cobra.sampling.sampling
¶
Provide a wrapper function for performing flux sampling of cobra models.
Module Contents¶
Functions¶
|
Sample valid flux distributions from a cobra model. |
- cobra.sampling.sampling.sample(model: cobra.Model, n: int, method: str = 'optgp', thinning: int = 100, processes: int = 1, seed: Optional[int] = None) pandas.DataFrame [source]¶
Sample valid flux distributions from a cobra model.
Currently, two methods are supported:
- ‘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 .
‘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
The generated flux samples. Each row corresponds to a sample of the fluxes and the columns are the reactions.
- Return type
pandas.DataFrame
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. https://doi.org/10.1371/journal.pone.0086587
- 2
Direction Choice for Accelerated Convergence in Hit-and-Run Sampling David E. Kaufman, Robert L. Smith Operations Research 199846:1 , 84-95 https://doi.org/10.1287/opre.46.1.84