def getSiteLikes(self):
"""Likelihoods, not log likes. Placed in self.siteLikes, a list."""
self._commonCStuff()
self.logLike = pf.p4_treeLogLike(self.cTree, 1) # second arg is getSiteLikes
self.siteLikes = []
for p in self.data.parts:
self.siteLikes += pf.getSiteLikes(p.cPart)
def getSiteLikes(self):
"""Likelihoods, not log likes. Placed in self.siteLikes, a list."""
self._commonCStuff()
self.logLike = pf.p4_treeLogLike(self.cTree,
1) # second arg is getSiteLikes
self.siteLikes = []
for p in self.data.parts:
self.siteLikes += pf.getSiteLikes(p.cPart)
def calcLogLike(self, verbose=1, resetEmpiricalComps=True):
"""Calculate the likelihood of the tree, without optimization."""
self._commonCStuff(resetEmpiricalComps=resetEmpiricalComps)
#print "about to p4_treeLogLike()..."
self.logLike = pf.p4_treeLogLike(self.cTree, 0) # second arg is getSiteLikes
if verbose:
print("Tree.calcLogLike(). %f" % self.logLike)
def optLogLike(self,
verbose=1,
newtAndBrentPowell=1,
allBrentPowell=0,
simplex=0):
"""Calculate the likelihood of the tree, with optimization.
There are 3 optimization methods-- choose one. I've made
'newtAndBrentPowell' the default, as it is fast and seems to be
working. The 'allBrentPowell' optimizer used to be the default,
as it seems to be the most robust, although it is slow. It would
be good for checking important calculations. The simplex
optimizer is the slowest, and will sometimes find better optima
for difficult data, but often fails to optimize (with no
warning)."""
if verbose:
theStartTime = time.clock()
self._commonCStuff()
# We want only one opt method.
if newtAndBrentPowell:
newtAndBrentPowell = 1
if allBrentPowell:
allBrentPowell = 1
if simplex:
simplex = 1
if (newtAndBrentPowell + allBrentPowell + simplex) != 1:
gm = ['Tree.optLogLike()']
gm.append("Choose 1 opt method.")
raise Glitch, gm
# Do the opt.
if allBrentPowell:
pf.p4_allBrentPowellOptimize(self.cTree)
elif simplex:
from Tree import Tree
pf.p4_simplexOptimize(self.cTree, self, Tree.simplexDump)
else:
pf.p4_newtSetup(self.cTree)
pf.p4_newtAndBrentPowellOpt(self.cTree)
self.logLike = pf.p4_treeLogLike(self.cTree,
0) # second arg is getSiteLikes
# get the brLens
brLens = pf.p4_getBrLens(self.cTree)
for n in self.iterNodesNoRoot():
n.br.len = brLens[n.nodeNum]
# get the other free prams
prams = pf.p4_getFreePrams(self.cTree)
self.model.restoreFreePrams(prams)
if verbose:
print "optLogLike = %f" % self.logLike
theEndTime = time.clock()
print "cpu time %s seconds." % (theEndTime - theStartTime)
def calcLogLike(self, verbose=1, resetEmpiricalComps=True):
"""Calculate the likelihood of the tree, without optimization."""
self._commonCStuff(resetEmpiricalComps=resetEmpiricalComps)
#print "about to p4_treeLogLike()..."
self.logLike = pf.p4_treeLogLike(self.cTree,
0) # second arg is getSiteLikes
if verbose:
print "Tree.calcLogLike(). %f" % self.logLike
def optLogLike(self, verbose=1, newtAndBrentPowell=1, allBrentPowell=0, simplex=0):
"""Calculate the likelihood of the tree, with optimization.
There are 3 optimization methods-- choose one. I've made
'newtAndBrentPowell' the default, as it is fast and seems to be
working. The 'allBrentPowell' optimizer used to be the default,
as it seems to be the most robust, although it is slow. It would
be good for checking important calculations. The simplex
optimizer is the slowest, and will sometimes find better optima
for difficult data, but often fails to optimize (with no
warning)."""
if verbose:
theStartTime = time.clock()
self._commonCStuff()
# We want only one opt method.
if newtAndBrentPowell:
newtAndBrentPowell = 1
if allBrentPowell:
allBrentPowell = 1
if simplex:
simplex = 1
if (newtAndBrentPowell + allBrentPowell + simplex) != 1:
gm = ['Tree.optLogLike()']
gm.append("Choose 1 opt method.")
