def achr(model):
"""Return ACHRSampler instance for tests."""
sampler = ACHRSampler(model, 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():
"""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 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 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 capitulo_7():
file = open("resultados_capitulo_7.txt", "w")
model = create_test_model("textbook")
s = sample(model, 100)
s.head()
print("One process:")
s = sample(model, 1000)
print("Two processes:")
s = sample(model, 1000, processes=2)
s = sample(model, 100, method="achr")
from cobra.flux_analysis.sampling import OptGPSampler, ACHRSampler
achr = ACHRSampler(model, thinning=10)
optgp = OptGPSampler(model, processes=4)
s1 = achr.sample(100)
s2 = optgp.sample(100)
import numpy as np
bad = np.random.uniform(-1000, 1000, size=len(model.reactions))
achr.validate(np.atleast_2d(bad))
achr.validate(s1)
counts = [
np.mean(s.Biomass_Ecoli_core > 0.1) for s in optgp.batch(100, 10)
]
file.write("Usually {:.2f}% +- {:.2f}% grow...".format(
np.mean(counts) * 100.0,
np.std(counts) * 100.0))
file.write("\n")
co = model.problem.Constraint(
model.reactions.Biomass_Ecoli_core.flux_expression, lb=0.1)
model.add_cons_vars([co])
s = sample(model, 10)
file.write(s.Biomass_Ecoli_core)
file.write("\n")
file.close()
def fluxSampling(model):
from cobra.test import create_test_model
from cobra.flux_analysis import sample
model = create_test_model("textbook")
s = sample(model, 100) #number of samples to generate
s.head()
s = sample(model, 1000)
s
#The sampling process can be controlled on a lower level by using the sampler classes directly.
from cobra.flux_analysis.sampling import OptGPSampler, ACHRSampler
achr = ACHRSampler(
model, thinning=10
) #“Thinning” means only recording samples every n iterations. A higher thinning factor means less correlated samples but also larger computation times.
optgp = OptGPSampler(
model, processes=4
) #an additional processes argument specifying how many processes are used to create parallel sampling chains.
#For OptGPSampler the number of samples should be a multiple of the number of
# processes, otherwise it will be increased to the nearest multiple automatically.
s1 = achr.sample(100)
s2 = optgp.sample(100)
# Sampling and validation
import numpy as np
bad = np.random.uniform(-1000, 1000, size=len(model.reactions))
achr.validate(np.atleast_2d(bad)) #should not be feasible
achr.validate(s1)
# Batch sampling
counts = [
np.mean(s.Biomass_Ecoli_core > 0.1) for s in optgp.batch(100, 10)
]
print("Usually {:.2f}% +- {:.2f}% grow...".format(
np.mean(counts) * 100.0,
np.std(counts) * 100.0))
# Adding constraints
co = model.problem.Constraint(
model.reactions.Biomass_Ecoli_core.flux_expression, lb=0.1)
model.add_cons_vars([co])
# Note that this is only for demonstration purposes. usually you could set
# the lower bound of the reaction directly instead of creating a new constraint.
s = sample(model, 10)
print(s.Biomass_Ecoli_core)
def test_achr_init_benchmark(model, benchmark):
"""Benchmark inital ACHR sampling."""
benchmark(lambda: ACHRSampler(model))
def test_achr_init_benchmark(self, model, benchmark):
benchmark(lambda: ACHRSampler(model))