def clusters_init(self):
k = self.nclusters
assignment = [0] * len(self.scorer.records)
for i in range(k):
assignment[np.random.randint(0, len(assignment))] = i + 1
partition = Partition(assignment)
clusters = [0] * k
members = partition.get_membership()[1:]
self.assign_clusters(clusters, members)
for (index, record) in enumerate(self.scorer.records):
scores = [self.ml(record, clusters[n]) for n in range(self.nclusters)]
# print scores
if assignment.count(assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
self.partition = Partition(assignment)
self.L = self.scorer.score(self.partition)
def maximise(self, method):
clusters = [0] * self.nclusters
alg = getattr(self, method)
count = 0
while True:
self.assign_clusters(clusters, self.partition.get_membership())
assignment = list(self.partition.partition_vector)
for (index, record) in enumerate(self.scorer.records):
scores = [
alg(record, clusters[n]) for n in range(self.nclusters)
]
# print scores
if assignment.count(
assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
assignment = Partition(assignment)
score = self.scorer.score(assignment)
if score > self.L:
self.L = score
self.partition = assignment
else:
count += 1
if count > 1:
break # Algorithm is deterministic so no need for more iterations
def maximise_random(self, method):
clusters = [0] * self.nclusters
alg = getattr(self, method)
count = 0
sampled = []
while True:
self.assign_clusters(clusters, self.partition.get_membership())
assignment = list(self.partition.partition_vector)
index = randint(0, len(self.scorer.records) - 1)
if index in sampled:
continue
else:
record = self.scorer.records[index]
sampled.append(index)
scores = [alg(record, clusters[n]) for n in range(self.nclusters)]
if assignment.count(
assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
assignment = Partition(assignment)
score = self.scorer.score(assignment)
if score > self.L:
self.L = score
self.partition = assignment
sampled = []
count = 0
else:
count += 1
if count == len(assignment): break
def split(self, k, assignment, verbosity=1):
"""
Function to split cluster based on least representative alignment
"""
if verbosity > 1:
print(assignment)
members = get_clusters(assignment)[k]
if len(members) == 1:
return assignment
elif len(members) == 2:
new_partition_vector = list(assignment.partition_vector)
new_partition_vector[members[0]] = max(assignment.partition_vector) + 1
new_assignment = Partition(new_partition_vector)
return new_assignment
tree = self.get_cluster_trees(assignment)[k]
alignment_scores = {}
if verbosity > 0:
print('Calculating alignment scores...')
for i in members:
r = self.Collection.records[i]
alignment_scores[i] = self.test(r, tree) / float(r.seqlength)
# per-site likelihood
seed, min_score = min(alignment_scores.iteritems(),
key=operator.itemgetter(1))
print('Splitting on {0}.'.format(seed + 1)) # convert to 1-based indexing
new_assignment = list(assignment.partition_vector)
new_assignment[seed] = max(assignment.partition_vector) + 1
if verbosity > 1:
print('New Partition: {0}'.format(new_assignment))
if verbosity > 0:
print('Assigning to new partition...')
