def cooperative_tradeoff(community, min_growth, fraction, fluxes, pfba):
"""Find the best tradeoff between community and individual growth."""
with community as com:
check_modification(community)
min_growth = _format_min_growth(min_growth, community.species)
_apply_min_growth(community, min_growth)
com.objective = 1000.0 * com.variables.community_objective
min_growth = optimize_with_retry(
com, message="could not get community growth rate.") / 1000.0
if not isinstance(fraction, Sized):
fraction = [fraction]
else:
fraction = np.sort(fraction)[::-1]
# Add needed variables etc.
regularize_l2_norm(com, 0.0)
results = []
for fr in fraction:
com.variables.community_objective.lb = fr * min_growth
com.variables.community_objective.ub = min_growth
sol = solve(community, fluxes=fluxes, pfba=pfba)
if sol.status != OPTIMAL:
sol = crossover(com, sol, fluxes=fluxes, pfba=pfba)
results.append((fr, sol))
if len(results) == 1:
return results[0][1]
return pd.DataFrame.from_records(results,
columns=["tradeoff", "solution"])
def cooperative_tradeoff(community, min_growth, fraction, fluxes, pfba):
"""Find the best tradeoff between community and individual growth."""
with community as com:
solver = interface_to_str(community.problem)
check_modification(community)
min_growth = _format_min_growth(min_growth, community.taxa)
_apply_min_growth(community, min_growth)
com.objective = com.scale * com.variables.community_objective
min_growth = (optimize_with_retry(
com, message="could not get community growth rate.") / com.scale)
if not isinstance(fraction, Sized):
fraction = [fraction]
else:
fraction = np.sort(fraction)[::-1]
# Add needed variables etc.
regularize_l2_norm(com, 0.0)
results = []
for fr in fraction:
com.variables.community_objective.lb = fr * min_growth
com.variables.community_objective.ub = min_growth
sol = solve(community, fluxes=fluxes, pfba=pfba)
# OSQP is better with QPs then LPs
# so it won't get better with the crossover
if (sol.status != OPTIMAL and solver != "osqp"):
sol = crossover(com, sol, fluxes=fluxes, pfba=pfba)
results.append((fr, sol))
if len(results) == 1:
return results[0][1]
return pd.DataFrame.from_records(results,
columns=["tradeoff", "solution"])
def knockout_taxa(
community, taxa, fraction, method, progress, diag=True
):
"""Knockout a taxon from the community."""
with community as com:
check_modification(com)
min_growth = _format_min_growth(0.0, com.taxa)
_apply_min_growth(com, min_growth)
com.objective = com.scale * com.variables.community_objective
community_min_growth = (
optimize_with_retry(com, "could not get community growth rate.")
/ com.scale
)
regularize_l2_norm(com, fraction * community_min_growth)
old = com.optimize().members["growth_rate"]
results = []
iter = track(taxa, description="Knockouts") if progress else taxa
for sp in iter:
with com:
logger.info("getting growth rates for " "%s knockout." % sp)
[
r.knock_out()
for r in com.reactions.query(
lambda ri: ri.community_id == sp
)
]
sol = optimize_with_fraction(com, fraction)
new = sol.members["growth_rate"]
if "change" in method:
new = new - old
if "relative" in method:
new /= old
results.append(new)
ko = pd.DataFrame(results, index=taxa).drop("medium", 1)
ko = ko.loc[ko.index.sort_values(), ko.columns.sort_values()]
if not diag:
np.fill_diagonal(ko.values, np.NaN)
return ko
def knockout_species(community,
species,
fraction,
method,
progress,
diag=True):
"""Knockout a species from the community."""
with community as com:
check_modification(com)
min_growth = _format_min_growth(0.0, com.species)
_apply_min_growth(com, min_growth)
com.objective = 1000.0 * com.variables.community_objective
community_min_growth = (
optimize_with_retry(com, "could not get community growth rate.") /
1000.0)
regularize_l2_norm(com, fraction * community_min_growth)
old = com.optimize().members["growth_rate"]
results = []
if progress:
species = tqdm(species, unit="knockout(s)")
for sp in species:
with com:
logger.info("getting growth rates for " "%s knockout." % sp)
[
r.knock_out() for r in com.reactions.query(
lambda ri: ri.community_id == sp)
]
sol = optimize_with_fraction(com, fraction)
new = sol.members["growth_rate"]
if "change" in method:
new = new - old
if "relative" in method:
new /= old
results.append(new)
ko = pd.DataFrame(results, index=species).drop("medium", 1)
if not diag:
np.fill_diagonal(ko.values, np.NaN)
return ko
def test_retry(community):
sol = ms.optimize_with_retry(community)
assert sol == approx(0.874, 0.001)