raise Glitch(gm)
# Do the opt.
if allBrentPowell:
pf.p4_allBrentPowellOptimize(self.cTree)
elif simplex:
from .Tree import Tree
pf.p4_simplexOptimize(self.cTree, self, Tree.simplexDump)
else:
pf.p4_newtSetup(self.cTree)
pf.p4_newtAndBrentPowellOpt(self.cTree)
self.logLike = pf.p4_treeLogLike(self.cTree, 0) # second arg is getSiteLikes
# get the brLens
brLens = pf.p4_getBrLens(self.cTree)
for n in self.iterNodesNoRoot():
n.br.len = brLens[n.nodeNum]
# get the other free prams
prams = pf.p4_getFreePrams(self.cTree)
self.model.restoreFreePrams(prams)
if verbose:
print("optLogLike = %f" % self.logLike)
theEndTime = time.clock()
print("cpu time %s seconds." % (theEndTime - theStartTime))
def optLogLike(self, verbose=1, newtAndBrentPowell=1, allBrentPowell=0):
"""Calculate the likelihood of the tree, with optimization.
There are two optimization methods-- choose one. I've made
'newtAndBrentPowell' the default, as it is fast and seems to be
working. The 'allBrentPowell' optimizer used to be the default,
as it seems to be the most robust, although it is slow. It would
be good for checking important calculations.
"""
if verbose:
theStartTime = time.clock()
self._commonCStuff()
# We want only one opt method.
if newtAndBrentPowell:
newtAndBrentPowell = 1
if allBrentPowell:
allBrentPowell = 1
if (newtAndBrentPowell + allBrentPowell) != 1:
gm = ['Tree.optLogLike()']
gm.append("Choose 1 opt method.")
raise P4Error(gm)
# Do the opt.
if allBrentPowell:
pf.p4_allBrentPowellOptimize(self.cTree)
else:
pf.p4_newtSetup(self.cTree)
pf.p4_newtAndBrentPowellOpt(self.cTree)
# second arg is getSiteLikes
self.logLike = pf.p4_treeLogLike(self.cTree, 0)
# get the brLens
brLens = pf.p4_getBrLens(self.cTree)
for n in self.iterNodesNoRoot():
n.br.len = brLens[n.nodeNum]
# get the other free prams
prams = pf.p4_getFreePrams(self.cTree)
self.model.restoreFreePrams(prams)
if verbose:
print "optLogLike = %f" % self.logLike
theEndTime = time.clock()
print "cpu time %s seconds." % (theEndTime - theStartTime)
def optTest(self):
self._commonCStuff()
theStartTime = time.clock()
doXfer = 0
for i in range(1):
if doXfer:
self.model.setCStuff()
self.setCStuff()
pf.p4_setPrams(self.cTree, -1)
self.logLike = pf.p4_treeLogLike(self.cTree, 0)
if doXfer:
# get the brLens
brLens = pf.p4_getBrLens(self.cTree)
for i in range(len(self.nodes)):
n = self.nodes[i]
if n != self.root:
n.br.len = brLens[i]
# get the other free prams
prams = pf.p4_getFreePrams(self.cTree)
self.model.restoreFreePrams(prams)
print "time %s seconds." % (time.clock() - theStartTime)
def optTest(self):
self._commonCStuff()
theStartTime = time.clock()
doXfer = 0
for i in range(1):
if doXfer:
self.model.setCStuff()
self.setCStuff()
pf.p4_setPrams(self.cTree, -1)
self.logLike = pf.p4_treeLogLike(self.cTree, 0)
if doXfer:
# get the brLens
brLens = pf.p4_getBrLens(self.cTree)
for i in range(len(self.nodes)):
n = self.nodes[i]
if n != self.root:
n.br.len = brLens[i]
# get the other free prams
prams = pf.p4_getFreePrams(self.cTree)
self.model.restoreFreePrams(prams)
print("time %s seconds." % (time.clock() - theStartTime))
def getSiteRates(self):
"""Get posterior mean site rate, and gamma category.
This says two things --
1. The posterior mean site rate, calculated like PAML
2. Which GDASRV category contributes most to the likelihood.
The posterior mean site rate calculation requires that there be
only one gdasrv over the tree, which will usually be the case.
For placement in categories, if its a tie score, then it is placed
in the first one.
The list of site rates, and the list of categories, both with one
value for each site, are put into separate numpy arrays, returned
as a list, ie [siteRatesArray, categoriesArray]
There is one of these lists for each data partition, and the results as a
whole are returned as a list. So if you only have one data
partition, then you get a 1-item list, and that single item is a list with 2
numpy arrays. Ie [[siteRatesArray, categoriesArray]]
If nGammaCat for a partition is 1, it will give that partition an
array of ones for the site rates and zeros for the categories.
