def mst_with_cutoff(distance_matrix,
pos,
savepath,
cutoff_type='rate',
cutoff_upper=0.9,
cutoff_lower=0.1,
interval=0.05):
"""
Do several MST clustering on given library of solutions in a range of different 'cutoff' parameters
Used for deciding the suitable cutoff value which will be utilized in the MST selection procedure
Parameters
----------
:param distance_matrix: distance matrix of given library of solutions
:param pos: positions generated by MDS or other embedding approaches, used for visualization
:param savepath: path where the visualization of clustering result stored
:param cutoff_type: type of parameter 'cutoff', should either be :
'threshold' : edges with larger weight than threshold will be cut
'rate' : number(>=1) / fraction(<1.0) of edges that will be cut
:param cutoff_upper: upper bound of parameter cutoff
:param cutoff_lower: lower bound of parameter cutoff
:param interval: interval of parameter cutoff
"""
# do MST clustering by setting 'cutoff' between [cutoff_lower, cutoff_upper] with given interval
cur_cutoff = cutoff_lower
while cur_cutoff <= cutoff_upper:
if cutoff_type == 'threshold':
mstmodel = MSTClustering(cutoff_scale=cur_cutoff,
min_cluster_size=2,
metric='precomputed',
approximate=False)
else:
mstmodel = MSTClustering(cutoff=cur_cutoff,
min_cluster_size=2,
metric='precomputed',
approximate=False)
mstmodel.fit(distance_matrix[0:-5, 0:-5])
filename = savepath + str(cur_cutoff) + '.png'
cv.plot_mst_result(mstmodel, pos, filename)
cur_cutoff += interval
return
def consistency_selection(nidpath,
pospath,
respath,
savepath,
mlset,
nlset,
distype='nid',
cutoff=0.9):
"""
:param nidpath:
:param pospath:
:param respath:
:param savepath:
:param mlset:
:param nlset:
:param distype:
:param cutoff:
:return:
"""
nidpath = os.path.expanduser(nidpath)
for f in os.listdir(nidpath):
if f.startswith('.'):
continue
fullpath = os.path.join(nidpath, f)
if os.path.isfile(fullpath):
fname = os.path.splitext(f)
filename = fname[0].split('_' + distype)[0]
dataset_name = filename.split('_')[0]
if not os.path.isdir(savepath + dataset_name):
os.mkdir(savepath + dataset_name)
# read distance matrix, position matrix and label matrix from external file
# note that the last 4 rows / cols are naive consensus & real labels
distanceMatrix = np.loadtxt(fullpath, delimiter=',')
pos = np.loadtxt(pospath + filename + '_mds2d.txt', delimiter=',')
labels = np.loadtxt(respath + filename + '.res', delimiter=',')
labels = labels.astype(int)
# real labels store in the last row
target = labels[-1]
class_num = len(np.unique(target))
# do mst clustering, we assume that there should be more than 5 solutions in each cluster
mstmodel = MSTClustering(cutoff=cutoff,
min_cluster_size=5,
metric='precomputed')
mstmodel.fit(distanceMatrix[0:-4, 0:-4])
# compute average consistency of each cluster of solutions
avg_cons = Metrics.average_consistency(mstmodel.labels_,
labels[0:-4], mlset, nlset)
# find the cluster of solution with largest consistency
maxclu = 0
max_cons = 0.0
print(avg_cons)
for clu, cons in avg_cons.iteritems():
if clu == -1:
continue
if cons > max_cons:
maxclu = clu
max_cons = cons
# do consensus, note that last 4 rows should be skipped
cluster_labels = labels[0:-4][mstmodel.labels_ == maxclu]
labels_CSPA = ce.cluster_ensembles_CSPAONLY(
cluster_labels, N_clusters_max=class_num)
labels_HGPA = ce.cluster_ensembles_HGPAONLY(
cluster_labels, N_clusters_max=class_num)
labels_MCLA = ce.cluster_ensembles_MCLAONLY(
cluster_labels, N_clusters_max=class_num)
# print labels and diversities (between the real labels)
nmi_CSPA = 1 - Metrics.diversityBtw2Cluster(labels_CSPA, target)
nmi_HGPA = 1 - Metrics.diversityBtw2Cluster(labels_HGPA, target)
nmi_MCLA = 1 - Metrics.diversityBtw2Cluster(labels_MCLA, target)
print('consensus result diversity (CSPA) =' + str(nmi_CSPA))
print('consensus diversity (HGPA) =' + str(nmi_HGPA))
print('consensus diversity (MCLA) =' + str(nmi_MCLA))
# store visualization file using 2d-MDS
fig = plt.figure(1)
plt.clf()
clusters = np.unique(mstmodel.labels_)
for i in clusters:
xs = pos[0:-4][mstmodel.labels_ == i, 0]
ys = pos[0:-4][mstmodel.labels_ == i, 1]
ax = plt.axes([0., 0., 1., 1.])
