def searchRegraft(conf, distmat, labels, stree, gene2species, params,
initTree=None, visited=None, proposeFunc=proposeTree2):
if visited == None:
visited = {}
# init with NJ
if initTree != None:
tree = initTree
else:
tree = phylo.neighborjoin(distmat, labels)
tree = phylo.reconRoot(tree, stree, gene2species)
Spidir.setTreeDistances(conf, tree, distmat, labels)
# init likelihood score
logl = Spidir.treeLogLikelihood(conf, tree, stree, gene2species, params)
# store tree in visited
addVisited(conf, visited, tree, gene2species)
# show initial tree
printMCMC(conf, 0, tree, stree, gene2species, visited)
for i in xrange(conf["regrafts"]):
tree = proposeTree3(conf, tree, distmat, labels,
stree, gene2species, params, visited)
if tree.data["logl"] > logl:
printMCMC(conf, i, tree, stree, gene2species, visited)
logl = tree.data["logl"]
return tree, tree.data["logl"]
def propose(chain, tree):
tree2 = proposeFunc(conf, tree, distmat, labels,
stree, gene2species, params, visited)
# check visited dict
thash = phylo.hash_tree(tree2)
if thash in visited:
logl, tree2, count = visited[thash]
#this.nold += 1
else:
Spidir.setTreeDistances(conf, tree2, distmat, labels)
logl = Spidir.treeLogLikelihood(conf, tree2, stree, gene2species, params)
this.nold = 0
addVisited(conf, visited, tree2, gene2species, thash)
# best yet tree
if logl > this.toplogl:
printMCMC(conf, "%d:%d" % (chain.name, this.iter),
tree2, stree, gene2species, visited)
this.toplogl = logl
this.toptree = tree2.copy()
# move some other chains to best state
#chains2 = sorted(chains, key=lambda x: x.logl)
#for chain in chains2[:1]:
# chain.state = this.toptree.copy()
# chain.logl = this.toplogl
# alter logl to influence search only
#chain.relax = conf["speedup"] * this.nold
return tree2, logl
def getProposals(conf, tree, distmat, labels, stree,
gene2species, params, visited, stuck=False):
# TODO: handle root edges
# try all NNI
# find edges for NNI
nodes = tree.nodes.values()
nodes = filter(lambda x: not x.isLeaf() and
x != tree.root and
x not in tree.root.children, nodes)
edges = [(node, node.parent) for node in nodes]
edges.append(tuple(tree.root.children))
treelib.drawTreeNames(tree, minlen=5, maxlen=5, out=sys.stderr)
util.printcols(util.map2(lambda x: x.name, edges), out=sys.stderr)
proposals = []
for edge in edges:
for change in (0,1):
proposeNni(tree, edge[0], edge[1], change)
tree2 = phylo.reconRoot(tree, stree, gene2species, newCopy=True)
thash = phylo.hash_tree(tree2)
if thash not in visited:
Spidir.setTreeDistances(conf, tree2, distmat, labels)
logl = Spidir.treeLogLikelihood(conf, tree2, stree,
gene2species, params)
visited[thash] = [logl, tree2, 1]
proposals.append([logl, edge, change])
else:
visited[thash][2] += 1
logl = visited[thash][0]
if not stuck:
proposals.append([logl, edge, change])
# switch branch back
proposeNni(tree, edge[0], edge[1], change)
proposals.sort(key=lambda x: x[0], reverse=True)
return proposals
#!/usr/bin/env python
from rasmus.common import *
from rasmus.bio import phylo
import Spidir
import Spidir.Likelihood
tree = readTree("../test/0.nt.tree")
stree = readTree("../test/flies.stree")
gene2species = genomeutil.readGene2species("../test/flies.smap")
params = Spidir.readParams("../test/flies.nt.param")
drawTree(tree)
print sum(x.dist for x in tree)
print Spidir.estGeneRate(tree, stree, params, gene2species)
print Spidir.estGeneRate(tree, stree, params, gene2species)
print Spidir.estGeneRate(tree, stree, params, gene2species)
print Spidir.Likelihood.getBaserate(tree, stree, params, gene2species=gene2species)
conf = {"python_only": True,
