Source code for cobra.sampling.optgp

# -*- coding: utf-8 -*-

"""Provide OptGP sampler."""

from __future__ import absolute_import, division

from multiprocessing import Pool

import numpy as np
import pandas

from cobra.core.configuration import Configuration
from cobra.sampling.hr_sampler import HRSampler, shared_np_array, step

__all__ = ("OptGPSampler",)

CONFIGURATION = Configuration()

def mp_init(obj):
    """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):
    """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 =
    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)

[docs]class OptGPSampler(HRSampler): """A parallel optimized 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 (default Configuration.processes) The number of processes used during sampling. thinning : int, optional The thinning factor of the generated sampling chain. A thinning of 10 means samples are returned every 10 steps. 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. seed : int > 0, optional Sets the random number seed. Initialized to the current time stamp if None. Attributes ---------- model : cobra.Model The cobra model from which the samples get generated. thinning : int The currently used thinning factor. n_samples : int The total number of samples that have been generated by this sampler instance. problem : collections.namedtuple A python object whose attributes define the entire sampling problem in matrix form. See docstring of `Problem`. warmup : numpy.matrix A matrix of 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. seed : int > 0, optional Sets the random number seed. Initialized to the current time stamp if None. nproj : int How often to reproject the sampling point into the feasibility space. fwd_idx : numpy.array Has one entry for each reaction in the model containing the index of the respective forward variable. rev_idx : numpy.array Has 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 reactions)^2 due to the required nullspace matrices and warmup points. So large models easily take up a few GB 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. """ def __init__(self, model, processes=None, thinning=100, nproj=None, seed=None): """Initialize a new OptGPSampler.""" super(OptGPSampler, self).__init__(model, thinning, seed=seed) 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 = shared_np_array( (len(self.model.variables),), self.warmup.mean(axis=0) )
[docs] def sample(self, n, fluxes=True): """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 (see Notes). fluxes : boolean 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. Returns ------- numpy.matrix Returns a matrix 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 as the CPUs of your machine. This may shorten computation time. However, there is also overhead in setting up parallel computation 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))) # No with statement or starmap here since Python 2.x # does not support it :( mp = Pool(self.processes, initializer=mp_init, initargs=(self,)) results =, args, chunksize=1) mp.close() mp.join() 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.n_samples * + np.atleast_2d(chains).sum(0)) / ( self.n_samples + n ) self.n_samples += n if fluxes: names = [ for r in self.model.reactions] return pandas.DataFrame( chains[:, self.fwd_idx] - chains[:, self.rev_idx], columns=names, ) else: names = [ for v in self.model.variables] return pandas.DataFrame(chains, columns=names)
# Models can be large so don't pass them around during multiprocessing
[docs] def __getstate__(self): """Return the object for serialization.""" d = dict(self.__dict__) del d["model"] return d