def mixture_model2(modelWh, modelPu):
'model process as single species, with mixture emission, supports multiple matingID'
mixModel = get_mix_model(modelWh, modelPu)
peaSpecies = model.VarFilterState('pea', mixModel)
prior = model.StateGraph({'START': {peaSpecies: 1.0}})
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({
peaSpecies: {
stop: 1.
},
robomendel.noneState: {
stop: 1.
}
})
sct = robomendel.SpeciesCrossTransition()
moms = model.BranchGenerator('mom', prior, iterTag='matingID')
dads = model.BranchGenerator('dad', prior, iterTag='matingID')
dg = model.DependencyGraph(
{
'START': {
moms: {},
dads: {}
},
('mom', 'dad'): {
'child': sct
},
'child': {
'STOP': term
}
},
joinTags=('matingID', ))
return dg
def unrelated_model(modelWh, modelPu):
'model mom, dad, child as independent'
pstate = model.VarFilterState('Pu', modelPu)
wstate = model.VarFilterState('Wh', modelWh)
prior = model.StateGraph({'START': {pstate: 0.9, wstate: 0.1}})
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({
pstate: {
stop: 1.
},
wstate: {
stop: 1.
},
robomendel.noneState: {
stop: 1.
}
})
dg = model.DependencyGraph({
'START': {
'mom': prior,
'dad': prior,
'child': prior
},
'mom': {
'STOP': term
},
'dad': {
'STOP': term
},
'child': {
'STOP': term
}
})
return dg
def mixture_model(modelWh, modelPu):
'model process as single species, with mixture emission'
mixModel = get_mix_model(modelWh, modelPu)
peaSpecies = model.VarFilterState('pea', mixModel)
prior = model.StateGraph({'START': {peaSpecies: 1.0}})
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({
peaSpecies: {
stop: 1.
},
robomendel.noneState: {
stop: 1.
}
})
sct = robomendel.SpeciesCrossTransition()
dg = model.DependencyGraph({
'START': {
'mom': prior,
'dad': prior
},
('mom', 'dad'): {
'child': sct
},
'child': {
'STOP': term
}
})
return dg
def family_model(modelWh, modelPu):
'standard mom x dad --> child model'
pstate, wstate, prior, stop, term, sct = multicond_setup(modelWh, modelPu)
dg = model.DependencyGraph({
'START': {
'mom': prior,
'dad': prior
},
('mom', 'dad'): {
'child': sct
},
'child': {
'STOP': term
}
})
return dg
def subgraph_pl_test(modelDict=dict(mix=mixture_model2,
family=family_model2,
unrelated=unrelated_model2,
environmental=environmental_model2),
obsSet=None):
'build multi-model hypergraph, apply to the obs, and print posterior liklihoods'
p = 1. / len(modelDict) # uninformative prior
modelWh = stats.norm(0, 1)
modelPu = stats.norm(10, 1)
if obsSet is None:
obsSet = get_family_obs(matingID=0)
stop = model.StopState(useObsLabel=False)
d = {}
d2 = {}
for model_name, model_f in modelDict.items(): # build distinct models
state = model.SilentState(model_name)
state.subgraph = model_f(modelWh, modelPu)
d[state] = p
d2[state] = {stop: 1.}
prior = model.StateGraph({'START': d})
term = model.StateGraph(d2)
dg = model.DependencyGraph({
'START': {
'model': prior
},
'model': {
'STOP': term
}
})
m = model.Model(dg, obsSet)
m.segmentGraph.p_forward(m.logPobsDict)
f = m.segmentGraph.fprob[m.start].f
fmerge = {}
for node, logP in f.items(): # merge forward calcs from subgraphs
try:
subgraph = node.segmentGraph
except AttributeError:
pass
else:
merge_forward_dict(subgraph.fprob[subgraph.start].f, logP, fmerge)
llDict = model.posterior_ll(fmerge)
print_pl(llDict)
return llDict
def family_model2(modelWh, modelPu, pHybrid=0.):
'standard mom x dad --> child model, supports multiple matingID'
pstate, wstate, prior, stop, term, sct = multicond_setup(
modelWh, modelPu, pHybrid)
moms = model.BranchGenerator('mom', prior, iterTag='matingID')
dads = model.BranchGenerator('dad', prior, iterTag='matingID')
dg = model.DependencyGraph(
{
'START': {
moms: {},
dads: {}
},
('mom', 'dad'): {
'child': sct
},
'child': {
'STOP': term
}
},
joinTags=('matingID', ))
return dg
def unrelated_model2(modelWh, modelPu):
'model mom, dad, child as independent, supports multiple matingID'
pstate = model.VarFilterState('Pu', modelPu)
wstate = model.VarFilterState('Wh', modelWh)
prior = model.StateGraph({'START': {pstate: 0.9, wstate: 0.1}})
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({
pstate: {
stop: 1.
},
wstate: {
stop: 1.
},
robomendel.noneState: {
stop: 1.