"famprob": True}
print Spidir.treeLogLikelihood(conf, tree, stree, gene2species, params)
conf = {}
#generate = Spidir.estGeneRate(tree, stree, params, gene2species)
for generate in frange(1.5, 2.3, .05):
print generate, Spidir.treeLogLikelihood(conf, tree, stree, gene2species, params,
baserate=generate)
def searchExhaustive(conf, distmat, labels, tree, stree, gene2species, params,
depth=2, visited=None, visited2=None, topDepth=True,
toplogl=None, short=False):
if visited == None:
visited = {}
if visited2 == None:
visited2 = {}
tree = tree.copy()
# find initial logl
thash = phylo.hash_tree(tree)
if thash not in visited:
Spidir.setTreeDistances(conf, tree, distmat, labels)
logl = Spidir.treeLogLikelihood(conf, tree, stree,
gene2species, params)
visited[thash] = [logl, tree.copy(), 1]
drawTreeLogl(tree)
else:
logl = visited[thash][0]
if toplogl == None:
toplogl = [logl]
debug(" " * (depth*2), "(%d)" % len(visited))
sys.stdout.flush()
if depth < 1:
return tree, logl
# try all NNI
# find edges for NNI
nodes = tree.nodes.values()
nodes = filter(lambda x: not x.isLeaf() and
x != tree.root and \
x.parent != tree.root, nodes)
edges = [(node, node.parent) for node in nodes]
for edge in edges:
for change in (0,1):
proposeNni(tree, edge[0], edge[1], change)
thash = phylo.hash_tree(tree)
if thash not in visited:
Spidir.setTreeDistances(conf, tree, distmat, labels)
logl = Spidir.treeLogLikelihood(conf, tree, stree,
gene2species, params)
visited[thash] = [logl, tree.copy(), 1]
else:
logl = visited[thash][0]
if logl > toplogl[0]:
toplogl[0] = logl
if short:
return tree, logl
else:
printMCMC(conf, "N/A", tree, stree, gene2species, visited)
if (thash not in visited2 or \
depth > visited2[thash]) and \
logl - toplogl[0] >= conf["eprune"]:
visited2[thash] = depth
# dig deeper
if depth > 1:
tree2, logl2 = searchExhaustive(conf, distmat, labels,
tree, stree, gene2species, params,
depth=depth-1, visited=visited,
visited2=visited2,
topDepth=False,
toplogl=toplogl, short=short)
if short and tree2 != None:
return tree2, logl2
# switch branch back
proposeNni(tree, edge[0], edge[1], change)
# debug
if topDepth:
items = visited.items()
i = util.argmaxfunc(lambda x: x[1][0], items)
thash, (logl, tree, count) = items[i]
return tree, logl
else:
return None, None
def searchGreedy(conf, distmat, labels, stree, gene2species, params, visited=None):
if visited == None:
visited = {}
totalgenes = len(labels)
ngenes = 2
# create initial 2 gene tree (labels[0], labels[1])
tree = treelib.Tree()
tree.make_root()
tree.add_child(tree.root, treelib.TreeNode(labels[0]))
tree.add_child(tree.root, treelib.TreeNode(labels[1]))
for ngenes in xrange(2, totalgenes):
debug("adding", labels[ngenes])
toplogl = -util.INF
toptree = None
distmat2 = matrixlib.submatrix(distmat, range(ngenes+1), range(ngenes+1))
labels2 = labels[:ngenes+1]
# place new gene on every branch
for name in tree.nodes:
tree2 = tree.copy()
node = tree2.nodes[name]
if node == tree2.root:
newnode = treelib.TreeNode(tree2.new_name())
tree2.add_child(newnode, tree2.root)
tree2.root = newnode
tree2.add_child(newnode, treelib.TreeNode(labels[ngenes]))
else:
parent = node.parent
tree2.remove(node)
newnode = treelib.TreeNode(tree2.new_name())
tree2.add_child(parent, newnode)
tree2.add_child(newnode, node)
tree2.add_child(newnode, treelib.TreeNode(labels[ngenes]))
#tree2 = phylo.reconRoot(tree2, stree, gene2species)
Spidir.setTreeDistances(conf, tree2, distmat2, labels2)
logl = Spidir.treeLogLikelihood(conf, tree2, stree, gene2species, params)
if logl >= toplogl:
toplogl = logl
