# Source code for cobra.sampling.optgp

```
"""Provide the OptGP sampler class and helper functions."""
from typing import TYPE_CHECKING, Dict, Optional, Tuple
import numpy as np
import pandas as pd
from ..core.configuration import Configuration
from ..util import ProcessPool
from .core import step
from .hr_sampler import HRSampler, shared_np_array
if TYPE_CHECKING:
from cobra import Model
__all__ = ("OptGPSampler",)
configuration = Configuration()
[docs]class OptGPSampler(HRSampler):
"""
Improved Artificial Centering Hit-and-Run sampler.
A parallel sampler with fast convergence and parallel execution.
See [1]_ for details.
Parameters
----------
model : cobra.Model
The cobra model from which to generate samples.
processes: int, optional
The number of processes used during sampling
(default cobra.Configuration.processes).
thinning : int, optional
The thinning factor of the generated sampling chain. A thinning of
10 means samples are returned every 10 steps (default 100).
nproj : int > 0, optional
How often to reproject the sampling point into the feasibility
space. Avoids numerical issues at the cost of lower sampling. If
you observe many equality constraint violations with
`sampler.validate` you should lower this number (default None).
seed : int > 0, optional
Sets the random number seed. Initialized to the current time stamp
if None (default None).
Attributes
----------
n_samples : int
The total number of samples that have been generated by this
sampler instance.
problem : typing.NamedTuple
A NamedTuple whose attributes define the entire sampling problem in
matrix form.
warmup : numpy.matrix
A numpy matrix with as many columns as reactions in the model and
more than 3 rows containing a warmup sample in each row. None if no
warmup points have been generated yet.
retries : int
The overall of sampling retries the sampler has observed. Larger
values indicate numerical instabilities.
fwd_idx : numpy.array
A numpy array having one entry for each reaction in the model,
containing the index of the respective forward variable.
rev_idx : numpy.array
A numpy array having one entry for each reaction in the model,
containing the index of the respective reverse variable.
prev : numpy.array
The current/last flux sample generated.
center : numpy.array
The center of the sampling space as estimated by the mean of all
previously generated samples.
Notes
-----
The sampler is very similar to artificial centering where each process
samples its own chain. Initial points are chosen randomly from the
warmup points followed by a linear transformation that pulls the points
a little bit towards the center of the sampling space.
If the number of processes used is larger than the one requested,
number of samples is adjusted to the smallest multiple of the number of
processes larger than the requested sample number. For instance, if you
have 3 processes and request 8 samples, you will receive 9.
Memory usage is roughly in the order of (2 * number of reactions)^2
due to the required nullspace matrices and warmup points. So, large
models easily take up a few GBs of RAM. However, most of the large
matrices are kept in shared memory. So the RAM usage is independent of
the number of processes.
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
"""
def __init__(
self,
model: "Model",
thinning: int = 100,
processes: Optional[int] = None,
nproj: Optional[int] = None,
seed: Optional[int] = None,
**kwargs
) -> None:
"""Initialize a new OptGPSampler."""
super().__init__(model, thinning, nproj=nproj, seed=seed, *kwargs)
self.generate_fva_warmup()
if processes is None:
self.processes = configuration.processes
else:
self.processes = processes
# This maps our saved center into shared memory,
# meaning they are synchronized across processes
self.center = shared_np_array(
(len(self.model.variables),), self.warmup.mean(axis=0)
)
[docs] def sample(self, n: int, fluxes: bool = True) -> pd.DataFrame:
"""Generate a set of samples.
This is the basic sampling function for all hit-and-run samplers.
Parameters
----------
n : int
The minimum number of samples that are generated at once.
fluxes : bool, optional
Whether to return fluxes or the internal solver variables. If
set to False, will return a variable for each forward and
backward flux as well as all additional variables you might
have defined in the model (default True).
Returns
-------
pandas.DataFrame
Returns a pandas DataFrame with `n` rows, each containing a
flux sample.
Notes
-----
Performance of this function linearly depends on the number
of reactions in your model and the thinning factor.
If the number of processes is larger than one, computation is split
across the CPU cores of your machine. This may shorten computation
time. However, there is also overhead in setting up parallel
computation primitives so, we recommend to calculate large numbers
of samples at once (`n` > 1000).
"""
if self.processes > 1:
n_process = np.ceil(n / self.processes).astype(int)
n = n_process * self.processes
# The cast to list is weird but not doing it gives recursion
# limit errors, something weird going on with multiprocessing
args = list(zip([n_process] * self.processes, range(self.processes)))
with ProcessPool(
self.processes, initializer=mp_init, initargs=(self,)
) as pool:
results = pool.map(_sample_chain, args, chunksize=1)
chains = np.vstack([r[1] for r in results])
self.retries += sum(r[0] for r in results)
else:
mp_init(self)
results = _sample_chain((n, 0))
chains = results[1]
# Update the global center
self.center = (self.n_samples * self.center + np.atleast_2d(chains).sum(0)) / (
self.n_samples + n
)
self.n_samples += n
if fluxes:
names = [r.id for r in self.model.reactions]
return pd.DataFrame(
chains[:, self.fwd_idx] - chains[:, self.rev_idx],
columns=names,
)
else:
names = [v.name for v in self.model.variables]
return pd.DataFrame(chains, columns=names)
# Models can be large so don't pass them around during multiprocessing
[docs] def __getstate__(self) -> Dict:
"""Return the object for serialization."""
d = dict(self.__dict__)
del d["model"]
return d
def mp_init(obj: OptGPSampler) -> None:
"""Initialize the multiprocessing pool."""
global sampler
sampler = obj
# Unfortunately this has to be outside the class to be usable with
# multiprocessing :()
def _sample_chain(args: Tuple[int, int]) -> Tuple[int, OptGPSampler]:
"""Sample a single chain for OptGPSampler.
`center` and `n_samples` are updated locally and forgotten afterwards.
"""
n, idx = args # has to be this way to work in Python 2.7
center = sampler.center
np.random.seed((sampler._seed + idx) % np.iinfo(np.int32).max)
pi = np.random.randint(sampler.n_warmup)
prev = sampler.warmup[pi, :]
prev = step(sampler, center, prev - center, 0.95)
n_samples = max(sampler.n_samples, 1)
samples = np.zeros((n, center.shape[0]))
for i in range(1, sampler.thinning * n + 1):
pi = np.random.randint(sampler.n_warmup)
delta = sampler.warmup[pi, :] - center
prev = step(sampler, prev, delta)
if sampler.problem.homogeneous and (
n_samples * sampler.thinning % sampler.nproj == 0
):
prev = sampler._reproject(prev)
center = sampler._reproject(center)
if i % sampler.thinning == 0:
samples[i // sampler.thinning - 1, :] = prev
center = (n_samples * center) / (n_samples + 1) + prev / (n_samples + 1)
n_samples += 1
return (sampler.retries, samples)
```