class Optimiser(object):
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.records,
analysis=analysis,
datatype=self.datatype,
tmpdir=tmpdir)
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
def _reset_counts(self):
self.done_worse = 0
self.stayed_put = 0
self.i = 0
self.resets = 0
def status(self, current_assignment, details=None):
iter_ = self.i
n = len(current_assignment)
curr_score = self.scorer.score(current_assignment, history=False)
best_score = self.global_best_scores[n]
details = ('\t'+str(details) if details is not None else '')
return 'Iter:{0}\tNclusters:{1}\tCurrent\tscore:{2}\tBest score:{3}{4}'.format(
iter_, n, curr_score, best_score, details)
def random_partition(self, nclusters):
return Partition(tuple(np.random.randint(nclusters,
size=len(self.Collection))))
def update(self, assignment):
"""
method for working interactively and keeping nclusters correct
"""
nclusters = len(assignment) # len(assignment) == number of clusters
best_score = self.global_best_scores.get(nclusters, NEGINF)
curr_score = self.scorer.score(assignment, history=False)
if (curr_score - best_score) > EPS:
self.global_best_assignments[nclusters] = assignment
self.global_best_scores[nclusters] = self.scorer.score(assignment,
history=False)
def get_clusters(self, assignment):
pvec = assignment.partition_vector
index_dict = defaultdict(list)
for (position, value) in enumerate(pvec):
index_dict[value].append(position)
return index_dict
def get_cluster_trees(self, assignment, index_dict=None):
index_dict = (index_dict or self.get_clusters(assignment))
tree_dict = {}
for (k, v) in index_dict.items():
if not tuple(v) in self.scorer.concats:
self.scorer.add(tuple(v))
tree_dict[k] = self.scorer.concats[tuple(v)]
return tree_dict
def score_sample(self, sample, assignment):
"""
!! changed to simply SCORE a PRE-MADE SAMPLE
sample_size:int, assignment:Partition object
Calculates score m*n score matrix, where m is number of alignments
in the sample, and n in the number of clusters encoded in the
assignment (==Partition object)
"""
# sample = random.sample(range(len(self.Collection)), sample_size)
cluster_trees = self.get_cluster_trees(assignment)
scores = np.zeros((len(sample), len(cluster_trees)))
for i, record_index in enumerate(sample):
rec = self.Collection.records[record_index]
for j, tree in cluster_trees.items():
scores[i, j-1] = self.test(rec, tree)
return (scores)
def constrain_assignment(self, assignment, nclusters=None):
"""
Constrain the assignment to have self.nclusters clusters
"""
if nclusters is None:
nclusters = self.nclusters
if (nclusters < 1) or (nclusters > len(self.Collection)):
raise ValueError('Invalid number of clusters: {}'.format(nclusters))
while len(assignment.get_membership()) > nclusters:
assignment = self.merge_closest(assignment)
while len(assignment.get_membership()) < nclusters:
assignment = self.split_search(assignment)
return assignment
def make_new_assignment(self, sample, scores, assignment, nreassign=1,
choose='max'):
"""
MAKES A NEW PARTITION BY REASSIGNING RECORDS BETWEEN CLUSTERS
"""
new_clusters = scores.argmax(axis=1)
M = scores/scores.sum(axis=1)[:, np.newaxis]
if choose == 'max':
reassignments = M.max(axis=1).argsort()[-nreassign:]
elif choose == 'min':
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 move(self, sample_size, assignment, nreassign=1, choose='max',
sampled=None):
"""
!! now generates own sample and passes to scores
wraps self.score_sample + self.new_assignment
"""
if sampled is None:
sampled = list()
unsampled = set(range(len(self.Collection))) - set(sampled)
if len(unsampled) > 0:
if sample_size > len(unsampled):
sample = unsampled
else:
sample = random.sample(unsampled, sample_size)
self.sampled.extend(sample)
scores = self.score_sample(sample, assignment)
assignment = self.make_new_assignment(sample, scores, assignment,
nreassign, choose)
return assignment
def merge(self, assignment, label1, label2):
pvec = ((x if x != label1 else label2)
for x in assignment.partition_vector)
return Partition(tuple(pvec))
def merge_closest(self, assignment):
print('Finding clusters to merge...')
