"""Workflow to run cooperative tradeoff with various tradeoff values."""
from cobra.util.solver import OptimizationError
from micom import load_pickle
from micom.logger import logger
from micom.workflows.core import workflow
from micom.workflows.media import process_medium
import numpy as np
from os import path
import pandas as pd
[docs]
def _tradeoff(args):
p, tradeoffs, medium, atol, rtol, presolve = args
com = load_pickle(p)
ex_ids = [r.id for r in com.exchanges]
logger.info(
"%d/%d import reactions found in model.",
medium.index.isin(ex_ids).sum(),
len(medium),
)
com.medium = medium[medium.index.isin(ex_ids)]
com.solver.configuration.presolve = presolve
try:
sol = com.optimize(rtol=rtol, atol=atol)
except Exception:
logger.error(
"Sample %s could not be optimized (%s)." % (com.id, com.solver.status),
)
return None
rates = sol.members
rates["taxon"] = rates.index
rates["tradeoff"] = np.nan
rates["sample_id"] = com.id
df = [rates]
# Get growth rates
try:
sol = com.cooperative_tradeoff(fraction=tradeoffs)
except Exception:
logger.warning(
"Sample %s could not be optimized with cooperative tradeoff (%s)."
% (com.id, com.solver.status),
)
return None
for i, s in enumerate(sol.solution):
rates = s.members
rates["taxon"] = rates.index
rates["tradeoff"] = sol.tradeoff[i]
rates["sample_id"] = com.id
df.append(rates)
df = pd.concat(df)
return df[df.taxon != "medium"]
[docs]
def tradeoff(
manifest,
model_folder,
medium,
tradeoffs=np.arange(0.1, 1.0 + 1e-6, 0.1),
threads=1,
atol=None,
rtol=None,
presolve=False,
):
"""Run growth rate predictions for varying tradeoff values.
Parameters
----------
manifest : pandas.DataFrame
The manifest as returned by the `build` workflow.
model_folder : str
The folder in which to find the files mentioned in the manifest.
medium : pandas.DataFrame
A growth medium. Must have columns "reaction" and "flux" denoting
exchnage reactions and their respective maximum flux.
tradeoffs : array of floats in (0.0, 1.0]
An array of tradeoff vaues to be tested. One simulation without
a tradeoff (no cooperative tradeoff) will always be run additionally
and will have a tradeoff of "NaN".
threads : int >=1
The number of parallel workers to use when building models. As a
rule of thumb you will need around 1GB of RAM for each thread.
atol : float
Absolute tolerance for the growth rates. If None will use the solver tolerance.
rtol : float
Relative tolerqance for the growth rates. If None will use the solver tolerance.
presolve : bool
Whether to use the presolver/scaling. Can improve numerical accuracy in some
cases.
Returns
-------
pandas.DataFrame
The predicted growth rates.
"""
samples = manifest.sample_id.unique()
paths = {
s: path.join(model_folder, manifest[manifest.sample_id == s].file.iloc[0])
for s in samples
}
if any(t < 0.0 or t > 1.0 for t in tradeoffs):
raise ValueError("tradeoff values must between 0 and 1 :(")
medium = process_medium(medium, samples)
args = [
[p, tradeoffs, medium.flux[medium.sample_id == s], atol, rtol, presolve]
for s, p in paths.items()
]
results = workflow(_tradeoff, args, threads)
if all(r is None for r in results):
raise OptimizationError(
"All numerical optimizations failed. This indicates a problem "
"with the solver or numerical instabilities. Check that you have "
"CPLEX or Gurobi installed. You may also increase the abundance "
"cutoff in `qiime micom build` to create simpler models or choose "
"a more permissive solver tolerance."
)
results = pd.concat(results)
return results