def test_optimize():
optimize(mapk_cascade, 1, 3, max_generation=10, overwrite=True)
for i in range(1, 4):
with open(MODEL_PATH + f'/out/{i:d}/optimization.log') as f:
logs = f.readlines()
assert logs[-1][:14] == 'Generation10: '
optimize_continue(mapk_cascade, 1, 3, max_generation=20)
for i in range(1, 4):
with open(MODEL_PATH + f'/out/{i:d}/optimization.log') as f:
logs = f.readlines()
assert logs[-1][:14] == 'Generation20: '
def run_ga(nth_paramset):
if not os.path.isdir('./out'):
os.mkdir('./out')
try:
files = os.listdir('./out/%d' % (nth_paramset))
for file in files:
if any(map(file.__contains__, ('.npy', 'log'))):
os.remove('./out/%d/%s' % (nth_paramset, file))
except FileNotFoundError:
os.mkdir('./out/%d' % (nth_paramset))
optimize(nth_paramset)
def test_param_estim():
optimize(model,
x_id=11,
options={
"popsize": 3,
"max_generation": 5,
"allowable_error": 0.0,
"local_search_method": "DE",
"maxiter": 3,
"workers": -1,
})
with open(os.path.join(model.path, "out", "11", "optimization.log")) as f:
logs = f.readlines()
assert logs[-1][:13] == "Generation5: "
def test_optimize():
optimize(
model=model,
x_id=range(1, 4),
options={
"popsize": 3,
"max_generation": 3,
"local_search_method": "mutation",
"n_children": 15,
"overwrite": True,
},
)
for paramset in range(1, 4):
with open(
os.path.join(
model.path,
"out",
f"{paramset:d}",
"optimization.log",
)
) as f:
logs = f.readlines()
assert logs[-1][:13] == "Generation3: "
optimize_continue(
model=model,
x_id=range(1, 4),
options={
"popsize": 3,
"max_generation": 6,
"local_search_method": "Powell",
},
)
for paramset in range(1, 4):
with open(
os.path.join(
model.path,
"out",
f"{paramset:d}",
"optimization.log",
)
) as f:
logs = f.readlines()
assert logs[-1][:13] == "Generation6: "
optimize_continue(
model=model,
x_id=range(1, 4),
options={
"popsize": 3,
"max_generation": 9,
"local_search_method": "DE",
},
)
for paramset in range(1, 4):
with open(
os.path.join(
model.path,
"out",
f"{paramset:d}",
"optimization.log",
)
) as f:
logs = f.readlines()
assert logs[-1][:13] == "Generation9: "
#Model construction
import Hass_2017
Hass_2017.show_info()
model = Hass_2017.create()
#Parameter estimation
from biomass import optimize
# Estimate 10 parameter sets simultaneously
optimize(model=model,
start=1,
end=10,
options={
"popsize": 5,
"max_generation": 1000,
"allowable_error": 0.5,
"local_search_method": "mutation",
"n_children": 200
})
#Visualization of simulation results
from biomass import run_simulation
run_simulation(model, viz_type="average", show_all=False, stdev=True)
#Sensitivity analysis
from biomass import run_analysis
run_analysis(model, target="reaction", metric='integral', style='barplot')
def run_ga_continue(nth_paramset):
if not os.path.isdir('./out/%d' % (nth_paramset)):
os.mkdir('./out/%d' % (nth_paramset))
optimize(nth_paramset)
else:
optimize_continue(nth_paramset)