clusters = self.get_clusters(assignment)
best_score = NEGINF
merging = [None, None]
for i in clusters:
for j in clusters:
# print('i = {}, j = {}'.format(i, j))
if i >= j:
continue
print('Testing Clusters {0} and {1}'.format(i, j))
test_assignment = self.merge(assignment, i, j)
self.update(test_assignment)
score = self.scorer.score(test_assignment, history=False)
if score > best_score:
merging[0] = i
merging[1] = j
best_score = score
best_assignment = test_assignment
print('Merging clusters {0} and {1}'.format(*merging))
print('Best assignment: {0}'.format(best_assignment))
return(best_assignment)
def split(self, k, assignment, verbosity=1):
"""
Function to split cluster based on least representative alignment
"""
if verbosity > 1:
print(assignment)
members = self.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 split_max_var(self, assignment):
clusters = self.get_clusters(assignment)
var_dict = {}
for k in clusters.keys():
var_dict[k] = self.var(clusters[k])
print(var_dict)
cluster_to_split, var = max(clusters.iteritems(),
key=operator.itemgetter(1))
def split_search(self, assignment, update=True):
clusters = self.get_clusters(assignment)
k = len(assignment)
best_score = NEGINF
for i in clusters:
print('i: {0}'.format(i))
test_assignment = self.split(i, assignment)
# score = self.scorer.score(test_assignment)
if len(test_assignment) == k + 1:
score = self.scorer.score(test_assignment, history=False)
self.update(test_assignment)
else:
score = -np.Inf
print('Something has gone wrong')
print(test_assignment)
print(score)
if score > best_score:
best_score = score
best_assignment = test_assignment
return best_assignment
def test(self, record, tree, model=None):
"""
TESTS AN ALIGNMENT AGAINST A TREE TOPOLOGY
"""
tmp_record = copy.deepcopy(record)
# if tree label set and record label set don't match
header_set = set(tmp_record.headers)
extra_in_tree = tree.labels - header_set
extra_in_record = header_set - tree.labels
if extra_in_tree:
for lab in extra_in_tree:
tmp_record.headers.append(lab)
tmp_record.sequences.append(''.join(['-']*tmp_record.seqlength))
tmp_record._update()
if extra_in_record:
for lab in extra_in_record:
i = tmp_record.headers.index(lab)
tmp_record.headers = (tmp_record.headers[:i] +
tmp_record.headers[i+1:])
tmp_record.sequences = (tmp_record.sequences[:i] +
tmp_record.sequences[i+1:])
tmp_record._update()
return tmp_alignment.likelihood(tree, self.tmpdir, fit_rates=True)
# alignment_file = tmp_record.write_phylip('{0}/tmp_alignment.phy'.format(
# self.tmpdir), interleaved=True)
# newick_file = tree.write_to_file('{0}/tmp_tree.nwk'.format(self.tmpdir))
# p = Phyml(tmp_record, self.tmpdir)
# p.add_tempfile(alignment_file)
# p.add_tempfile(newick_file)
# p.add_flag('-i', alignment_file)
# p.add_flag('-u', newick_file)
# p.add_flag('-b', '0') # no bootstraps
# if tmp_record.datatype == 'dna':
# if model is None:
# model = 'GTR'
# p.add_flag('-m', model)
# p.add_flag('-d', 'nt')
# else:
# if model is None:
# model = 'WAG'
# p.add_flag('-m', model) # evolutionary model
# p.add_flag('-d', 'aa') # datatype
# p.add_flag('-o', 'n') # no optimisation
# return p.run().score
def var(self, members):
score = self.scorer.add(tuple(members)).score
records = [self.Collection.records[i] for i in members]
total_length = sum([r.seqlength for r in records])
return(score / total_length)
def optimise(self,
assignment,
nclusters=None,
update=True,
history=True,
sample_size=10,
nreassign=10,
max_stayed_put=25,
max_resets=5,
max_done_worse=5,
max_iter=1000):
if nclusters is None:
nclusters = self.nclusters
assignment = self.constrain_assignment(assignment, nclusters)
local_best_assignment = assignment
local_best_score = self.scorer.score(local_best_assignment,
history=False)
current_assignment = local_best_assignment
self.sampled = []
print(self.status(current_assignment))
while True:
if self.stayed_put > max_stayed_put:
print('stayed put too many times ({0})'.format(max_stayed_put))
break
if self.resets == max_resets:
print('Reset limit reached ({0})'.format(max_resets))
break
if self.done_worse == max_done_worse:
print('wandered off, resetting...')
