Source code for micom.workflows.tradeoff

"""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 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 = [ for r in com.exchanges] "%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.solver.status), ) return None rates = sol.members rates["taxon"] = rates.index rates["tradeoff"] = np.nan rates["sample_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.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"] = 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