if i == -1:
plt.scatter(xs,
ys,
c=_colors[((int(i) + 1) % len(_colors))],
label='Outliers')
elif i == maxclu:
plt.scatter(xs,
ys,
c=_colors[((int(i) + 1) % len(_colors))],
marker='*',
label='Selected')
else:
plt.scatter(xs,
ys,
c=_colors[((int(i) + 1) % len(_colors))],
label='Clusters-' + str(i))
plt.scatter(pos[-4:-1, 0],
pos[-4:-1, 1],
c='blue',
marker='D',
label='Consensus')
plt.scatter(pos[-1:, 0],
pos[-1:, 1],
c='red',
marker='D',
label='Real')
plt.legend(loc='best', shadow=True)
plt.savefig(savepath + dataset_name + '/' + filename +
'_afterMST_selection_' + str(cutoff) + '.png',
format='png',
dpi=240)
return
def all_cluster_consensus(nidpath, respath, distype='nid', cutoff=0.9):
"""
:param nidpath:
:param respath:
:param distype:
:param cutoff:
:return:
"""
nidpath = os.path.expanduser(nidpath)
for f in os.listdir(nidpath):
if f.startswith('.'):
continue
fullpath = os.path.join(nidpath, f)
if os.path.isfile(fullpath):
fname = os.path.splitext(f)
filename = fname[0].split('_' + distype)[0]
dataset_name = filename.split('_')[0]
# read distance matrix, position matrix and label matrix from external file
# note that the last 4 rows / cols are naive consensus & real labels
print(fullpath)
distanceMatrix = np.loadtxt(fullpath, delimiter=',')
labels = np.loadtxt(respath + filename + '.res', delimiter=',')
labels = labels.astype(int)
# real labels store in the last row
target = labels[-1]
class_num = len(np.unique(target))
# do mst clustering, we assume that there should be more than 5 solutions in each cluster
mstmodel = MSTClustering(cutoff=cutoff,
min_cluster_size=5,
metric='precomputed')
mstmodel.fit(distanceMatrix[0:-4, 0:-4])
clusters = np.unique(mstmodel.labels_)
for i in clusters:
# do consensus, note that last 4 rows should be skipped
cluster_labels = labels[0:-4][mstmodel.labels_ == i]
labels_CSPA = ce.cluster_ensembles_CSPAONLY(
cluster_labels, N_clusters_max=class_num)
labels_HGPA = ce.cluster_ensembles_HGPAONLY(
cluster_labels, N_clusters_max=class_num)
labels_MCLA = ce.cluster_ensembles_MCLAONLY(
cluster_labels, N_clusters_max=class_num)
# print labels and diversities (between the real labels)
nmi_CSPA = 1 - Metrics.diversityBtw2Cluster(
labels_CSPA, target)
nmi_HGPA = 1 - Metrics.diversityBtw2Cluster(
labels_HGPA, target)
nmi_MCLA = 1 - Metrics.diversityBtw2Cluster(
labels_MCLA, target)
print('Cluster ' + str(i) +
'===========================================')
print('consensus result diversity (CSPA) =' + str(nmi_CSPA))
print('consensus diversity (HGPA) =' + str(nmi_HGPA))
print('consensus diversity (MCLA) =' + str(nmi_MCLA))
return
def mst_with_cutoff(distance_matrix,
pos,
labels,
savepath,
logger,
mlset,
nlset,
cutoff_type='rate',
cutoff_upper=0.9,
cutoff_lower=0.1,
interval=0.05,
min_cluster_size=2,
top_k=5):
"""
Do several MST clustering on given library of solutions in a range of different 'cutoff' parameters
Used for deciding the suitable cutoff value which will be utilized in the MST selection procedure
Parameters
----------
:param distance_matrix: distance matrix of given library of solutions
:param pos: positions generated by MDS or other embedding approaches, used for visualization
:param savepath: path where the visualization of clustering result stored
:param cutoff_type: type of parameter 'cutoff', should either be :
'threshold' : edges with larger weight than threshold will be cut
