def _add_prior(self, prior, name):
alpha_prior = xml.prior(prior,
id="covarion_alpha_prior.s:%s" % name,
name="distribution",
x="@covarion_alpha.s:%s" % name)
xml.Uniform(alpha_prior,
id="CovAlphaUniform:%s" % name,
name="distr",
upper="Infinity")
switch_prior = xml.prior(prior,
id="covarion_s_prior.s:%s" % name,
name="distribution",
x="@covarion_s.s:%s" % name)
gamma = xml.Gamma(switch_prior, id="Gamma.0:%s" % name, name="distr")
xml.parameter(gamma,
text="0.05",
id="covarion_switch_gamma_param1:%s" % name,
name="alpha",
lower="0.0",
upper="0.0")
xml.parameter(gamma,
text="10.0",
id="covarion_switch_gamma_param2:%s" % name,
name="beta",
lower="0.0",
upper="0.0")
def add_prior(self, prior):
BaseClock.add_prior(self, prior)
# Gamma prior over rates
sub_prior = xml.prior(prior,
id="RandomRatesPrior.c:%s" % self.name,
name="distribution",
x="@clockrates.c:%s" % self.name)
xml.Gamma(sub_prior,
id="RandomRatesPrior:%s" % self.name,
name="distr",
alpha="@%s" % self.shape_id,
beta="@%s" % self.scale_id)
# Exponential prior over Gamma scale parameter
# (mean param copied from rate heterogeneity implementation in BaseModel)
if self.estimate_variance:
sub_prior = xml.prior(prior,
id="randomClockGammaScalePrior.s:%s" %
self.name,
name="distribution",
x="@%s" % self.shape_id)
xml.Exponential(sub_prior,
id="randomClockGammaScalePriorExponential.s:%s" %
self.name,
mean="0.23",
name="distr")
# Poisson prior over number of rate changes
sub_prior = xml.prior(prior,
id="RandomRateChangesPrior.c:%s" % self.name,
name="distribution")
xml.x(sub_prior,
id="RandomRateChangesCount:%s" % self.name,
spec="util.Sum",
arg="@Indicators.c:%s" % self.name)
poisson = xml.distr(sub_prior,
id="RandomRatechangesPoisson.c:%s" % self.name,
spec="beast.math.distributions.Poisson")
xml.parameter(poisson,
text="0.6931471805599453",
id="RandomRateChangesPoissonLambda:%s" % self.name,
estimate=False,
name="lambda")
def add_prior(self, prior):
# TODO: Lift some logic from beastxml.BeastXML.add_calibration
# and surroundings to parse prior specifications.
# Uniform prior on mean clock rate
sub_prior = xml.prior(prior,
id="clockPrior:%s" % self.name,
name="distribution",
x="@clockRate.c:%s" % self.name)
self.distribution.generate_xml_element(sub_prior)
def add_prior(self, prior):
# Uniform prior on mean clock rate
sub_prior = xml.prior(prior,
id="clockPrior:%s" % self.name,
name="distribution",
x="@clockRate.c:%s" % self.name)
xml.Uniform(sub_prior,
id="UniformClockPrior:%s" % self.name,
name="distr",
upper="Infinity")
def add_prior(self, beastxml):
"""Add a (calibrated) birth-death tree prior."""
# Tree prior
attribs = {}
attribs["id"] = "BirthDeathModel.t:beastlingTree"
attribs["tree"] = "@Tree.t:beastlingTree"
attribs[
"spec"] = "beast.evolution.speciation.BirthDeathGernhard08Model"
attribs["birthDiffRate"] = "@birthRate.t:beastlingTree"
attribs["relativeDeathRate"] = "@deathRate.t:beastlingTree"
attribs["sampleProbability"] = "@sampling.t:beastlingTree"
attribs[
"type"] = "unscaled" #TODO: Someone dropped the "restricted" type here, which does not exist.
