def combine_max_LLH(self, bestpar, tpartitions, pmap, spe_setting = None, fo = "", plot = True):
idxpar = self._convert2idx(bestpar)
bestsupport = [0.0] * self.numtaxa
for par in idxpar:
w = pmap.get(par, 0.0)
for idx in par:
bestsupport[idx] = float(w)/float(len(tpartitions))
self.meansupport = numpy.mean(bestsupport)
spes, support = self._partition2names(bestpar, bestsupport)
if spe_setting != None and plot:
spe_setting = add_bayesain_support(delimitation = spe_setting, pmap = pmap, taxaorder =self.taxaorder, numpar = len(tpartitions))
spe_setting.root.write(features = ["bs"], outfile = fo + ".ml.tre", format = 0)
showTree(delimitation = spe_setting, scale = self.scale, render = True, fout = fo, form = "svg", show_support = True)
showTree(delimitation = spe_setting, scale = self.scale, render = True, fout = fo, form = "png", show_support = True)
fo_bestpar = open(fo, "w")
fo_bestpar.write("# Max likilhood partition \n")
for i in range(len(spes)):
spe = spes[i]
sup = support[i]
fo_bestpar.write("Species " + str(i+1) + " (support = " + "{0:.3f}".format(sup) + ")\n")
fo_bestpar.write(" " + self._print_list(spe) + "\n")
fo_bestpar.close()
def combine_max_LLH(self,
bestpar,
tpartitions,
pmap,
spe_setting=None,
fo="",
plot=True):
idxpar = self._convert2idx(bestpar)
bestsupport = [0.0] * self.numtaxa
for par in idxpar:
w = pmap.get(par, 0.0)
for idx in par:
bestsupport[idx] = float(w) / float(len(tpartitions))
self.meansupport = numpy.mean(bestsupport)
spes, support = self._partition2names(bestpar, bestsupport)
if spe_setting != None and plot:
spe_setting = add_bayesain_support(delimitation=spe_setting,
pmap=pmap,
taxaorder=self.taxaorder,
numpar=len(tpartitions))
spe_setting.root.write(features=["bs"],
outfile=fo + ".ml.tre",
format=0)
showTree(delimitation=spe_setting,
scale=self.scale,
render=True,
fout=fo,
form="svg",
show_support=True)
showTree(delimitation=spe_setting,
scale=self.scale,
render=True,
fout=fo,
form="png",
show_support=True)
fo_bestpar = open(fo, "w")
fo_bestpar.write("# Max likilhood partition \n")
for i in range(len(spes)):
spe = spes[i]
sup = support[i]
fo_bestpar.write("Species " + str(i + 1) + " (support = " +
"{0:.3f}".format(sup) + ")\n")
fo_bestpar.write(" " + self._print_list(spe) + "\n")
fo_bestpar.close()
def combine_simple_heuristic(self, tpartitions, pmap, idxpars, fo, sp_setting = [], plot = True):
maxw = 0
bestpar = None
bestsupport = None
for i in range(len(tpartitions)):
partition = tpartitions[i]
pars = idxpars[i]
support = [0.0] * self.numtaxa
sumw = 0.0
for par in pars:
w = pmap.get(par, 0.0)
for idx in par:
support[idx] = float(w)/float(len(tpartitions))
sumw = sumw + w
if sumw > maxw:
maxw = sumw
bestpar = i
bestsupport = support
self.meansupport = numpy.mean(bestsupport)
spes, support = self._partition2names(tpartitions[bestpar], bestsupport)
spe_setting = sp_setting[bestpar]
if plot:
spe_setting = add_bayesain_support(delimitation = spe_setting, pmap = pmap, taxaorder =self.taxaorder, numpar = len(tpartitions))
spe_setting.root.write(features = ["bs"], outfile = fo + ".sh.tre", format = 0)
showTree(delimitation = spe_setting, scale = self.scale, render = True, fout = fo, form = "svg", show_support = True)
showTree(delimitation = spe_setting, scale = self.scale, render = True, fout = fo, form = "png", show_support = True)
fo_bestpar = open(fo, "w")
fo_bestpar.write("# Most supported partition found by simple heuristic search\n")
for i in range(len(spes)):
spe = spes[i]
sup = support[i]
fo_bestpar.write("Species " + str(i+1) + " (support = " + "{0:.3f}".format(sup) + ")\n")
fo_bestpar.write(" " + self._print_list(spe) + "\n")
fo_bestpar.close()
return tpartitions[bestpar]
def combine_simple_heuristic(self,
tpartitions,
pmap,
idxpars,
fo,
sp_setting=[],
plot=True):
maxw = 0
bestpar = None
bestsupport = None
for i in range(len(tpartitions)):
partition = tpartitions[i]
pars = idxpars[i]
support = [0.0] * self.numtaxa
sumw = 0.0
for par in pars:
w = pmap.get(par, 0.0)
for idx in par:
support[idx] = float(w) / float(len(tpartitions))
sumw = sumw + w
if sumw > maxw:
maxw = sumw
bestpar = i
bestsupport = support
self.meansupport = numpy.mean(bestsupport)
spes, support = self._partition2names(tpartitions[bestpar],
bestsupport)
spe_setting = sp_setting[bestpar]
if plot:
spe_setting = add_bayesain_support(delimitation=spe_setting,
pmap=pmap,
taxaorder=self.taxaorder,
numpar=len(tpartitions))
spe_setting.root.write(features=["bs"],
outfile=fo + ".sh.tre",
format=0)
showTree(delimitation=spe_setting,
scale=self.scale,
render=True,
fout=fo,
form="svg",
show_support=True)
showTree(delimitation=spe_setting,
scale=self.scale,
render=True,
fout=fo,
form="png",
show_support=True)
fo_bestpar = open(fo, "w")
fo_bestpar.write(
"# Most supported partition found by simple heuristic search\n")
for i in range(len(spes)):
spe = spes[i]
sup = support[i]
fo_bestpar.write("Species " + str(i + 1) + " (support = " +
"{0:.3f}".format(sup) + ")\n")
fo_bestpar.write(" " + self._print_list(spe) + "\n")
fo_bestpar.close()
return tpartitions[bestpar]
