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 test_crossover(community):
sol = community.cooperative_tradeoff(fraction=1.0)
gcs = sol.members.growth_rate.dropna()
sol = ms.crossover(community, sol)
for i, g in enumerate(gcs):
sol.members.growth_rate[i] == approx(g)