'rate' : number(>=1) / fraction(<1.0) of edges that will be cut
:param cutoff_upper: upper bound of parameter cutoff
:param cutoff_lower: lower bound of parameter cutoff
:param interval: interval of parameter cutoff
:param min_cluster_size:
"""
# do MST clustering by setting 'cutoff' between [cutoff_lower, cutoff_upper] with given interval
cluster_num = {}
cur_cutoff = cutoff_lower
while cur_cutoff <= cutoff_upper:
if cutoff_type == 'threshold':
mstmodel = MSTClustering(cutoff_scale=cur_cutoff,
min_cluster_size=min_cluster_size,
metric='precomputed',
approximate=False)
else:
mstmodel = MSTClustering(cutoff=cur_cutoff,
min_cluster_size=min_cluster_size,
metric='precomputed',
approximate=False)
mstmodel.fit(distance_matrix[0:-5, 0:-5])
# filename = savepath + str(cur_cutoff) + '.png'
cluster_num[cur_cutoff] = len(np.unique(mstmodel.labels_))
# cv.plot_mst_result(mstmodel, pos, filename)
cur_cutoff += interval
sorted_cluster_num = sorted(cluster_num.iteritems(),
key=lambda item: item[1],
reverse=True)
while top_k > 0:
consensus_cutoff = sorted_cluster_num[top_k - 1][0]
all_cluster_consensus(distance_matrix,
labels,
pos,
savepath,
logger,
mlset,
nlset,
cutoff=consensus_cutoff)
top_k -= 1
return
def all_cluster_consensus(distance_matrix,
labels,
pos,
savepath,
logger,
mlset,
nlset,
cutoff=0.9):
"""
:param distance_matrix:
:param labels:
:param pos:
:param savepath:
:param cutoff:
:return:
"""
target = labels[-1]
class_num = len(np.unique(target))
# do mst clustering, we assume that there should be more than 2 solutions in each cluster
mstmodel = MSTClustering(cutoff=cutoff,
min_cluster_size=2,
metric='precomputed')
mstmodel.fit(distance_matrix[0:-5, 0:-5])
filename = savepath + str(cutoff) + '.png'
cv.plot_mst_result(mstmodel, pos, filename)
clusters = np.unique(mstmodel.labels_)
# compute average consistency of each cluster of solutions
avg_cons_both = Metrics.average_consistency(mstmodel.labels_, labels[0:-5],
mlset, nlset)
avg_cons_must = Metrics.average_consistency(mstmodel.labels_,
labels[0:-5],
mlset,
nlset,
cons_type='must')
avg_cons_cannot = Metrics.average_consistency(mstmodel.labels_,
labels[0:-5],
mlset,
nlset,
cons_type='cannot')
for i in clusters:
# do consensus, note that last 4 rows should be skipped
cluster_labels = labels[0:-4][mstmodel.labels_ == i]
labels_CSPA = ce.cluster_ensembles_CSPAONLY(cluster_labels,
N_clusters_max=class_num)
labels_HGPA = ce.cluster_ensembles_HGPAONLY(cluster_labels,
N_clusters_max=class_num)
labels_MCLA = ce.cluster_ensembles_MCLAONLY(cluster_labels,
N_clusters_max=class_num)
# print labels and diversities (between the real labels)
nmi_CSPA = Metrics.normalized_max_mutual_info_score(
labels_CSPA, target)
nmi_HGPA = Metrics.normalized_max_mutual_info_score(
labels_HGPA, target)
nmi_MCLA = Metrics.normalized_max_mutual_info_score(
labels_MCLA, target)
logger.debug('Cluster ' + str(i) + ':')
logger.debug('Both Consistency is ' + str(avg_cons_both[i]))
logger.debug('Must Consistency is ' + str(avg_cons_must[i]))
logger.debug('Cannot Consistency is ' + str(avg_cons_cannot[i]))
logger.debug('CSPA performance is ' + str(nmi_CSPA))
logger.debug('HGPA performance is ' + str(nmi_HGPA))
logger.debug('MCLA performance is ' + str(nmi_MCLA))
logger.debug(
'-----------------------------mst finish--------------------------------'
)
logger.debug('')
return