Source code for cobra.sampling.achr

"""Provide the ACHR sampler class."""

from typing import TYPE_CHECKING, Optional

import numpy as np
import pandas as pd

from .core import step
from .hr_sampler import HRSampler

    from cobra import Model

[docs]class ACHRSampler(HRSampler): """ Artificial Centering Hit-and-Run sampler. A sampler with low memory footprint and good convergence. Parameters ---------- model : cobra.Model The cobra model from which to generate samples. 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 ----- ACHR generates samples by choosing new directions from the sampling space's center and the warmup points. The implementation used here is the same as in the MATLAB COBRA Toolbox [2]_ and uses only the initial warmup points to generate new directions and not any other previous iterations. This usually gives better mixing, since the startup points are chosen to span the space in a wide manner. This also makes the generated sampling chain quasi-Markovian since the center converges rapidly. 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. References ---------- .. [1] Direction Choice for Accelerated Convergence in Hit-and-Run Sampling David E. Kaufman, Robert L. Smith Operations Research 199846:1 , 84-95 .. [2] """ def __init__( self, model: "Model", thinning: int = 100, nproj: Optional[int] = None, seed: Optional[int] = None, **kwargs ) -> None: """Initialize a new ACHRSampler.""" super().__init__(model, thinning, nproj=nproj, seed=seed, **kwargs) self.generate_fva_warmup() self.prev = = self.warmup.mean(axis=0) np.random.seed(self._seed)
[docs] def __single_iteration(self) -> None: """Run a single iteration of the sampling.""" pi = np.random.randint(self.n_warmup) # mix in the original warmup points to not get stuck delta = self.warmup[pi, :] - self.prev = step(self, self.prev, delta) if self.problem.homogeneous and ( self.n_samples * self.thinning % self.nproj == 0 ): self.prev = self._reproject(self.prev) = self._reproject( = (self.n_samples * / ( self.n_samples + 1 ) + self.prev / (self.n_samples + 1) self.n_samples += 1
[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 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. """ samples = np.zeros((n, self.warmup.shape[1])) for i in range(1, self.thinning * n + 1): self.__single_iteration() if i % self.thinning == 0: samples[i // self.thinning - 1, :] = self.prev if fluxes: names = [ for r in self.model.reactions] return pd.DataFrame( samples[:, self.fwd_idx] - samples[:, self.rev_idx], columns=names, ) else: names = [ for v in self.model.variables] return pd.DataFrame(samples, columns=names)