toptree = tree2
tree = toptree
# only use visited hash table if all genes are present
if ngenes == totalgenes:
visited2 = visited
else:
# otherwise use a new temp hash table
visited2 = {}
tree, logl = searchExhaustive(conf, distmat2, labels2,
tree, stree, gene2species, params,
visited=visited2,
depth=conf["depth"])
if logl >= toplogl:
toplogl = logl
toptree = tree
tree = toptree
debug()
visited.update(visited2)
return tree, toplogl
def searchMCMC(conf, distmat, labels, stree, gene2species, params,
initTree=None, visited=None, proposeFunc=proposeTree2):
if visited == None:
visited = {}
this = util.Bundle(
nold=0,
toplogl = -util.INF,
toptree = None,
iter=0)
# init with NJ
if initTree != None:
tree = initTree
else:
tree = phylo.neighborjoin(distmat, labels)
tree = phylo.reconRoot(tree, stree, gene2species)
Spidir.setTreeDistances(conf, tree, distmat, labels)
# init likelihood score
this.toplogl = Spidir.treeLogLikelihood(conf, tree, stree, gene2species, params)
this.toptree = tree
# store tree in visited
addVisited(conf, visited, tree, gene2species)
# show initial tree
printMCMC(conf, 0, tree, stree, gene2species, visited)
# proposal function
def propose(chain, tree):
tree2 = proposeFunc(conf, tree, distmat, labels,
stree, gene2species, params, visited)
# check visited dict
thash = phylo.hash_tree(tree2)
if thash in visited:
logl, tree2, count = visited[thash]
#this.nold += 1
else:
Spidir.setTreeDistances(conf, tree2, distmat, labels)
logl = Spidir.treeLogLikelihood(conf, tree2, stree, gene2species, params)
this.nold = 0
addVisited(conf, visited, tree2, gene2species, thash)
# best yet tree
if logl > this.toplogl:
printMCMC(conf, "%d:%d" % (chain.name, this.iter),
tree2, stree, gene2species, visited)
this.toplogl = logl
this.toptree = tree2.copy()
# move some other chains to best state
#chains2 = sorted(chains, key=lambda x: x.logl)
#for chain in chains2[:1]:
# chain.state = this.toptree.copy()
# chain.logl = this.toplogl
# alter logl to influence search only
#chain.relax = conf["speedup"] * this.nold
return tree2, logl
# init chains
chains = []
for i in range(conf["nchains"]):
chains.append(McmcChain(i, tree.copy(), this.toplogl, propose))
# run chains
for i in xrange(1, conf["iters"]):
this.iter += 1
for chain in chains:
chain.step()
return this.toptree, this.toplogl
def searchHillClimb(conf, distmat, labels, stree, gene2species, params,
initTree=None, visited=None):
if visited == None:
visited = {}
# init with NJ
if initTree != None:
tree = initTree
else:
#tree = bionj.bionj(labels=labels, distmat=distmat, verbose=False)
tree = phylo.neighborjoin(distmat, labels)
tree = phylo.reconRoot(tree, stree, gene2species)
Spidir.setTreeDistances(conf, tree, distmat, labels)
# init likelihood score
logl = treeLogLikelihood(conf, tree, stree, gene2species, params)
# store tree in visited
addVisited(conf, visited, tree, gene2species)
stuck = False
for i in range(conf["hilliters"]):
printMCMC(conf, i, tree, stree, gene2species, visited)
proposals = getProposals(conf, tree, distmat, labels,
stree, gene2species, params, visited, stuck)
util.printcols(map(lambda (a,(b,c),d): [a, b.name, c.name, d], proposals))
print
# determine which proposals to use
edgeset = set()
proposals2 = []
for logl2, edge, change in proposals:
if edge in edgeset:
continue
proposals2.append([logl2, edge, change])
edgeset.add((getNniUncle(tree, edge[0], edge[1]), edge[1]))
edgeset.add((edge[0].children[change], edge[0]))
edgeset.add((edge[0], edge[1]))
util.printcols(map(lambda (a,(b,c),d): [a, b.name, c.name, d], proposals2))
print
heat = 1.0
