Source code for pm4py.util.lp.variants.cvxopt_solver_custom_align_ilp
'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
import sys
from enum import Enum
from pm4py.util import exec_utils
from cvxopt import blas
from cvxopt import glpk
[docs]
class Parameters(Enum):
INTEGRALITY = "integrality"
this_options = {}
this_options["LPX_K_MSGLEV"] = 0
this_options["msg_lev"] = "GLP_MSG_OFF"
this_options["show_progress"] = False
this_options["presolve"] = "GLP_ON"
this_options_lp = {}
this_options_lp["LPX_K_MSGLEV"] = 0
this_options_lp["msg_lev"] = "GLP_MSG_OFF"
this_options_lp["show_progress"] = False
this_options_lp["presolve"] = "GLP_ON"
TOL = 10**(-5)
[docs]
def check_lp_sol_is_integer(x):
for i in range(len(x)):
if abs(x[i] - round(x[i])) > TOL:
return False
return True
[docs]
def custom_solve_ilp(c, G, h, A, b, I):
status, x, y, z = glpk.lp(c, G, h, A, b, options=this_options_lp)
if status == "optimal":
if not check_lp_sol_is_integer(x):
status, x = glpk.ilp(c, G, h, A, b, I=I, options=this_options)
if status == 'optimal':
pcost = blas.dot(c, x)
else:
pcost = None
return {'status': status, 'x': x, 'primal objective': pcost}
else:
return {'status': status, 'x': None, 'primal objective': None}
[docs]
def apply(c, Aub, bub, Aeq, beq, parameters=None):
"""
Gets the overall solution of the problem
Parameters
------------
c
c parameter of the algorithm
Aub
A_ub parameter of the algorithm
bub
b_ub parameter of the algorithm
Aeq
A_eq parameter of the algorithm
beq
b_eq parameter of the algorithm
parameters
Possible parameters of the algorithm
Returns
-------------
sol
Solution of the LP problem by the given algorithm
"""
if parameters is None:
parameters = {}
integrality = exec_utils.get_param_value(Parameters.INTEGRALITY, parameters, None)
if integrality is None:
size = Aub.size[1]
I = {i for i in range(size)}
else:
I = {i for i in range(len(integrality)) if integrality[i] == 1}
sol = custom_solve_ilp(c, Aub, bub, Aeq, beq, I)
return sol
[docs]
def get_prim_obj_from_sol(sol, parameters=None):
"""
Gets the primal objective from the solution of the LP problem
Parameters
-------------
sol
Solution of the ILP problem by the given algorithm
parameters
Possible parameters of the algorithm
Returns
-------------
prim_obj
Primal objective
"""
return sol["primal objective"]
[docs]
def get_points_from_sol(sol, parameters=None):
"""
Gets the points from the solution
Parameters
-------------
sol
Solution of the LP problem by the given algorithm
parameters
Possible parameters of the algorithm
Returns
-------------
points
Point of the solution
"""
if parameters is None:
parameters = {}
maximize = parameters["maximize"] if "maximize" in parameters else False
return_when_none = parameters["return_when_none"] if "return_when_none" in parameters else False
var_corr = parameters["var_corr"] if "var_corr" in parameters else {}
if sol and 'x' in sol and sol['x'] is not None:
return list(sol['x'])
else:
if return_when_none:
if maximize:
return [sys.float_info.max] * len(list(var_corr.keys()))
return [sys.float_info.min] * len(list(var_corr.keys()))
