import argparse
import six
import time
import numpy as np
import pandas as pd
from symengine import sympify
from warnings import warn, catch_warnings, filterwarnings, resetwarnings
from cobra.exceptions import OptimizationError
from dexom_python.imat_functions import imat
from dexom_python.model_functions import load_reaction_weights, read_model, check_model_options, check_threshold_tolerance
from dexom_python.enum_functions.enumeration import EnumSolution, create_enum_variables, read_prev_sol
from dexom_python.default_parameter_values import DEFAULT_VALUES
[docs]def create_icut_constraint(model, reaction_weights, threshold, prev_sol, name, full=False):
"""
Creates an icut constraint on the previously found solution.
This solution is excluded from the solution space.
"""
tol = model.solver.configuration.tolerances.feasibility
if full:
prev_sol_binary = (np.abs(prev_sol.fluxes) >= threshold-tol).values.astype(int)
expr = sympify('1')
newbound = sum(prev_sol_binary)
cvector = [1 if x else -1 for x in prev_sol_binary]
for idx, rxn in enumerate(model.reactions):
expr += cvector[idx] * model.solver.variables['x_' + rxn.id]
else:
newbound = -1
var_vals = []
for rid, weight in six.iteritems(reaction_weights):
if weight != 0.:
x = model.solver.variables['x_' + rid]
if np.abs(prev_sol.fluxes[rid]) >= (threshold-tol):
var_vals.append(x)
newbound += 1
else:
var_vals.append(-x)
expr = sum(var_vals)
constraint = model.solver.interface.Constraint(expr, ub=newbound, name=name)
if expr.evalf() == 1:
print('No reactions were found in reaction_weights when attempting to create an icut constraint')
constraint = None
return constraint
[docs]def icut(model, reaction_weights, prev_sol=None, eps=DEFAULT_VALUES['epsilon'], thr=DEFAULT_VALUES['threshold'],
obj_tol=DEFAULT_VALUES['obj_tol'], maxiter=DEFAULT_VALUES['maxiter'], full=False):
"""
integer-cut method
Parameters
----------
model: cobrapy Model
reaction_weights: dict
keys = reactions and values = weights
prev_sol: imat Solution object
an imat solution used as a starting point
eps: float
activation threshold in imat
thr: float
detection threshold of activated reactions
obj_tol: float
variance allowed in the objective_values of the solutions
maxiter: foat
maximum number of solutions to check for
full: bool
if True, carries out integer-cut on all reactions; if False, only on reactions with non-zero weights
Returns
-------
solution: EnumSolution object
In the case of integer-cut, all_solutions and unique_solutions are identical
"""
check_threshold_tolerance(model=model, epsilon=eps, threshold=thr)
if prev_sol is None:
prev_sol = imat(model, reaction_weights, epsilon=eps, threshold=thr, full=full)
else:
model = create_enum_variables(model=model, reaction_weights=reaction_weights, eps=eps, thr=thr, full=full)
tol = model.solver.configuration.tolerances.feasibility
prev_sol_binary = (np.abs(prev_sol.fluxes) >= thr-tol).values.astype(int)
optimal_objective_value = prev_sol.objective_value - obj_tol*prev_sol.objective_value
all_solutions = [prev_sol]
all_solutions_binary = [prev_sol_binary]
icut_constraints = []
for idx in range(1, maxiter+1):
t0 = time.perf_counter()
const = create_icut_constraint(model, reaction_weights, thr, prev_sol, name='icut_'+str(idx), full=full)
model.solver.add(const)
icut_constraints.append(const)
with catch_warnings():
filterwarnings('error')
try:
prev_sol = imat(model, reaction_weights, epsilon=eps, threshold=thr, full=full)
t1 = time.perf_counter()
print('time for iteration ' + str(idx) + ':', t1 - t0)
if prev_sol.objective_value >= optimal_objective_value:
all_solutions.append(prev_sol)
prev_sol_binary = (np.abs(prev_sol.fluxes) >= thr - tol).values.astype(int)
all_solutions_binary.append(prev_sol_binary)
else:
break
except UserWarning as w:
resetwarnings()
prev_sol = all_solutions[-1]
if 'time_limit' in str(w):
print('The solver has reached the timelimit in iteration %i. If this happens frequently, there may '
'be too many constraints in the model. Alternatively, you can try modifying solver '
'parameters such as the feasibility tolerance or the MIP gap tolerance.' % idx)
warn('Solver status is "time_limit" in iteration %i' % idx)
elif 'infeasible' in str(w):
print('The solver has encountered an infeasible optimization in iteration %i. If this happens '
'frequently, there may be a problem with the starting solution. Alternatively, you can try '
'modifying solver parameters such as the feasibility tolerance or the MIP gap tolerance.'
