/usr/lib/python2.7/dist-packages/cogent/evolve/likelihood_calculation.py is in python-cogent 1.9-9.
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"""This file controls the central function of EVOLVE, the calculation of the log-
likelihood of an alignment given a phylogenetic tree and substitution model.
The likelihood calculation is done according to Felsenstein's 1981 pruning
algorithm. This file contains a Python implementation of that
algorithm and an interface to a more computationally efficient Pyrex
implementation. The two versions are maintained for the purpose of cross-
validating accuracy. The calculations can be performed for tree's that have polytomies
in addition to binary trees.
"""
import numpy
Float = numpy.core.numerictypes.sctype2char(float)
from cogent.recalculation.definition import CalculationDefn, _FuncDefn, \
CalcDefn, ProbabilityParamDefn, NonParamDefn, SumDefn, CallDefn, \
ParallelSumDefn
from cogent.evolve.likelihood_tree import LikelihoodTreeEdge
from cogent.evolve.simulate import argpick
from cogent.maths.markov import SiteClassTransitionMatrix
__author__ = "Peter Maxwell"
__copyright__ = "Copyright 2007-2016, The Cogent Project"
__credits__ = ["Peter Maxwell", "Gavin Huttley"]
__license__ = "GPL"
__version__ = "1.9"
__maintainer__ = "Peter Maxwell"
__email__ = "pm67nz@gmail.com"
__status__ = "Production"
class _PartialLikelihoodDefn(CalculationDefn):
def setup(self, edge_name):
self.edge_name = edge_name
class LeafPartialLikelihoodDefn(_PartialLikelihoodDefn):
name = "sequence"
def calc(self, lh_tree):
lh_leaf = lh_tree.getEdge(self.edge_name)
return lh_leaf.input_likelihoods
class PartialLikelihoodProductDefn(_PartialLikelihoodDefn):
name = "plh"
recycling = True
def calc(self, recycled_result, lh_edge, *child_likelihoods):
if recycled_result is None:
recycled_result = lh_edge.makePartialLikelihoodsArray()
return lh_edge.sumInputLikelihoodsR(recycled_result, *child_likelihoods)
class PartialLikelihoodProductDefnFixedMotif(PartialLikelihoodProductDefn):
def calc(self, recycled_result, fixed_motif, lh_edge, *child_likelihoods):
if recycled_result is None:
recycled_result = lh_edge.makePartialLikelihoodsArray()
result = lh_edge.sumInputLikelihoodsR(
recycled_result, *child_likelihoods)
if fixed_motif not in [None, -1]:
for motif in range(result.shape[-1]):
if motif != fixed_motif:
result[:, motif] = 0.0
return result
class LhtEdgeLookupDefn(CalculationDefn):
name = 'col_index'
def setup(self, edge_name):
self.edge_name = edge_name
# so that it can be found by reconstructAncestralSeqs etc:
if edge_name == 'root':
self.name = 'root'
def calc(self, lht):
return lht.getEdge(self.edge_name)
def makePartialLikelihoodDefns(edge, lht, psubs, fixed_motifs):
kw = {'edge_name':edge.Name}
if edge.istip():
plh = LeafPartialLikelihoodDefn(lht, **kw)
else:
lht_edge = LhtEdgeLookupDefn(lht, **kw)
children = []
for child in edge.Children:
child_plh = makePartialLikelihoodDefns(child, lht, psubs,
fixed_motifs)
psub = psubs.selectFromDimension('edge', child.Name)
child_plh = CalcDefn(numpy.inner)(child_plh, psub)
children.append(child_plh)
if fixed_motifs:
fixed_motif = fixed_motifs.selectFromDimension('edge', edge.Name)
plh = PartialLikelihoodProductDefnFixedMotif(
fixed_motif, lht_edge, *children, **kw)
else:
plh = PartialLikelihoodProductDefn(lht, *children, **kw)
return plh
def recursive_lht_build(edge, leaves):
if edge.istip():
lhe = leaves[edge.Name]
else:
lht_children = []
for child in edge.Children:
lht = recursive_lht_build(child, leaves)
lht_children.append(lht)
lhe = LikelihoodTreeEdge(lht_children, edge_name=edge.Name)
return lhe
class LikelihoodTreeDefn(CalculationDefn):
name = 'lht'
def setup(self, tree):
self.tree = tree
def calc(self, leaves):
return recursive_lht_build(self.tree, leaves)
class LikelihoodTreeAlignmentSplitterDefn(CalculationDefn):
name = 'local_lht'
def calc(self, parallel_context, lht):
return lht.parallelShare(parallel_context)
def makeTotalLogLikelihoodDefn(tree, leaves, psubs, mprobs, bprobs, bin_names,
locus_names, sites_independent):
fixed_motifs = NonParamDefn('fixed_motif', ['edge'])
lht = LikelihoodTreeDefn(leaves, tree=tree)
# Split up the alignment columns between the available CPUs.
parallel_context = NonParamDefn('parallel_context')
lht = LikelihoodTreeAlignmentSplitterDefn(parallel_context, lht)
plh = makePartialLikelihoodDefns(tree, lht, psubs, fixed_motifs)