new_assignment = Partition(new_assignment)
scores = self.score_sample(members, new_assignment)
assignment = self.make_new_assignment(members, scores, new_assignment,
nreassign=len(members))
if verbosity > 1:
print('Returning: {0}'.format(assignment))
return assignment
def get_partition(clusters):
seq = clusters if isinstance(clusters, dict) else range(len(clusters))
length = sum([len(clusters[i]) for i in seq])
pvec = [0] * length
for k in seq:
for i in clusters[k]:
pvec[i] = k
return Partition(tuple(pvec))
def clusters_init(self):
k = self.nclusters
assignment = [0] * len(self.scorer.records)
for i in range(k):
assignment[np.random.randint(0, len(assignment))] = i + 1
partition = Partition(assignment)
clusters = [0] * k
members = partition.get_membership()[1:]
self.assign_clusters(clusters, members)
for (index, record) in enumerate(self.scorer.records):
scores = [
self.ml(record, clusters[n]) for n in range(self.nclusters)
]
# print scores
if assignment.count(
assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
self.partition = Partition(assignment)
self.L = self.scorer.score(self.partition)
def embedding_plotter(
self,
coordinates,
dimensions,
partition=None,
add_sphere=False,
xlab='PCo1',
ylab='PCo2',
zlab='PCo3',
title='Trees embedded in dimension-reduced space',
outfile=False,
):
""" Points are coloured according to cluster membership specified
by Partition object (or all black if no Partition specified) """
optioncheck(dimensions, [2, 3])
partition = (partition or Partition(tuple([0] * len(coordinates))))
colours = zip(
*zip(range(len(partition)), itertools.cycle('bgrcmyk')))[1]
print(colours)
colour_mapping = np.array(
[colours[i - 1] for i in partition.partition_vector])
fig = plt.figure()
if dimensions == 3:
ax = fig.add_subplot(111,
projection='3d',
xlabel=xlab,
ylabel=ylab,
zlabel=zlab,
title=title)
if add_sphere:
ax = self.sphere(ax)
else:
ax = fig.add_subplot(111, xlabel=xlab, ylabel=ylab, title=title)
ax.scatter(*coordinates.T, color=colour_mapping)
# ax.set_aspect(1)
if outfile:
fig.savefig('{0}.pdf'.format(outfile))
return fig
def maximise_heuristic(self):
clusters = [0] * self.nclusters
sampled = []
for i in range(1000):
self.assign_clusters(clusters, self.partition.get_membership())
assignment = list(self.partition.partition_vector)
index = randint(0, len(self.scorer.records) - 1)
record = self.scorer.records[index]
sampled.append(index)
lls = [self.ml(record, clusters[n]) for n in range(self.nclusters)]
a = {'ll': max(lls)}
a['n'] = lls.index(a['ll'])
lls.pop(a['n'])
b = {'ll': max(lls)}
b['n'] = lls.index(b['ll'])
a['p'] = np.maths.exp(a['ll'] - logsum(a['ll'], b['ll']))
if np.random.uniform() > a['p']:
choice = a['n']
else:
choice = b['n']
if assignment.count(
assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = choice + 1
assignment = Partition(assignment)
if i % 10 == 0:
score = self.scorer.score(assignment)
if score > self.L:
self.max_L = score
self.max_partition = assignment
def make_new_assignment(self, sample, scores, assignment, nreassign=1,
choose='max'):
"""
MAKES A NEW PARTITION BY REASSIGNING RECORDS BETWEEN CLUSTERS
"""
optioncheck(choose, ('max', 'min'))
new_clusters = scores.argmax(axis=1)
M = scores / scores.sum(axis=1)[:, np.newaxis]
if choose == 'max':
reassignments = M.max(axis=1).argsort()[-nreassign:]
else:
reassignments = M.min(axis=1).argsort()[:nreassign]
new_assignment = list(assignment.partition_vector)
for i in reassignments:
new_assignment[sample[i]] = new_clusters[i] + 1
# because cluster number is in range
# [1,x], and new_clusters is in range [0,x-1]
return Partition(tuple(new_assignment))
def __init__(self, nclusters, collection, tmpdir=TMPDIR,
analysis='nj', initial_assignment=None, scorer=None):
optioncheck(analysis, ANALYSES + ['tc', 'TreeCollection'])
if self.analysis == 'tc':
self.analysis = 'TreeCollection'
else:
self.analysis = analysis
self.Collection = collection
if not self.Collection.records[0].tree:
print('Calculating {} trees for collection...'.format(analysis))
self.Collection.calc_NJ_trees()
self.datatype = collection.datatype
if scorer is not None and isinstance(scorer, Scorer):
self.scorer = scorer
else:
self.scorer = Scorer(self.Collection)
self.nclusters = nclusters
self.tmpdir = tmpdir
print('Calculating initial scores...')
if initial_assignment is None:
initial_assignment = Partition(tuple([0] * len(collection)))
# initial_assignment = self.random_partition(nclusters)
self.global_best_scores = {}
self.global_best_assignments = {}
self.global_best_scores[self.nclusters] = self.scorer.score(
initial_assignment, history=True)
self.global_best_assignments[self.nclusters] = initial_assignment
self.done_worse = 0
self.stayed_put = 0
self.i = 0
self.resets = 0
self.merges = 0
class EMTrees(object):
def __init__(
self,
collection,
nclusters,
metric='euc',
tmpdir=None,
):
if not isinstance(nclusters, int) or nclusters <= 1:
raise Exception('Need appropriate value for number of clusters.')
