def maxNeighbor(t0):
toDo = copy.deepcopy(
t0["root"][2]) #Start the to do list with the kids of root.
bestTree = t0 #Use the inputs as the best tree and best score.
bestScore = ps.scoreTree(t0)
print "Neighbors:"
while len(toDo) > 0:
nextNode = toDo.pop()
toDo.extend(copy.deepcopy(t0[nextNode][2]))
if len(t0[nextNode][2]) > 0:
t1, t2 = nniNeighbor(t0, nextNode)
s1 = ps.scoreTree(t1)
s2 = ps.scoreTree(t2)
print "\t", convertNewick(t1, "root"), s1
print "\t", convertNewick(t2, "root"), s2
if s1 < bestScore:
print s1, bestScore
bestTree = t1
bestScore = s1
if s2 < bestScore:
print s2, bestScore
bestTree = t2
bestScore = s2
return bestTree, bestScore
def maxNeighbor(t0): import copy import parsimonyScore as ps toDo = copy.deepcopy(t0["root"][2]) #Start the to do list with the kids of root. bestTree = t0 #Use the inputs as the best tree and best score. bestScore = ps.scoreTree(t0) print "Neighbors:" while len(toDo) > 0: nextNode = toDo.pop() toDo.extend(copy.deepcopy(t0[nextNode][2])) if len(t0[nextNode][2]) > 0: t1, t2 = nniNeighbor(t0,nextNode) s1 = ps.scoreTree(t1) s2 = ps.scoreTree(t2) print "\t", convertNewick(t1, "root"), s1 print "\t", convertNewick(t2, "root"), s2 if s1 < bestScore and bestTree != t1: #print s1, bestScore bestTree = t1 bestScore = s1 if s2 < bestScore and bestTree != t2: #print s2, bestScore bestTree = t2 bestScore = s2 if s1 == bestScore and bestTree != t1: #print s1, bestScore,"E" bestTree = t1 bestScore = s1 if s2 == bestScore and bestTree != t2: #print s2, bestScore,"E" bestTree = t2 bestScore = s2 return bestTree, bestScore
def treeSample(sequences): import parsimonyScore as ps steps = 0 bestTree = buildRandomTree(sequences) bestScore = ps.scoreTree(bestTree) for iteration in range(1000): ## choose random tree newTree = buildRandomTree(sequences) ## score the tree newScore = ps.scoreTree(newTree) #print convertNewick(newTree,"root") #print newScore if newScore < bestScore: bestScore = newScore bestTree = newTree steps += 1 return steps,bestTree,bestScore
def treeSample(sequences):
bestTree = buildRandomTree(sequences)
bestScore = ps.scoreTree(bestTree)
#For 1000 steps:
for i in range(100):
#Choose randomly a tree.
newTree = buildRandomTree(sequences)
#Score the tree.
newScore = ps.scoreTree(newTree)
#We'll add in a print to see what's going on:
print convertNewick(newTree, "root"), newScore
#If better score than <tt>bestScore</tt>,
if newScore < bestScore:
#choose it to be the current tree
bestScore = newScore
bestTree = newTree
#Return best tree and score.
return bestTree, bestScore
def treeSearch(sequences):
#Choose randomly a tree to be bestSoFar.
bestSoFar = buildRandomTree(sequences)
#Score the tree, bestSoFar.
bestScore = ps.scoreTree(bestSoFar)
print "Start:", convertNewick(bestSoFar, "root"), bestScore
#For 1000 steps:
for i in range(5):
# Make a list of all the NNI neighbors of bestSoFar
neighborBest, neighborScore = maxNeighbor(bestSoFar)
# if any of the NNI neighbors of bestSoFar have better score,
if neighborScore < bestScore:
# choose it to be the current tree
bestSoFar = neighborBest
bestScore = neighborScore
print "Best so far: ", convertNewick(bestSoFar, "root"), bestScore
return bestSoFar, bestScore
if __name__ == "__main__":
#Sample tree:
tree = {
"root": ["", "", ["i1", "i2"]],
"i1": ["root", "", ["a", "i3"]],
"i2": ["root", "", ["b", "c"]],
"i3": ["i1", "", ["d", "e"]],
"a": ["i1", "T A A A A", []],
"b": ["i2", "A A A A G", []],
"c": ["i2", "C A A A T", []],
"d": ["i3", "A A A A A", []],
"e": ["i3", "T A A A A", []]
}
print "\nTest tree:", convertNewick(tree, "root"), ":", ps.scoreTree(tree)
#Sample list of (species, sequence) pairs:
seqList = [("ape", "A A A A A"), ("baboon", "A A A A G"),
("chimp", "C A A A A"), ("dog", "T G G G G"),
("elephant", "T G A C A"), ("fox", "A A G G C")]
#Test the tree sampling function:
bestTree, bestScore = treeSample(seqList)
print "\nBest from sample:", convertNewick(bestTree,
"root"), ":", bestScore
print "\n"
t, s = treeSearch(seqList)
print convertNewick(t, "root"), s