# After the root partial likelihoods have been calculated it remains to
# sum over the motifs, local sites, other sites (ie: cpus), bins and loci.
# The motifs are always done first, but after that it gets complicated.
# If a bin HMM is being used then the sites from the different CPUs must
# be interleaved first, otherwise summing over the CPUs is done last to
# minimise inter-CPU communicaton.
root_mprobs = mprobs.selectFromDimension('edge', 'root')
lh = CalcDefn(numpy.inner, name='lh')(plh, root_mprobs)
if len(bin_names) > 1:
if sites_independent:
site_pattern = CalcDefn(BinnedSiteDistribution, name='bdist')(
bprobs)
else:
parallel_context = None # hmm does the gathering over CPUs
switch = ProbabilityParamDefn('bin_switch', dimensions=['locus'])
site_pattern = CalcDefn(PatchSiteDistribution, name='bdist')(
switch, bprobs)
blh = CallDefn(site_pattern, lht, name='bindex')
tll = CallDefn(blh, *lh.acrossDimension('bin', bin_names),
**dict(name='tll'))
else:
lh = lh.selectFromDimension('bin', bin_names[0])
tll = CalcDefn(log_sum_across_sites, name='logsum')(lht, lh)
if len(locus_names) > 1 or parallel_context is None:
# "or parallel_context is None" only because SelectFromDimension
# currently has no .makeParamController() method.
tll = SumDefn(*tll.acrossDimension('locus', locus_names))
else:
tll = tll.selectFromDimension('locus', locus_names[0])
if parallel_context is not None:
tll = ParallelSumDefn(parallel_context, tll)
return tll
def log_sum_across_sites(root, root_lh):
return root.getLogSumAcrossSites(root_lh)
class BinnedSiteDistribution(object):
def __init__(self, bprobs):
self.bprobs = bprobs
def getWeightedSumLh(self, lhs):
result = numpy.zeros(lhs[0].shape, lhs[0].dtype.char)
temp = numpy.empty(result.shape, result.dtype.char)
for (bprob, lh) in zip(self.bprobs, lhs):
temp[:] = lh
temp *= bprob
result += temp
return result
def __call__(self, root):
return BinnedLikelihood(self, root)
def emit(self, length, random_series):
result = numpy.zeros([length], int)
for i in range(length):
result[i] = argpick(self.bprobs, random_series)
return result
class PatchSiteDistribution(object):
def __init__(self, switch, bprobs):
half = len(bprobs) // 2
self.alloc = [0] * half + [1] * (len(bprobs)-half)
pprobs = numpy.zeros([max(self.alloc)+1], Float)
for (b,p) in zip(self.alloc, bprobs):
pprobs[b] += p
self.bprobs = [p/pprobs[self.alloc[i]] for (i,p) in enumerate(bprobs)]
self.transition_matrix = SiteClassTransitionMatrix(switch, pprobs)
def getWeightedSumLhs(self, lhs):
result = numpy.zeros((2,)+lhs[0].shape, lhs[0].dtype.char)
temp = numpy.empty(lhs[0].shape, result.dtype.char)
for (patch, weight, lh) in zip(self.alloc, self.bprobs, lhs):
temp[:] = lh
temp *= weight
result[patch] += temp
return result
def __call__(self, root):
return SiteHmm(self, root)
def emit(self, length, random_series):
bprobs = [[p for (patch,p) in zip(self.alloc, self.bprobs) if patch==a]
for a in [0,1]]
source = self.transition_matrix.emit(random_series)
result = numpy.zeros([length], int)
for i in range(length):
patch = source.next() - 1
result[i] = argpick(bprobs[patch], random_series)
return result
class BinnedLikelihood(object):
def __init__(self, distrib, root):
self.distrib = distrib
self.root = root
def __call__(self, *lhs):
result = self.distrib.getWeightedSumLh(lhs)
return self.root.getLogSumAcrossSites(result)
def getPosteriorProbs(self, *lhs):
# posterior bin probs, not motif probs
assert len(lhs) == len(self.distrib.bprobs)
result = numpy.array(
[b*self.root.getFullLengthLikelihoods(p)
for (b,p) in zip(self.distrib.bprobs, lhs)])
result /= result.sum(axis=0)
return result
class SiteHmm(object):
def __init__(self, distrib, root):
self.root = root
self.distrib = distrib
def __call__(self, *lhs):
plhs = self.distrib.getWeightedSumLhs(lhs)
plhs = numpy.ascontiguousarray(numpy.transpose(plhs))
matrix = self.distrib.transition_matrix
return self.root.logDotReduce(
matrix.StationaryProbs, matrix.Matrix, plhs)
def getPosteriorProbs(self, *lhs):
plhs = []
for lh in self.distrib.getWeightedSumLhs(lhs):
plh = self.root.getFullLengthLikelihoods(lh)
plhs.append(plh)
plhs = numpy.transpose(plhs)
pprobs = self.distrib.transition_matrix.getPosteriorProbs(plhs)
pprobs = numpy.array(numpy.transpose(pprobs))
lhs = numpy.array(lhs)
blhs = lhs / numpy.sum(lhs, axis=0)
blhs = numpy.array(
[b * self.root.getFullLengthLikelihoods(p)
for (b,p) in zip(self.distrib.bprobs, blhs)])
binsum = numpy.zeros(pprobs.shape, Float)
for (patch, data) in zip(self.distrib.alloc, blhs):
binsum[patch] += data
for (patch, data) in zip(self.distrib.alloc, blhs):
data *= pprobs[patch] / binsum[patch]
return blhs
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