}
})
moms = model.BranchGenerator('mom', prior, iterTag='matingID')
dads = model.BranchGenerator('dad', prior, iterTag='matingID')
kids = model.BranchGenerator('child', prior, iterTag='matingID')
dg = model.DependencyGraph(
{
'START': {
moms: {},
dads: {},
kids: {}
},
'mom': {
'STOP': term
},
'dad': {
'STOP': term
},
'child': {
'STOP': term
}
},
joinTags=('matingID', ))
return dg
def mating_test(species, priors=None, **kwargs):
'generate 2 x 2 test of all possible mating combinations'
if not priors:
priors = (1. / len(species), ) * len(species)
scm = robomendel.SpeciesCrossModel(species, priors, **kwargs)
mstate = model.LinearState('mating', scm)
prior = model.StateGraph({'START': {mstate: 1}})
branches = model.BranchGenerator('chi', prior, iterTag='matingID')
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({mstate: {stop: 1.}})
dg = model.DependencyGraph({
'START': {
branches: {}
},
'chi': {
'STOP': term
}
})
obsSet = model.ObsSet('mating obs')
obsSet.add_obs(species[0].rvs(3), matingID=0)
obsSet.add_obs((species[0].rvs(1)[0], species[1].rvs(1)[0], None),
matingID=1)
obsSet.add_obs((species[0].rvs(1)[0], species[0].rvs(1)[0], None),
matingID=2)
obsSet.add_obs(
(species[0].rvs(1)[0], species[1].rvs(1)[0], species[0].rvs(1)[0]),
matingID=3)
m = model.Model(dg, obsSet)
logPobs = m.calc_fb()
llDict = m.posterior_ll()
for matingID, t in enumerate(
((0, 0, 0), (0, 1, None), (0, 0, None), (0, 1, 0))):
obsLabel = obsSet.get_subset(matingID=matingID)
print 'mating %s:\tlogP = %1.3f, %1.3f, %1.3f' % \
tuple([str(t)] + llDict[obsLabel])
def multicond2_calc(modelWh, modelPu, obsSet):
'''This test creates nodes representing mom, dad and the child,
with a multi-cond edge from (mom,dad) --> child
and tests two different matings simultaneously.'''
pstate, wstate, prior, stop, term, sct = multicond_setup(modelWh, modelPu)
moms = model.BranchGenerator('mom', prior, iterTag='matingID')
dads = model.BranchGenerator('dad', prior, iterTag='matingID')
dg = model.DependencyGraph(
{
'START': {
moms: {},
dads: {}
},
('mom', 'dad'): {
'child': sct
},
'child': {
'STOP': term
}
},
joinTags=('matingID', ))
m = model.Model(dg, obsSet)
return m, m.segmentGraph.p_forward(m.logPobsDict)
def environmental_model(modelWh, modelPu):
'model wh / pu as random extrinsic variable'
def filter_from_node(fromNode, *args):
return dict(var=fromNode.var.label)
pstate = model.VarFilterState('Pu', modelPu, filter_f=filter_from_node)
wstate = model.VarFilterState('Wh', modelWh, filter_f=filter_from_node)
peaSpecies = model.SilentState('pea')
prior = model.StateGraph({'START': {peaSpecies: 1.}})
extSG = model.StateGraph({peaSpecies: {pstate: 0.9, wstate: 0.1}})
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({pstate: {stop: 1.}, wstate: {stop: 1.}})
sct = robomendel.SpeciesCrossTransition()
dg = model.DependencyGraph({
'START': {
'mom': prior,
'dad': prior
},
'mom': {
'ext': extSG
},
'dad': {
'ext': extSG
},
('mom', 'dad'): {
'child': sct
},
'child': {
'ext': extSG
},
'ext': {
'STOP': term
}
})
return dg
def pheno1_setup(modelWh, modelPu):
pstate = model.LinearState('Pu', modelPu)
wstate = model.LinearState('Wh', modelWh)
prior = model.StateGraph({'START': {pstate: 0.9, wstate: 0.1}})
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({pstate: {stop: 1.}, wstate: {stop: 1.}})
branches = model.BranchGenerator('chi', prior, iterTag='plantID')
dg = model.DependencyGraph({
'START': {
branches: {}
},
'chi': {
'STOP': term
}
})
obsSet = model.ObsSet('plants')
for plant in range(2): # two white plants
obsSet.add_obs(modelWh.rvs(100), plantID=plant)
for plant in range(2, 20): # 18 purple plants
obsSet.add_obs(modelPu.rvs(100), plantID=plant)
m = model.Model(dg, obsSet)
return m, obsSet
def environmental_model2(modelWh, modelPu):
'model wh / pu as random extrinsic variable, supports multiple matingID'
def filter_from_node(fromNode, *args):
return dict(var=fromNode.var.label)
pstate = model.VarFilterState('Pu', modelPu, filter_f=filter_from_node)
wstate = model.VarFilterState('Wh', modelWh, filter_f=filter_from_node)
peaSpecies = model.SilentState('pea')
prior = model.StateGraph({'START': {peaSpecies: 1.}})
noSpecies = model.SilentState('no-species')
extNone = model.VarFilterState('ext-none',
model.EmissionDict({None: 1.}),
filter_f=filter_from_node)
extSG = model.StateGraph({
peaSpecies: {
pstate: 0.9,
wstate: 0.1
},
noSpecies: {
extNone: 1.
}
})
stop = model.StopState(useObsLabel=False)
term = model.StateGraph({
pstate: {
stop: 1.
},
wstate: {
stop: 1.
},
extNone: {
stop: 1.
}
})
sct = robomendel.SpeciesCrossTransition(noneState=noSpecies)
moms = model.BranchGenerator('mom', prior, iterTag='matingID')
dads = model.BranchGenerator('dad', prior, iterTag='matingID')
dg = model.DependencyGraph(
{
'START': {
moms: {},
dads: {}
},
'mom': {
'ext': extSG
},
'dad': {
'ext': extSG
},
('mom', 'dad'): {
'child': sct
},
'child': {
'ext': extSG
},
'ext': {
'STOP': term
}
},
joinTags=('matingID', ))
return dg
