import argparse
import six
import time
import pandas as pd
import numpy as np
from symengine import Add, sympify
from warnings import warn, catch_warnings, filterwarnings, resetwarnings
from cobra.exceptions import OptimizationError
from dexom_python.enum_functions.icut_functions import create_icut_constraint
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_maxdist_constraint(model, reaction_weights, prev_sol, obj_tol, name='maxdist_optimality', full=False):
"""
Creates the optimality constraint for the maxdist algorithm.
This constraint conserves the optimal objective value of the previous solution
"""
lower_opt = prev_sol.objective_value - prev_sol.objective_value * obj_tol
variables = []
weights = []
for rid, weight in reaction_weights.items():
if weight > 0:
variables.append(model.solver.variables['x_' + rid])
weights.append(weight)
elif weight < 0:
variables.append(sympify('1') - model.solver.variables['x_' + rid])
weights.append(abs(weight))
opt_const = model.solver.interface.Constraint(Add(*[x * w for x, w in zip(variables, weights)]), lb=lower_opt, name=name)
return opt_const
[docs]def create_maxdist_objective(model, reaction_weights, prev_sol, prev_sol_bin, only_ones=False, full=False):
"""
Create the new objective for the maxdist algorithm.
This objective is the minimization of similarity between the binary solution vectors
If only_ones is set to False, the similarity will only be calculated with overlapping ones
"""
expr = sympify('0')
if full:
for rxn in model.reactions:
rid = rxn.id
rid_loc = prev_sol.fluxes.index.get_loc(rid)
x = model.solver.variables['x_' + rid]
if prev_sol_bin[rid_loc] == 1:
expr += x
elif not only_ones:
expr += 1 - x
else:
for rid, weight in six.iteritems(reaction_weights):
rid_loc = prev_sol.fluxes.index.get_loc(rid)
if weight > 0:
x = model.solver.variables['x_' + rid]
if prev_sol_bin[rid_loc] == 1:
expr += x
elif not only_ones:
expr += 1 - x
elif weight < 0:
x_rl = sympify('1') - model.solver.variables['x_' + rid]
if prev_sol_bin[rid_loc] == 1:
expr += 1 - x_rl
elif not only_ones:
expr += x_rl
objective = model.solver.interface.Objective(expr, direction='min')
return objective
[docs]def maxdist(model, reaction_weights, prev_sol=None, eps=DEFAULT_VALUES['epsilon'], thr=DEFAULT_VALUES['threshold'],
obj_tol=DEFAULT_VALUES['obj_tol'], maxiter=DEFAULT_VALUES['maxiter'], icut=True, full=False, only_ones=False):
"""
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
icut: bool
if True, icut constraints are applied
full: bool
if True, carries out integer-cut on all reactions; if False, only on reactions with non-zero weights
only_ones: bool
if True, only the ones in the binary solution are used for distance calculation (as in dexom matlab)
Returns
-------
solution: EnumSolution object
"""
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
icut_constraints = []
all_solutions = [prev_sol]
prev_sol_bin = (np.abs(prev_sol.fluxes) >= thr-tol).values.astype(int)
all_binary = [prev_sol_bin]
# adding the optimality constraint: the new objective value must be equal to the previous objective value
opt_const = create_maxdist_constraint(model, reaction_weights, prev_sol, obj_tol,
name='maxdist_optimality', full=full)
model.solver.add(opt_const)
for idx in range(1, maxiter+1):
t0 = time.perf_counter()
if icut:
# adding the icut constraint to prevent the algorithm from finding the same solutions
const = create_icut_constraint(model, reaction_weights, thr, prev_sol, name='icut_'+str(idx), full=full)
model.solver.add(const)
icut_constraints.append(const)
# defining the objective: minimize the number of overlapping ones and zeros
objective = create_maxdist_objective(model, reaction_weights, prev_sol, prev_sol_bin, only_ones, full)
model.objective = objective
with catch_warnings():
filterwarnings('error')
try:
# with model:
prev_sol = model.optimize()
prev_sol_bin = (np.abs(prev_sol.fluxes) >= thr-tol).values.astype(int)
all_solutions.append(prev_sol)
all_binary.append(prev_sol_bin)
t1 = time.perf_counter()
print('time for iteration ' + str(idx) + ':', t1 - t0)
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])
model.solver.remove(opt_const)
solution = EnumSolution(all_solutions, all_binary, all_solutions[0].objective_value)
return solution
def _main():
"""
This function is called when you run this script from the commandline.
It performs the distance-maximization enumeration algorithm
Use --help to see commandline parameters
"""
description = 'Performs the distance-maximization enumeration algorithm'
parser = argparse.ArgumentParser(description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-m', '--model', default=argparse.SUPPRESS,
help='Metabolic model in sbml, matlab, or json format')
parser.add_argument('-r', '--reaction_weights', default=argparse.SUPPRESS,
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('--noicut', action='store_true', help='Use this flag to remove the icut constraint')
parser.add_argument('--full', action='store_true', help='Use this flag to assign non-zero weights to all reactions')
parser.add_argument('--onlyones', action='store_true', help='Use this flag for the old implementation of maxdist')
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 = False if args.noicut else True
maxdist_sol = maxdist(model=model, reaction_weights=reaction_weights, prev_sol=prev_sol, eps=args.epsilon,
thr=args.threshold, obj_tol=args.obj_tol, maxiter=args.maxiter, icut=icut, full=args.full,
only_ones=args.onlyones)
sol = pd.DataFrame(maxdist_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 maxdist_sol.solutions], axis=1).T.reset_index().drop('index', axis=1)
fluxes.to_csv(args.output + '_fluxes.csv')
return True
if __name__ == '__main__':
_main()