xml.distribution(beastxml.prior, attrib=attribs)
# Birth rate prior
attribs = {}
attribs["id"] = "BirthRatePrior.t:beastlingTree"
attribs["name"] = "distribution"
attribs["x"] = "@birthRate.t:beastlingTree"
sub_prior = xml.prior(beastxml.prior, attrib=attribs)
xml.Uniform(sub_prior, id="Uniform.0", name="distr", upper="Infinity")
# Relative death rate prior
attribs = {}
attribs["id"] = "relativeDeathRatePrior.t:beastlingTree"
attribs["name"] = "distribution"
attribs["x"] = "@deathRate.t:beastlingTree"
sub_prior = xml.prior(beastxml.prior, attrib=attribs)
xml.Uniform(sub_prior, id="Uniform.1", name="distr", upper="Infinity")
# Sample probability prior
attribs = {}
attribs["id"] = "samplingPrior.t:beastlingTree"
attribs["name"] = "distribution"
attribs["x"] = "@sampling.t:beastlingTree"
sub_prior = xml.prior(beastxml.prior, attrib=attribs)
xml.Uniform(sub_prior,
id="Uniform.3",
name="distr",
lower="0",
upper="1")
def _add_prior(self, prior, name):
switch_prior = xml.prior(
prior,
id="%s:pdcovarion_s_prior.s" % name,
name="distribution",
x="@%s:pdcovarion_s.s" % name)
gamma = xml.Gamma(switch_prior, id="%s: Gamma.0" % name, name="distr")
xml.parameter(
gamma,
text="0.05",
id="%s:pdcovarion_switch_gamma_param1" % name,
name="alpha",
lower="0.0",
upper="0.0")
xml.parameter(
gamma,
text="10.0",
id="%s:pdcovarion_switch_gamma_param2" % name,
name="beta",
lower="0.0",
upper="0.0")
origin_prior = xml.prior(
prior,
id="%s:pdcovarion_origin_prior.s" % name,
name="distribution",
x="@%s:pdcovarion_origin.s" % name)
xml.Uniform(
origin_prior,
id="%s:PDCovOriginUniform" % name,
name="distr",
upper="Infinity")
death_prior = xml.prior(
prior,
id="%s:pdcovarion_death_prior.s" % name,
name="distribution",
x="@{:}:pdcovarion_death.s".format(name))
xml.Exponential(
death_prior,
id="%s:PDCovDeathExp" % name,
name="distr",
mean="1.0")
def add_prior(self, prior):
"""
Add prior distributions for Gamma-distributed rate heterogenetiy, if
configured.
"""
if self.rate_variation:
# Gamma prior with mean 1 over all mutation rates
sub_prior = xml.prior(prior,
id="featureClockRatePrior.s:%s" % self.name,
name="distribution")
compound = xml.input(sub_prior,
id="featureClockRateCompound:%s" % self.name,
spec="beast.core.parameter.CompoundValuable",
name="x")
plate = xml.plate(compound, var="rate", range=self.all_rates)
xml.var(plate, idref="featureClockRate:%s:$(rate)" % self.name)
xml.input(sub_prior,
id="featureClockRatePriorGamma:%s" % self.name,
spec="beast.math.distributions.Gamma",
name="distr",
alpha="@featureClockRateGammaShape:%s" % self.name,
beta="@featureClockRateGammaScale:%s" % self.name)
# Exponential hyperprior on scale of Gamma prior
# Exponential prior favours small scales over large scales, i.e. less rate variation
# Mean scale 0.23 chosen for general sensibility, e.g.:
# - Prior distribution is roughly 50/50 that ratio of fastest
# to slowest feature rate in a dataset of size 200 is below
# or above 10.
# - Prior probability of roughly 0.90 that this ratio is below
# 100.
sub_prior = xml.prior(
prior,
id="featureClockRateGammaScalePrior.s:%s" % self.name,
name="distribution",
x="@featureClockRateGammaScale:%s" % self.name)
xml.Exponential(
sub_prior,
id="featureClockRateGammaShapePriorExponential.s:%s" %
self.name,
mean="0.23",
name="distr")
def add_prior(self, prior):
RelaxedClock.add_prior(self, prior)
gamma_param_prior = xml.prior(prior,
id="clockRateGammaShapePrior.s:%s" %
self.name,
name="distribution",
x="@clockRateGammaShape:%s" % self.name)
xml.Exponential(gamma_param_prior,
id="clockRateGammaShapePriorExponential.s:%s" %
self.name,
name="distr",
mean="1.0")
def add_prior(self, prior):
"""
Add prior distributions for Gamma-distributed rate heterogenetiy, if
configured.
"""
if self.scale_precision:
precision_prior = xml.prior(prior,
id="sphericalPrecisionPrior",
x="@sphericalPrecision",
name="distribution")
xml.Uniform(precision_prior,
id="sphericalPrecisionPriorUniform",
name="distr",
lower="0",
upper="1e10")
def add_prior(self, prior):
RelaxedClock.add_prior(self, prior)
if self.estimate_variance:
# Gamma prior on the standard deviation for lognormal dist
sub_prior = xml.prior(prior,
id="ucldSdev:%s" % self.name,
name="distribution",
x="@ucldSdev.c:%s" % self.name)
gamma = xml.Gamma(sub_prior,
id="uclSdevPrior:%s" % self.name,
name="distr")
xml.parameter(gamma,
text="0.5396",
id="uclSdevPriorAlpha:%s" % self.name,
estimate="false",
name="alpha")
xml.parameter(gamma,
text="0.3819",
id="uclSdevPriorBeta:%s" % self.name,
estimate="false",
name="beta")
def add_prior(self, beastxml):
"""
Add Yule birth-process tree prior.