self.nclusters = nclusters
self.scorer = Scorer(collection.records, collection.analysis) # Could check for entries
self.datatype = collection.datatype
self.metric = metric
try:
self.tmpdir
except:
self.tmpdir = collection.tmpdir
def clusters_init(self):
k = self.nclusters
assignment = [0] * len(self.scorer.records)
for i in range(k):
assignment[np.random.randint(0, len(assignment))] = i + 1
partition = Partition(assignment)
clusters = [0] * k
members = partition.get_membership()[1:]
self.assign_clusters(clusters, members)
for (index, record) in enumerate(self.scorer.records):
scores = [self.ml(record, clusters[n]) for n in range(self.nclusters)]
# print scores
if assignment.count(assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
self.partition = Partition(assignment)
self.L = self.scorer.score(self.partition)
def random_partition(self):
self.partition = Partition(tuple(np.random.randint(self.nclusters,
size=len(self.scorer.records))))
self.L = self.scorer.score(self.partition)
def assign_clusters(self, clusters, members):
for n in range(self.nclusters):
if not clusters[n] or clusters[n].members != members[n]:
clusters[n] = Cluster(members[n], self.scorer.records, self.scorer.analysis)
return(clusters)
def maximise(self, method):
clusters = [0] * self.nclusters
alg = getattr(self, method)
count = 0
while True:
self.assign_clusters(clusters, self.partition.get_membership())
assignment = list(self.partition.partition_vector)
for (index, record) in enumerate(self.scorer.records):
scores = [alg(record, clusters[n]) for n in range(self.nclusters)]
# print scores
if assignment.count(assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
assignment = Partition(assignment)
score = self.scorer.score(assignment)
if score > self.L:
self.L = score
self.partition = assignment
else:
count += 1
if count > 1: break # Algorithm is deterministic so no need for more iterations
def maximise_random(self, method):
clusters = [0] * self.nclusters
alg = getattr(self, method)
count = 0
sampled = []
while True:
self.assign_clusters(clusters, self.partition.get_membership())
assignment = list(self.partition.partition_vector)
index = randint(0, len(self.scorer.records) - 1)
if index in sampled:
continue
else:
record = self.scorer.records[index]
sampled.append(index)
scores = [alg(record, clusters[n]) for n in range(self.nclusters)]
if assignment.count(assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
assignment = Partition(assignment)
score = self.scorer.score(assignment)
if score > self.L:
self.L = score
self.partition = assignment
sampled = []
count = 0
else:
count += 1
if count == len(assignment): break
def maximise_heuristic(self):
clusters = [0] * self.nclusters
sampled = []
for i in range(1000):
self.assign_clusters(clusters, self.partition.get_membership())
assignment = list(self.partition.partition_vector)
index = randint(0, len(self.scorer.records) - 1)
record = self.scorer.records[index]
sampled.append(index)
lls = [self.ml(record, clusters[n]) for n in range(self.nclusters)]
a = {'ll': max(lls)}
a['n'] = lls.index(a['ll'])
lls.pop(a['n'])
b = {'ll': max(lls)}
b['n'] = lls.index(b['ll'])
a['p'] = np.maths.exp(a['ll'] - logsum(a['ll'], b['ll']))
if np.random.uniform() > a['p']:
choice = a['n']
else:
choice = b['n']
if assignment.count(assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = choice + 1
assignment = Partition(assignment)
if i % 10 == 0:
score = self.scorer.score(assignment)
if score > self.L:
self.max_L = score
self.max_partition = assignment
def dist(self, obj1, obj2):
distance = DistanceMatrix([obj1.tree, obj2.tree], self.metric)[0][1]
return(-distance)
def ml(self, record, cluster, verbose=1):
p = Phyml(record, tmpdir=self.tmpdir)
input_tree = os.path.join(self.tmpdir, 'input_tree')
cluster.tree.write_to_file(input_tree)
p.add_tempfile(input_tree)
p.add_flag('--inputtree', input_tree)
p.add_flag('-o', 'r') # Optimise only on substitutions`
p.add_flag('-a', 'e')
p.add_flag('-b', 0)
p.add_flag('-c', 4)
p.add_flag('--quiet', '')
if self.datatype == 'protein':
p.add_flag('-d', 'aa')
elif self.datatype == 'dna':
p.add_flag('-d', 'nt')
score = p.run(verbosity=verbose).score
return(score)
def random_partition(self):
self.partition = Partition(tuple(np.random.randint(self.nclusters,
size=len(self.scorer.records))))
self.L = self.scorer.score(self.partition)
def random_partition(self):
self.partition = Partition(
tuple(
np.random.randint(self.nclusters,
size=len(self.scorer.records))))
self.L = self.scorer.score(self.partition)
class EMTrees(object):
def __init__(
self,
collection,
nclusters,
metric='euc',
tmpdir=None,
):
if not isinstance(nclusters, int) or nclusters <= 1:
raise Exception('Need appropriate value for number of clusters.')