"""
self._commonCStuff()
self.logLike = pf.p4_treeLogLike(self.cTree,
0) # second arg is getSiteLikes
#self.winningGammaCats = []
#for p in self.data.parts:
# self.winningGammaCats += pf.getWinningGammaCats(p.cPart)
results = []
for partNum in range(len(self.data.parts)):
if len(self.model.parts[partNum].gdasrvs) > 1:
gm = ['Tree.getSiteRates()']
gm.append("Part %i has %i gdasrvs. Maximum 1 allowed." %
(partNum, len(self.model.parts[partNum].gdasrvs)))
raise Glitch, gm
for partNum in range(len(self.data.parts)):
p = self.data.parts[partNum]
if self.model.parts[partNum].nGammaCat == 1:
siteRates = numpy.ones(p.nChar, numpy.float)
gammaCats = numpy.zeros(p.nChar, numpy.int32)
elif self.model.parts[partNum].nGammaCat > 1:
siteRates = numpy.zeros(p.nChar, numpy.float)
gammaCats = numpy.zeros(p.nChar, numpy.int32)
work = numpy.zeros(self.model.parts[partNum].nGammaCat,
numpy.float)
for charNum in range(p.nChar):
gammaCats[charNum] = -1
#pf.getWinningGammaCats(self.cTree, p.cPart, i, gammaCats, work)
pf.getSiteRates(self.cTree, p.cPart, partNum, siteRates, gammaCats,
work)
#print siteRates
#print gammaCats
#print work
if 0:
counts = numpy.zeros(self.model.parts[partNum].nGammaCat,
numpy.int32)
for charNum in range(p.nChar):
counts[winningGammaCats[charNum]] += 1
print counts
else:
raise Glitch, "This should not happen."
results.append([siteRates, gammaCats])
return results
def getSiteRates(self):
"""Get posterior mean site rate, and gamma category.
This says two things --
1. The posterior mean site rate, calculated like PAML
2. Which GDASRV category contributes most to the likelihood.
The posterior mean site rate calculation requires that there be
only one gdasrv over the tree, which will usually be the case.
For placement in categories, if its a tie score, then it is placed
in the first one.
The list of site rates, and the list of categories, both with one
value for each site, are put into separate numpy arrays, returned
as a list, ie [siteRatesArray, categoriesArray]
There is one of these lists for each data partition, and the results as a
whole are returned as a list. So if you only have one data
partition, then you get a 1-item list, and that single item is a list with 2
numpy arrays. Ie [[siteRatesArray, categoriesArray]]
If nGammaCat for a partition is 1, it will give that partition an
array of ones for the site rates and zeros for the categories.
"""
self._commonCStuff()
self.logLike = pf.p4_treeLogLike(self.cTree, 0) # second arg is getSiteLikes
#self.winningGammaCats = []
#for p in self.data.parts:
# self.winningGammaCats += pf.getWinningGammaCats(p.cPart)
results = []
for partNum in range(len(self.data.parts)):
if len(self.model.parts[partNum].gdasrvs) > 1:
gm = ['Tree.getSiteRates()']
gm.append("Part %i has %i gdasrvs. Maximum 1 allowed." % (
partNum, len(self.model.parts[partNum].gdasrvs)))
raise Glitch(gm)
for partNum in range(len(self.data.parts)):
p = self.data.parts[partNum]
if self.model.parts[partNum].nGammaCat == 1:
siteRates = numpy.ones(p.nChar, numpy.float)
gammaCats = numpy.zeros(p.nChar, numpy.int32)
elif self.model.parts[partNum].nGammaCat > 1:
siteRates = numpy.zeros(p.nChar, numpy.float)
gammaCats = numpy.zeros(p.nChar, numpy.int32)
work = numpy.zeros(self.model.parts[partNum].nGammaCat, numpy.float)
for charNum in range(p.nChar):
gammaCats[charNum] = -1
#pf.getWinningGammaCats(self.cTree, p.cPart, i, gammaCats, work)
pf.getSiteRates(self.cTree, p.cPart, partNum, siteRates, gammaCats, work)
#print siteRates
#print gammaCats
#print work
if 0:
counts = numpy.zeros(self.model.parts[partNum].nGammaCat, numpy.int32)
for charNum in range(p.nChar):
counts[winningGammaCats[charNum]] += 1
print(counts)
else:
raise Glitch("This should not happen.")
results.append([siteRates, gammaCats])
return results