start = 0
while start < len(proposals2):
nproposals = int(math.ceil(len(proposals2) * heat))
# apply proposals
for logl3, edge, change in proposals2[start:start+nproposals]:
proposeNni(tree, edge[0], edge[1], change)
tree2 = phylo.reconRoot(tree, stree, gene2species, newCopy=True)
# calc likelihood
thash = phylo.hash_tree(tree2)
if thash not in visited:
Spidir.setTreeDistances(conf, tree2, distmat, labels)
logl2 = Spidir.treeLogLikelihood(conf, tree2, stree,
gene2species, params)
stuck = False
else:
logl2 = visited[thash][0]
Spidir.setTreeDistances(conf, tree2, distmat, labels)
logl2 = Spidir.treeLogLikelihood(conf, tree2, stree,
gene2species, params)
if nproposals == 1:
stuck = True
addVisited(conf, visited, tree2, gene2species, thash)
debug("logl2", logl2)
if logl2 > logl:
logl = logl2
tree = tree2
break
if nproposals == 1:
logl = logl2
tree = tree2
break
heat *= .5
# undo reversals
for logl3, edge, change in util.reverse(proposals2[start:start+nproposals]):
proposeNni(tree, edge[0], edge[1], change)
debug("start:", start)
debug("swaps:", nproposals)
debug("heat:", heat)
debug("stuck:", stuck)
items = visited.items()
i = util.argmaxfunc(lambda x: x[1][0], items)
thash, (logl, tree, count) = items[i]
return tree, logl
def proposeTree3(conf, tree, distmat, labels,
stree, gene2species, params, visited):
toplogl = tree.data["logl"]
toptree = tree.copy()
tree = tree.copy()
nodes = tree.nodes.values()
nodes.remove(tree.root)
weights = [1 for x in nodes] #[x.data["error"] for x in nodes]
badgene = nodes[stats.sample(weights)]
# detemine distance from badgene to everyone else
dists = util.Dict(default=-util.INF)
def walk(node, dist):
dists[node.name] = dist
for child in node.children:
walk(child, dist + child.dist)
walk(badgene, 0)
seen = set([badgene])
node = badgene.parent
dist = badgene.dist
while node != None:
for child in node.children:
if child not in seen:
walk(child, dist)
seen.add(node)
dist += node.dist
node = node.parent
tree1, tree2 = splitTree(tree, badgene, badgene.parent)
names = tree1.nodes.keys()
names.remove(tree1.root.name)
#names.sort(key=lambda x: dists[x])
random.shuffle(names)
for name in names[:min(len(names), conf["regraftloop"])]:
tree = tree1.copy()
node = tree.nodes[name]
#print "p3>>", node.name, node.parent.name
regraftTree(tree, tree2.copy(), node, node.parent)
thash = phylo.hash_tree(tree)
if thash not in visited:
Spidir.setTreeDistances(conf, tree, distmat, labels)
logl = Spidir.treeLogLikelihood(conf, tree, stree, gene2species, params)
addVisited(conf, visited, tree, gene2species, thash)
logl, tree, count = visited[thash]
if logl > toplogl:
toplogl = logl
toptree = tree
# try returning immediately
#return toptree
assert toptree != None
return toptree
tree = readTree("../test/0.nt.tree")
stree = readTree("../test/flies.stree")
gene2species = genomeutil.readGene2species("../test/flies.smap")
params = Spidir.readParams("../test/flies.nt.param")
drawTree(tree)
print sum(x.dist for x in tree)
print Spidir.estGeneRate(tree, stree, params, gene2species)
print Spidir.estGeneRate(tree, stree, params, gene2species)
print Spidir.estGeneRate(tree, stree, params, gene2species)
print Spidir.Likelihood.getBaserate(tree,
stree,
params,
gene2species=gene2species)
conf = {"python_only": True, "famprob": True}
print Spidir.treeLogLikelihood(conf, tree, stree, gene2species, params)
conf = {}
#generate = Spidir.estGeneRate(tree, stree, params, gene2species)
for generate in frange(1.5, 2.3, .05):
print generate, Spidir.treeLogLikelihood(conf,
tree,
stree,
gene2species,
params,
baserate=generate)