% idx)
warn('Solver status is "infeasible" in iteration %i' % idx)
else:
print('An unexpected error has occured during the solver call in iteration %i.' % idx)
warn(w)
except OptimizationError as e:
resetwarnings()
prev_sol = all_solutions[-1]
print('An unexpected error has occured during the solver call in iteration %i.' % idx)
warn(str(e), UserWarning)
model.solver.remove([const for const in icut_constraints if const in model.solver.constraints])
solution = EnumSolution(all_solutions, all_solutions_binary, all_solutions[0].objective_value)
if full:
print('full icut iterations: ', idx)
else:
print('partial icut iterations: ', idx)
return solution
def _main():
"""
This function is called when you run this script from the commandline.
It performs the integer-cut enumeration algorithm
Use --help to see commandline parameters
"""
description = 'Performs the integer-cut enumeration algorithm'
parser = argparse.ArgumentParser(description=description, formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('-m', '--model', help='Metabolic model in sbml, matlab, or json format')
parser.add_argument('-r', '--reaction_weights', default=None,
help='Reaction weights in csv format (first row: reaction names, second row: weights)')
parser.add_argument('-p', '--prev_sol', default=[], help='starting solution or directory of recent solutions')
parser.add_argument('-e', '--epsilon', type=float, default=DEFAULT_VALUES['epsilon'],
help='Activation threshold for highly expressed reactions')
parser.add_argument('--threshold', type=float, default=DEFAULT_VALUES['threshold'],
help='Activation threshold for all reactions')
parser.add_argument('-t', '--timelimit', type=int, default=DEFAULT_VALUES['timelimit'], help='Solver time limit')
parser.add_argument('--tol', type=float, default=DEFAULT_VALUES['tolerance'], help='Solver feasibility tolerance')
parser.add_argument('--mipgap', type=float, default=DEFAULT_VALUES['mipgap'], help='Solver MIP gap tolerance')
parser.add_argument('--obj_tol', type=float, default=DEFAULT_VALUES['obj_tol'],
help='objective value tolerance, as a fraction of the original value')
parser.add_argument('-i', '--maxiter', type=int, default=DEFAULT_VALUES['maxiter'], help='Iteration limit')
parser.add_argument('-o', '--output', default='div_enum', help='Base name of output files, without format')
parser.add_argument('--full', action='store_true', help='Use this flag to assign non-zero weights to all reactions')
args = parser.parse_args()
model = read_model(args.model)
check_model_options(model, timelimit=args.timelimit, feasibility=args.tol, mipgaptol=args.mipgap)
reaction_weights = {}
if args.reaction_weights is not None:
reaction_weights = load_reaction_weights(args.reaction_weights)
prev_sol, _ = read_prev_sol(prev_sol_arg=args.prev_sol, model=model, rw=reaction_weights, eps=args.epsilon,
thr=args.threshold)
icut_sol = icut(model=model, reaction_weights=reaction_weights, prev_sol=prev_sol, eps=args.epsilon,
thr=args.threshold, obj_tol=args.obj_tol, maxiter=args.maxiter, full=args.full)
sol = pd.DataFrame(icut_sol.binary)
sol.columns = [r.id for r in model.reactions]
sol.to_csv(args.output+'_solutions.csv')
fluxes = pd.concat([s.fluxes for s in icut_sol.solutions], axis=1).T.reset_index().drop('index', axis=1)
fluxes.to_csv(args.output + '_fluxes.csv')
return True
if __name__ == '__main__':
_main()