def optgp(model):
"""Return OptGPSampler instance for tests."""
sampler = OptGPSampler(model, processes=1, thinning=1)
assert ((sampler.n_warmup > 0) and
(sampler.n_warmup <= 2 * len(model.variables)))
assert all(sampler.validate(sampler.warmup) == "v")
return sampler
def test_complicated_model(self):
"""Difficult model since the online mean calculation is numerically
unstable so many samples weakly violate the equality constraints."""
model = Model('flux_split')
reaction1 = Reaction('V1')
reaction2 = Reaction('V2')
reaction3 = Reaction('V3')
reaction1.lower_bound = 0
reaction2.lower_bound = 0
reaction3.lower_bound = 0
reaction1.upper_bound = 6
reaction2.upper_bound = 8
reaction3.upper_bound = 10
A = Metabolite('A')
reaction1.add_metabolites({A: -1})
reaction2.add_metabolites({A: -1})
reaction3.add_metabolites({A: 1})
model.add_reactions([reaction1])
model.add_reactions([reaction2])
model.add_reactions([reaction3])
optgp = OptGPSampler(model, 1, seed=42)
achr = ACHRSampler(model, seed=42)
optgp_samples = optgp.sample(100)
achr_samples = achr.sample(100)
assert any(optgp_samples.corr().abs() < 1.0)
assert any(achr_samples.corr().abs() < 1.0)
# > 95% are valid
assert(sum(optgp.validate(optgp_samples) == "v") > 95)
assert(sum(achr.validate(achr_samples) == "v") > 95)
def test_complicated_model():
"""Test a complicated model.
Difficult model since the online mean calculation is numerically
unstable so many samples weakly violate the equality constraints.
"""
model = Model('flux_split')
reaction1 = Reaction('V1')
reaction2 = Reaction('V2')
reaction3 = Reaction('V3')
reaction1.bounds = (0, 6)
reaction2.bounds = (0, 8)
reaction3.bounds = (0, 10)
A = Metabolite('A')
reaction1.add_metabolites({A: -1})
reaction2.add_metabolites({A: -1})
reaction3.add_metabolites({A: 1})
model.add_reactions([reaction1, reaction2, reaction3])
optgp = OptGPSampler(model, 1, seed=42)
achr = ACHRSampler(model, seed=42)
optgp_samples = optgp.sample(100)
achr_samples = achr.sample(100)
assert any(optgp_samples.corr().abs() < 1.0)
assert any(achr_samples.corr().abs() < 1.0)
# > 95% are valid
assert sum(optgp.validate(optgp_samples) == "v") > 95
assert sum(achr.validate(achr_samples) == "v") > 95
def test_complicated_model(self):
"""Difficult model since the online mean calculation is numerically
unstable so many samples weakly violate the equality constraints."""
model = Model('flux_split')
reaction1 = Reaction('V1')
reaction2 = Reaction('V2')
reaction3 = Reaction('V3')
reaction1.lower_bound = 0
reaction2.lower_bound = 0
reaction3.lower_bound = 0
reaction1.upper_bound = 6
reaction2.upper_bound = 8
reaction3.upper_bound = 10
A = Metabolite('A')
reaction1.add_metabolites({A: -1})
reaction2.add_metabolites({A: -1})
reaction3.add_metabolites({A: 1})
model.add_reactions([reaction1])
model.add_reactions([reaction2])
model.add_reactions([reaction3])
optgp = OptGPSampler(model, 1, seed=42)
achr = ACHRSampler(model, seed=42)
optgp_samples = optgp.sample(100)
achr_samples = achr.sample(100)
assert any(optgp_samples.corr().abs() < 1.0)
assert any(achr_samples.corr().abs() < 1.0)
# > 95% are valid
assert (sum(optgp.validate(optgp_samples) == "v") > 95)
assert (sum(achr.validate(achr_samples) == "v") > 95)
def getFluxSample(self, nsamples=5000):
"""
Generates a sample of the flux cone. It uses the default sampler in cobrapy
"""
optGPS = OptGPSampler(self.GEM, thinning=100, processes=3)
samplerSample = optGPS.sample(nsamples)
sample = samplerSample[optGPS.validate(samplerSample) == "v"]
return sample
def setup_class(self):
from . import create_test_model
model = create_test_model("textbook")
achr = ACHRSampler(model, thinning=1)
assert ((achr.n_warmup > 0) and
(achr.n_warmup <= 2 * len(model.variables)))
assert all(achr.validate(achr.warmup) == "v")
self.achr = achr
optgp = OptGPSampler(model, processes=1, thinning=1)
assert ((optgp.n_warmup > 0) and
(optgp.n_warmup <= 2 * len(model.variables)))
assert all(optgp.validate(optgp.warmup) == "v")
self.optgp = optgp
def setup_class(self):
from . import create_test_model
model = create_test_model("textbook")
achr = ACHRSampler(model, thinning=1)
assert ((achr.n_warmup > 0)
and (achr.n_warmup <= 2 * len(model.variables)))
assert all(achr.validate(achr.warmup) == "v")
self.achr = achr
optgp = OptGPSampler(model, processes=1, thinning=1)
assert ((optgp.n_warmup > 0)
and (optgp.n_warmup <= 2 * len(model.variables)))
assert all(optgp.validate(optgp.warmup) == "v")
self.optgp = optgp
def setup_class(self):
from . import create_test_model
model = create_test_model("textbook")
arch = ARCHSampler(model, thinning=1)
assert ((arch.n_warmup > 0)
and (arch.n_warmup <= 2 * len(model.reactions)))
assert all(arch.validate(arch.warmup) == "v")
self.arch = arch
optgp = OptGPSampler(model, processes=1, thinning=1)
assert ((optgp.n_warmup > 0)
and (optgp.n_warmup <= 2 * len(model.reactions)))
assert all(optgp.validate(optgp.warmup) == "v")
self.optgp = optgp
