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 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)
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)
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)