs)
ncs[max_nc] = n_clusters - sum(ncs[0:max_nc]) - sum(ncs[max_nc + 1:])
assert sum(ncs) == n_clusters
assert (ncs > 0).all()
logger.info('Found %d connected components (CC) of sizes: %s',
len(ccs), ' ,'.join([str(len(cc)) for cc in ccs]))
logger.info('Nb of clusters to search in each CC: %s',
' ,'.join(map(str, ncs)))
for nc, cc in zip(ncs, ccs):
if len(cc) < 2:
continue
if len(cc) < len(graph):
cc_graph, _ = sub_graph(graph, cc)
else:
cc_graph = graph
cc_connectivity = graph_to_sparse_matrix(cc_graph)
cc_data = func_data[cc]
logger.info(
'Launch spatial Ward (nclusters=%d) on data of shape %s', nc,
str(cc_data.shape))
ward_object = AgglomerativeClustering(
n_clusters=nc, connectivity=cc_connectivity).fit(cc_data)
labels[cc] += ward_object.labels_ + 1 + labels.max()
else:
ward_object = AgglomerativeClustering(n_clusters=n_clusters).fit(
func_data) # connectivity=None
labels = ward_object.labels_ + 1
return labels