self.resets += 1
self.done_worse = 0
current_assignment = local_best_assignment
if self.i == max_iter:
print('max iterations reached')
break
new_assignment = self.move(sample_size, current_assignment,
nreassign)
new_assignment = self.constrain_assignment(new_assignment,
nclusters)
score = self.scorer.score(new_assignment, history=history)
self.update(new_assignment)
if (score - local_best_score) > EPS:
self.sampled = []
local_best_score = score
local_best_assignment = new_assignment
self.stayed_put = 0
self.done_worse = 0
self.resets = 0
print(self.status(new_assignment, '(Improved)'))
elif np.abs(score - local_best_score) < EPS:
self.stayed_put += 1
self.done_worse = 0
message = ('(No improvement - [{}/{}])'.format(self.stayed_put,
max_stayed_put))
print(self.status(new_assignment, message))
else:
self.sampled = []
#self.stayed_put = 0
self.done_worse += 1
message = '(Did worse - [{}/{}]'.format(self.done_worse,
max_done_worse)
print(self.status(new_assignment, message))
self.i += 1
self._reset_counts()
return local_best_assignment
def optimise_with_variable_clusters(self,
assignment,
target_clusters,
max_clusters,
optimise_on_ascent=True,
optimise_on_descent=True,
update=True,
**kwargs):
if max_clusters < target_clusters:
raise ValueError('max_clusters ({}) must be at least equal to '
'target_clusters ({})'.format(max_clusters, target_clusters))
current_clusters = len(assignment)
print('Optimising current assignment with {} clusters. Optimiser will '
'ascend to {} clusters, and descend to a target of {} clusters'
'.'.format(current_clusters, max_clusters, target_clusters))
for n in range(current_clusters, max_clusters+1):
print("ASCENDING (optimisation:{}) -> Current target: "
"{} clusters".format(('ON' if optimise_on_ascent else 'OFF'),
n))
if optimise_on_ascent:
assignment = self.optimise(assignment, nclusters=n, **kwargs)
else:
assignment = self.constrain_assignment(assignment, n)
for n in range(max_clusters-1, target_clusters-1, -1):
print('DESCENDING (optimisation:{}) -> Current target: {} '
'clusters'.format(('ON' if optimise_on_descent else 'OFF'),
n))
if optimise_on_descent:
assignment = self.optimise(assignment, nclusters=n, **kwargs)
else:
assignment = self.constrain_assignment(assignment, n)
return self.constrain_assignment(assignment, target_clusters)
def write(self, filename):
headers = ['Iteration', 'CPU Time', 'Likelihood', 'Partition',
'NClusters']
output = [[i] + x + len(x[-1])
for (i, x) in enumerate(self.scorer.history)]
with open(filename, 'w+') as file_:
writer = csv.writer(file_, delimiter='\t', quoting=csv.QUOTE_NONE)
writer.writerow(headers)
writer.writerows(output)
class Optimiser(object):
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
def _reset_counts(self):
self.done_worse = 0
self.stayed_put = 0
self.i = 0
self.resets = 0
def status(self, current_assignment, details=None):
iter_ = self.i
n = len(current_assignment)
curr_score = self.scorer.score(current_assignment, history=False)
best_score = self.global_best_scores[n]
details = ('\t' + str(details) if details is not None else '')
return 'Iter:{0}\tNclusters:{1}\tCurrent\tscore:{2}\tBest score:{3}{4}'.format(
iter_, n, curr_score, best_score, details)
def random_partition(self, nclusters):
return Partition(tuple(np.random.randint(nclusters,
size=len(self.Collection))))
def update(self, assignment):
"""
method for working interactively and keeping nclusters correct
"""
nclusters = len(assignment) # len(assignment) == number of clusters
best_score = self.global_best_scores.get(nclusters, NEGINF)
curr_score = self.scorer.score(assignment, history=False)
if (curr_score - best_score) > EPS:
self.global_best_assignments[nclusters] = assignment
self.global_best_scores[nclusters] = self.scorer.score(assignment,
history=False)
def get_cluster_trees(self, assignment, index_dict=None):
index_dict = (index_dict or get_clusters(assignment))
tree_dict = {}
for (k, v) in index_dict.items():
if not tuple(v) in self.scorer.concats:
self.scorer.add(tuple(v))
tree_dict[k] = self.scorer.concats[tuple(v)]
return tree_dict
def score_sample(self, sample, assignment):
"""
!! changed to simply SCORE a PRE-MADE SAMPLE
sample_size:int, assignment:Partition object
Calculates score m*n score matrix, where m is number of alignments
