Source code for cobra.sampling.sampling

"""Provide a wrapper function for performing flux sampling of cobra models."""

from typing import TYPE_CHECKING, Optional

import pandas as pd

from .achr import ACHRSampler
from .optgp import OptGPSampler

    from cobra import Model

[docs]def sample( model: "Model", n: int, method: str = "optgp", thinning: int = 100, processes: int = 1, seed: Optional[int] = None, ) -> pd.DataFrame: """Sample valid flux distributions from a cobra model. Currently, two methods are supported: 1. 'optgp' (default) which uses the OptGPSampler that supports parallel sampling. Requires large numbers of samples to be performant (`n` > 1000). For smaller samples, 'achr' might be better suited. For details, refer [1]_ . 2. 'achr' which uses artificial centering hit-and-run. This is a single process method with good convergence. For details, refer [2]_ . Parameters ---------- model : cobra.Model The model from which to sample flux distributions. n : int The number of samples to obtain. When using 'optgp', this must be a multiple of `processes`, otherwise a larger number of samples will be returned. method : {"optgp", "achr"}, optional The sampling algorithm to use (default "optgp"). thinning : int, optional The thinning factor of the generated sampling chain. A thinning of 10 means samples are returned every 10 steps. Defaults to 100 which in benchmarks gives approximately uncorrelated samples. If set to 1 will return all iterates (default 100). processes : int, optional Only used for 'optgp'. The number of processes used to generate samples (default 1). seed : int > 0, optional Sets the random number seed. Initialized to the current time stamp if None (default None). Returns ------- pandas.DataFrame The generated flux samples. Each row corresponds to a sample of the fluxes and the columns are the reactions. Notes ----- The samplers have a correction method to ensure equality feasibility for long-running chains, however this will only work for homogeneous models, meaning models with no non-zero fixed variables or constraints ( right-hand side of the equalities are zero). References ---------- .. [1] Megchelenbrink W, Huynen M, Marchiori E (2014) optGpSampler: An Improved Tool for Uniformly Sampling the Solution-Space of Genome-Scale Metabolic Networks. PLoS ONE 9(2): e86587. .. [2] Direction Choice for Accelerated Convergence in Hit-and-Run Sampling David E. Kaufman, Robert L. Smith Operations Research 199846:1 , 84-95 """ if method == "optgp": sampler = OptGPSampler(model, processes=processes, thinning=thinning, seed=seed) elif method == "achr": sampler = ACHRSampler(model, thinning=thinning, seed=seed) else: raise ValueError( f'Invalid value: "{method}" for method used. ' 'The value must be "optgp" or "achr".' ) return pd.DataFrame( columns=[ for rxn in model.reactions], data=sampler.sample(n) )