"""
# Tree prior
## Decide whether to use the standard Yule or the fancy calibrated one
if len(beastxml.config.calibrations) == 1:
yule = "calibrated"
elif len(beastxml.config.calibrations) == 2:
# Two calibrations can be handled by the calibrated Yule if they
# are nested
langs1, langs2 = [
c.langs for c in beastxml.config.calibrations.values()
]
if len(set(langs1) & set(langs2)) in (len(langs1), len(langs2)):
yule = "calibrated"
else:
yule = "standard"
else:
yule = "standard"
attribs = {}
attribs["id"] = "YuleModel.t:beastlingTree"
attribs["tree"] = "@Tree.t:beastlingTree"
if yule == "standard":
attribs["spec"] = "beast.evolution.speciation.YuleModel"
attribs["birthDiffRate"] = "@birthRate.t:beastlingTree"
if "root" in beastxml.config.calibrations:
attribs["conditionalOnRoot"] = "true"
elif yule == "calibrated":
attribs["spec"] = "beast.evolution.speciation.CalibratedYuleModel"
attribs["birthRate"] = "@birthRate.t:beastlingTree"
xml.distribution(beastxml.prior, attrib=attribs)
# Birth rate prior
sub_prior = xml.prior(beastxml.prior,
id="YuleBirthRatePrior.t:beastlingTree",
name="distribution",
x="@birthRate.t:beastlingTree")
xml.Uniform(sub_prior, id="Uniform.0", name="distr", upper="Infinity")
def add_prior(self, prior):
BaseModel.add_prior(self, prior)
for n, f in enumerate(self.features):
fname = "%s:%s" % (self.name, f)
# Boolean Rate on/off
sub_prior = xml.prior(prior, id="nonZeroRatePrior.s:%s" % fname, name="distribution")
xml.x(sub_prior, arg="@rateIndicator.s:%s" % fname, spec="util.Sum")
N = self.valuecounts[f]
if self.symmetric:
offset = N-1
dim = N*(N-1)/2
else:
offset = N
dim = N*(N-1)
if dim == offset:
# In this situation (e.g. N=2, symmetric), we have no real
# freedom in the number of non-zero rates. So just set a
# uniform prior
xml.distr(
sub_prior,
id="Poisson:%s.%d" % (fname, n),
offset=offset,
spec="beast.math.distributions.Uniform",
lower="0.0",
upper="Infinity")
elif self.svsprior == "poisson":
distr = xml.distr(
sub_prior,
id="Poisson:%s.%d" % (fname, n),
offset=offset,
spec="beast.math.distributions.Poisson")
xml.parameter(
distr,
# Set Poisson mean equal to the midpoint of the range of sensible values
text=(dim - offset) / 2.0,
id="RealParameter:%s.%d.0" % (fname, n),
lower="0.0",
name="lambda",
upper="0.0")
elif self.svsprior == "exponential":
# Set Exponential mean so that 99% of probability density
# lies inside the sensible range
# Exponential quantile function is
# F(p,lambda) = -ln(1-p) / lambda
exponential_mean = math.log(100.0) / (dim - offset)
distr = xml.distr(
sub_prior,
id="Exponential:%s.%d" % (fname, n),
offset=offset,
spec="beast.math.distributions.Exponential")
xml.parameter(
distr,
text=exponential_mean,
id="RealParameter:%s.%d.0" % (fname, n),
lower="0.0",
name="mean",
upper="0.0")
# Relative rate
sub_prior = xml.prior(
prior,
id="relativeGeoRatesPrior.s:%s" % fname,
name="distribution",
x="@relativeGeoRates.s:%s" % fname)
gamma = xml.Gamma(sub_prior, id="Gamma:%s.%d.0" % (fname, n), name="distr")
xml.parameter(
gamma,
text="1.0",
id="RealParameter:%s.%d.1" % (fname, n),
lower="0.0",
name="alpha",
upper="0.0")
xml.parameter(
gamma,
text="1.0",
id="RealParameter:%s.%d.2" % (fname, n),
lower="0.0",
name="beta",
upper="0.0")
