def _taxOrder(trees) :
dtops = Counter(toNewick(tree, topologyOnly=1) for tree in trees)
averagedPosteriorDistances = defaultdict(lambda : 0)
for tx in dtops:
tree = parseNewick(tx)
distances = dict()
taxaDistance(tree, tree.root, distances)
for k in distances:
averagedPosteriorDistances[k] += distances[k] * dtops[tx]/len(trees)
taxa = sorted(trees[0].get_taxa())
ntax = len(taxa)
for j,k in allPairs(taxa) :
averagedPosteriorDistances[k,j] = averagedPosteriorDistances[j,k]
def groupDistance(g1,g2,dm) :
return mean([dm[i] for i in itertools.product(g1,g2)])
## def groupDistance(g1,g2,dm) :
## return min([dm[i] for i in itertools.product(g1,g2)])
groups = [[x] for x in taxa]
dm = averagedPosteriorDistances
while len(groups) > 1:
# find the two closest groups.
dists = [(groupDistance(groups[j],groups[k],dm), (j,k))
for j,k in allPairs(range(len(groups)))]
dists = sorted(dists)
d,(ij,ik) = dists[0]
# 123 abc 0,0 0,1 1,0 02 11 20
# 321 abc
# 123 cba
# 321 cba
# abc 123
# abc 321
# cba 123
# cba 321
g1,g2 = groups[ij],groups[ik]
def gid(g1,g2,dm) :
d = []
for n in range(len(g1)+len(g2) - 1) :
for i in range(-1,max(-len(g1)-1,-(len(g2)-n+1)), -1) :
#i > -(len(g2)-n+1)
d.append(dm[g1[i], g2[n-(i+1)]])
return d
dis = gid(g1,g2,dm),gid(g1,list(reversed(g2)),dm),gid(g2, g1,dm), gid(list(reversed(g2)),g1,dm)
dis = sorted(zip(dis,range(4)))
o = dis[0][1]
if o & 1 :
g2 = list(reversed(g2))
if o > 1 :
g1,g2 = g2,g1
groups[ij] = g1 + g2
del groups[ik]
otaxa = groups[0]
return otaxa
def getTaxaOrder(trees, refTree = None, reportTopologies = False,
nSample = 1, progress = False) :
if progress: print >> sys.stderr, "getting topologies...",
dtops = Counter(toNewick(tree, topologyOnly=1) for tree in trees)
averagedPosteriorDistances = defaultdict(lambda : 0)
if progress: print >> sys.stderr, ("taxa distance matrix (%d tops)..." % len(dtops)),
for tx in dtops:
tree = INexus.Tree(tx)
distances = dict()
taxaDistance(tree, tree.root, distances)
for k in distances:
averagedPosteriorDistances[k] += distances[k] * dtops[tx]/len(trees)
if refTree is None:
sdtops = sorted(dtops, key = lambda x : dtops[x])
refTrees = [INexus.Tree(t) for t in sdtops[-nSample:]]
else :
refTrees = [refTree]
overallMaxCR = -1
for tree in refTrees :
allOrders = getAllSingleFlipTaxaOrders(tree, tree.root)
maximizingOrder = allOrders[0]
treeMaxCR = getCR(maximizingOrder, averagedPosteriorDistances)
if progress: print >> sys.stderr, "optimizing... (cr",treeMaxCR,")",
nTries = 0
while True:
nTries += 1
mnode = None
for order,node in allOrders[1:] :
cr = getCR(order, averagedPosteriorDistances)
if cr > treeMaxCR:
treeMaxCR = cr
maximizingOrder = order
mnode = node
if progress: print >> sys.stderr, treeMaxCR,
if mnode is not None:
mnode.succ = [mnode.succ[1], mnode.succ[0]]
allOrders = getAllSingleFlipTaxaOrders(tree, tree.root)
else :
break
if treeMaxCR > overallMaxCR :
overallMaxCR = treeMaxCR
overallMaximizingOrder = maximizingOrder
mtree = tree
if progress: print >> sys.stderr, ("%d tries" % nTries)
if progress: print >> sys.stderr, "done"
if reportTopologies :
dtops = dict([(top,[]) for top in dtops])
for k,top in enumerate(toNewick(tree, topologyOnly=1) for tree in trees) :
dtops[top].append(k)
return (overallMaximizingOrder, mtree, dtops)
return (overallMaximizingOrder, mtree)