self.nclusters = nclusters
self.scorer = Scorer(collection.records,
collection.analysis) # Could check for entries
self.datatype = collection.datatype
self.metric = metric
try:
self.tmpdir
except:
self.tmpdir = collection.tmpdir
def clusters_init(self):
k = self.nclusters
assignment = [0] * len(self.scorer.records)
for i in range(k):
assignment[np.random.randint(0, len(assignment))] = i + 1
partition = Partition(assignment)
clusters = [0] * k
members = partition.get_membership()[1:]
self.assign_clusters(clusters, members)
for (index, record) in enumerate(self.scorer.records):
scores = [
self.ml(record, clusters[n]) for n in range(self.nclusters)
]
# print scores
if assignment.count(
assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
self.partition = Partition(assignment)
self.L = self.scorer.score(self.partition)
def random_partition(self):
self.partition = Partition(
tuple(
np.random.randint(self.nclusters,
size=len(self.scorer.records))))
self.L = self.scorer.score(self.partition)
def assign_clusters(self, clusters, members):
for n in range(self.nclusters):
if not clusters[n] or clusters[n].members != members[n]:
clusters[n] = Cluster(members[n], self.scorer.records,
self.scorer.analysis)
return (clusters)
def maximise(self, method):
clusters = [0] * self.nclusters
alg = getattr(self, method)
count = 0
while True:
self.assign_clusters(clusters, self.partition.get_membership())
assignment = list(self.partition.partition_vector)
for (index, record) in enumerate(self.scorer.records):
scores = [
alg(record, clusters[n]) for n in range(self.nclusters)
]
# print scores
if assignment.count(
assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
assignment = Partition(assignment)
score = self.scorer.score(assignment)
if score > self.L:
self.L = score
self.partition = assignment
else:
count += 1
if count > 1:
break # Algorithm is deterministic so no need for more iterations
def maximise_random(self, method):
clusters = [0] * self.nclusters
alg = getattr(self, method)
count = 0
sampled = []
while True:
self.assign_clusters(clusters, self.partition.get_membership())
assignment = list(self.partition.partition_vector)
index = randint(0, len(self.scorer.records) - 1)
if index in sampled:
continue
else:
record = self.scorer.records[index]
sampled.append(index)
scores = [alg(record, clusters[n]) for n in range(self.nclusters)]
if assignment.count(
assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = scores.index(max(scores)) + 1
assignment = Partition(assignment)
score = self.scorer.score(assignment)
if score > self.L:
self.L = score
self.partition = assignment
sampled = []
count = 0
else:
count += 1
if count == len(assignment): break
def maximise_heuristic(self):
clusters = [0] * self.nclusters
sampled = []
for i in range(1000):
self.assign_clusters(clusters, self.partition.get_membership())
assignment = list(self.partition.partition_vector)
index = randint(0, len(self.scorer.records) - 1)
record = self.scorer.records[index]
sampled.append(index)
lls = [self.ml(record, clusters[n]) for n in range(self.nclusters)]
a = {'ll': max(lls)}
a['n'] = lls.index(a['ll'])
lls.pop(a['n'])
b = {'ll': max(lls)}
b['n'] = lls.index(b['ll'])
a['p'] = np.maths.exp(a['ll'] - logsum(a['ll'], b['ll']))
if np.random.uniform() > a['p']:
choice = a['n']
else:
choice = b['n']
if assignment.count(