in the sample, and n in the number of clusters encoded in the
assignment (==Partition object)
"""
# sample = random.sample(range(len(self.Collection)), sample_size)
cluster_trees = self.get_cluster_trees(assignment)
scores = np.zeros((len(sample), len(cluster_trees)))
for i, record_index in enumerate(sample):
rec = self.Collection.records[record_index]
for j, tree in cluster_trees.items():
scores[i, j - 1] = self.test(rec, tree)
return scores
def constrain_assignment(self, assignment, nclusters=None):
"""
Constrain the assignment to have self.nclusters clusters
"""
if nclusters is None:
nclusters = self.nclusters
if (nclusters < 1) or (nclusters > len(self.Collection)):
raise ValueError('Invalid number of clusters: {}'.format(nclusters))
while len(assignment.get_membership()) > nclusters:
assignment = self.merge_closest(assignment)
while len(assignment.get_membership()) < nclusters:
assignment = self.split_search(assignment)
return 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 move(self, sample_size, assignment, nreassign=1, choose='max',
sampled=None):
"""
!! now generates own sample and passes to scores
wraps self.score_sample + self.new_assignment
"""
if sampled is None:
sampled = list()
unsampled = set(range(len(self.Collection))) - set(sampled)
if len(unsampled) > 0:
if sample_size > len(unsampled):
sample = unsampled
else:
sample = random.sample(unsampled, sample_size)
self.sampled.extend(sample)
scores = self.score_sample(sample, assignment)
assignment = self.make_new_assignment(sample, scores, assignment,
nreassign, choose)
return assignment
def merge(self, assignment, label1, label2):
pvec = ((x if x != label1 else label2)
for x in assignment.partition_vector)
return Partition(tuple(pvec))
def merge_closest(self, assignment):
print('Finding clusters to merge...')
clusters = get_clusters(assignment)
best_score = NEGINF
merging = [None, None]
for i in clusters:
for j in clusters:
# print('i = {}, j = {}'.format(i, j))
if i >= j:
continue
print('Testing Clusters {0} and {1}'.format(i, j))
test_assignment = self.merge(assignment, i, j)
self.update(test_assignment)
score_value = self.scorer.score(test_assignment, history=False)
if score_value > best_score:
merging[0] = i
merging[1] = j
best_score = score_value
best_assignment = test_assignment
print('Merging clusters {0} and {1}'.format(*merging))
print('Best assignment: {0}'.format(best_assignment))
return best_assignment
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 split_max_var(self, assignment):
clusters = get_clusters(assignment)
var_dict = {}
for k in clusters.keys():
var_dict[k] = self.var(clusters[k])
print(var_dict)
cluster_to_split, var = max(clusters.iteritems(),
key=operator.itemgetter(1))
def split_search(self, assignment, update=True):
clusters = get_clusters(assignment)
k = len(assignment)
best_score = NEGINF
for i in clusters:
print('i: {0}'.format(i))
test_assignment = self.split(i, assignment)
# score = self.scorer.score(test_assignment)
if len(test_assignment) == k + 1:
curr_score = self.scorer.score(test_assignment, history=False)
self.update(test_assignment)
else:
curr_score = -np.Inf
print('Something has gone wrong')
print(test_assignment)
print(curr_score)
if curr_score > best_score:
best_score = curr_score
best_assignment = test_assignment
return best_assignment
def test(self, record, tree, model=None):
"""
TESTS AN ALIGNMENT AGAINST A TREE TOPOLOGY
"""
tmp_record = copy.deepcopy(record)
# if tree label set and record label set don't match
header_set = set(tmp_record.headers)
extra_in_tree = tree.labels - header_set
extra_in_record = header_set - tree.labels
if extra_in_tree:
for lab in extra_in_tree:
tmp_record.headers.append(lab)
tmp_record.sequences.append(''.join(['-'] * tmp_record.seqlength))
tmp_record.update()
if extra_in_record:
for lab in extra_in_record:
i = tmp_record.headers.index(lab)
tmp_record.headers = (tmp_record.headers[:i] +
tmp_record.headers[i + 1:])
tmp_record.sequences = (tmp_record.sequences[:i] +
tmp_record.sequences[i + 1:])
tmp_record.update()
return tmp_alignment.likelihood(tree, self.tmpdir, fit_rates=True)
# alignment_file = tmp_record.write_phylip('{0}/tmp_alignment.phy'.format(
# self.tmpdir), interleaved=True)
# newick_file = tree.write_to_file('{0}/tmp_tree.nwk'.format(self.tmpdir))
# p = Phyml(tmp_record, self.tmpdir)
# p.add_tempfile(alignment_file)
# p.add_tempfile(newick_file)
# p.add_flag('-i', alignment_file)
# p.add_flag('-u', newick_file)
# p.add_flag('-b', '0') # no bootstraps
# if tmp_record.datatype == 'dna':
# if model is None:
# model = 'GTR'
# p.add_flag('-m', model)
# p.add_flag('-d', 'nt')
# else:
# if model is None:
# model = 'WAG'
# p.add_flag('-m', model) # evolutionary model
# p.add_flag('-d', 'aa') # datatype
# p.add_flag('-o', 'n') # no optimisation
# return p.run().score
def var(self, members):
score = self.scorer.add(tuple(members)).score
records = [self.Collection.records[i] for i in members]
total_length = sum([r.seqlength for r in records])
return score / total_length
def optimise(self,
assignment,
nclusters=None,
update=True,
history=True,
sample_size=10,
nreassign=10,
max_stayed_put=25,
max_resets=5,
max_done_worse=5,
max_iter=1000):
if nclusters is None:
nclusters = self.nclusters
assignment = self.constrain_assignment(assignment, nclusters)
local_best_assignment = assignment
local_best_score = self.scorer.score(local_best_assignment,
history=False)
current_assignment = local_best_assignment
self.sampled = []
print(self.status(current_assignment))
while True:
if self.stayed_put > max_stayed_put:
print('stayed put too many times ({0})'.format(max_stayed_put))
break
if self.resets == max_resets:
print('Reset limit reached ({0})'.format(max_resets))
break
if self.done_worse == max_done_worse:
print('wandered off, resetting...')
self.resets += 1
self.done_worse = 0
current_assignment = local_best_assignment
if self.i == max_iter:
print('max iterations reached')
break
new_assignment = self.move(sample_size, current_assignment,
nreassign)
new_assignment = self.constrain_assignment(new_assignment,
nclusters)
score = self.scorer.score(new_assignment, history=history)
self.update(new_assignment)
if (score - local_best_score) > EPS:
self.sampled = []
local_best_score = score
local_best_assignment = new_assignment
self.stayed_put = 0
self.done_worse = 0
self.resets = 0
print(self.status(new_assignment, '(Improved)'))
elif np.abs(score - local_best_score) < EPS:
self.stayed_put += 1
self.done_worse = 0
message = ('(No improvement - [{}/{}])'.format(self.stayed_put,
max_stayed_put))
print(self.status(new_assignment, message))
else:
self.sampled = []
# self.stayed_put = 0
self.done_worse += 1
message = '(Did worse - [{}/{}]'.format(self.done_worse,
max_done_worse)
print(self.status(new_assignment, message))
self.i += 1
self._reset_counts()
return local_best_assignment
def optimise_with_variable_clusters(self,
assignment,
target_clusters,
max_clusters,
optimise_on_ascent=True,
optimise_on_descent=True,
update=True,
**kwargs):
if max_clusters < target_clusters:
raise ValueError('max_clusters ({}) must be at least equal to '
'target_clusters ({})'.format(max_clusters, target_clusters))
current_clusters = len(assignment)
print('Optimising current assignment with {} clusters. Optimiser will '
'ascend to {} clusters, and descend to a target of {} clusters'
'.'.format(current_clusters, max_clusters, target_clusters))
for n in range(current_clusters, max_clusters + 1):
print("ASCENDING (optimisation:{}) -> Current target: "
"{} clusters".format(('ON' if optimise_on_ascent else 'OFF'),
n))
if optimise_on_ascent:
assignment = self.optimise(assignment, nclusters=n, **kwargs)
else:
assignment = self.constrain_assignment(assignment, n)
for n in range(max_clusters - 1, target_clusters - 1, -1):
print('DESCENDING (optimisation:{}) -> Current target: {} '
'clusters'.format(('ON' if optimise_on_descent else 'OFF'),
n))
if optimise_on_descent:
assignment = self.optimise(assignment, nclusters=n, **kwargs)
else:
assignment = self.constrain_assignment(assignment, n)
return self.constrain_assignment(assignment, target_clusters)
def write(self, filename):
headers = ['Iteration', 'CPU Time', 'Likelihood', 'Partition',
'NClusters']
output = [[i] + x + len(x[-1])
for (i, x) in enumerate(self.scorer.history)]
with open(filename, 'w+') as file_:
writer = csv.writer(file_, delimiter='\t', quoting=csv.QUOTE_NONE)
writer.writerow(headers)
writer.writerows(output)
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)