assignment[index]) > 1 or assignment[index] == 0:
assignment[index] = choice + 1
assignment = Partition(assignment)
if i % 10 == 0:
score = self.scorer.score(assignment)
if score > self.L:
self.max_L = score
self.max_partition = assignment
def dist(self, obj1, obj2):
distance = DistanceMatrix([obj1.tree, obj2.tree], self.metric)[0][1]
return (-distance)
def ml(self, record, cluster, verbose=1):
p = Phyml(record, tmpdir=self.tmpdir)
input_tree = os.path.join(self.tmpdir, 'input_tree')
cluster.tree.write_to_file(input_tree)
p.add_tempfile(input_tree)
p.add_flag('--inputtree', input_tree)
p.add_flag('-o', 'r') # Optimise only on substitutions`
p.add_flag('-a', 'e')
p.add_flag('-b', 0)
p.add_flag('-c', 4)
p.add_flag('--quiet', '')
if self.datatype == 'protein':
p.add_flag('-d', 'aa')
elif self.datatype == 'dna':
p.add_flag('-d', 'nt')
score = p.run(verbosity=verbose).score
return (score)
def likelihood(partition, scorer):
score = scorer.score(Partition(partition))
return(score)
def get_true_partition(self):
l = []
for k in range(len(self.class_list)):
l.extend([k + 1] * self.class_list[k])
self.true_partition = Partition(l)
return self.true_partition
def random_partition(self, nclusters):
return Partition(tuple(np.random.randint(nclusters,
size=len(self.Collection))))
def get_partition_from_file(filename):
with open(filename) as f:
pvec = [int(x) for x in f.readline().split()]
return Partition(tuple(pvec))
print 'yep'
print 'Can build Plotter from Collection + DistanceMatrix...',
plotter_with_dm = Plotter(c, dm=dm)
print 'yes'
print 'Can build Plotter from a list of TrClSeq objects...',
plotter_from_records = Plotter(records=c.records)
print 'yes'
print 'Can build Plotter from DistanceMatrix only...',
plotter_just_dm = Plotter(dm=dm)
print 'yes'
print 'Testing plotting'
p = Partition(tuple([1] * 15 + [2] * 15 + [3] * 15 + [4] * 15))
p_rand = Partition(
tuple([
1, 3, 1, 4, 2, 3, 3, 3, 2, 2, 1, 3, 3, 4, 1, 4, 1, 1, 2, 4, 1, 2,
2, 2, 2, 2, 3, 4, 2, 2, 1, 4, 3, 1, 4, 4, 3, 1, 3, 1, 3, 2, 4, 4,
1, 4, 1, 2, 3, 4, 2, 4, 3, 2, 1, 3, 4, 4, 1, 3
]))
fig1 = plotter_from_collection.embedding('MDS', 2, p) # 2d MDS embedding
fig2 = plotter_from_collection.embedding('MDS', 3, p) # 3d MDS embedding
fig3 = plotter_from_collection.embedding('spectral', 2,
p_rand) # 2d spectral
fig4 = plotter_from_collection.embedding('spectral', 3,
p_rand) # 3d spectral
fig5 = plotter_just_dm.heatmap(p) # distance matrix as
fig6 = plotter_just_dm.heatmap(p_rand)
plt.show()
def merge(self, assignment, label1, label2):
pvec = ((x if x != label1 else label2)
for x in assignment.partition_vector)
return Partition(tuple(pvec))
if not len(p) == len(c):
print('Partition is of incorrect length '
'(expected {0}, got {1}'.format(len(c), len(p)))
sys.exit(1)
o = Optimiser(args.nclusters, c, tmpdir=new_tmpdir,
initial_assignment=p)
else:
o = Optimiser(args.nclusters, c, tmpdir=new_tmpdir)
# Hierarchical clustering via likelihood
if args.hierarchical is not None:
if args.hierarchical == 'top_down':
p = Partition(tuple([1] * len(c)))
elif args.hierarchical == 'bottom_up':
p = Partition(range(1, len(c) + 1))
result = o.constrain_assignment(p, args.nclusters)
# o.Scorer.clear_history()
score = o.Scorer.score(result)
o.global_best_assignments[args.nclusters] = result
o.global_best_scores[args.nclusters] = score
# Quit early
if args.quit:
pass
else:
if args.merge is True: