Source code for pm4py.algo.analysis.woflan.place_invariants.utility
'''
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 numpy as np
from copy import copy
from pm4py.util.lp import solver
from pm4py.util import constants
import warnings
import importlib.util
[docs]
def removearray(L, arr):
"""
Remove an array from a given list and return the list with the removed element.
:param L: list object
:param arr: array that has to be removed
:return: list object without array
"""
ind = 0
size = len(L)
while ind != size and not np.array_equal(L[ind], arr):
ind += 1
if ind != size:
L.pop(ind)
else:
raise ValueError("array not found in list.")
[docs]
def transform_basis(basis, style=None):
"""
We construct a (I)LP to transform our basis into a set of vectors by using linear combination to fit certain styles/
properties
:param basis: list of p-invariants. Commonly computed by the method 'compute_place_invariants' in
place_invariants.py
:param style: String that is used to construct certain constraints
At the moment, 'uniform' (all weights have value 0 or 1), and 'weighted' (all weights are >=0) are supported
:return: List of p-invariants that fits the style
"""
if style is None:
style = "weighted"
# First, we want to check if a vector of a basis only contains non-positve entries. If so, then we multiply the
# vector -1.
modified_base = []
for vector in basis:
all_non_positiv = True
for entry in vector:
if entry > 0:
all_non_positiv = False
if all_non_positiv:
modified_base.append(-1 * vector)
else:
modified_base.append(vector)
# For uniform variants, it is necessary that the weight for a place is either 0 or 1. We collect the variants for
# which this condition does not hold. We also collect the variants for the
# weighted invariants the entry is <0.
to_modify = []
for vector in modified_base:
for entry in vector:
if ((entry < 0 or entry > 1) and style == "uniform") or (
entry < 0 and style == "weighted"
):
to_modify.append(vector)
break
# if we have nothing to modify, we are done
if len(to_modify) > 0:
for vector in to_modify:
removearray(modified_base, vector)
set_B = range(0, len(modified_base))
# start of the problem
"""prob = pulp.LpProblem("linear_combination", pulp.LpMinimize)
X = pulp.LpVariable.dicts("x", set_B, cat='Integer')
y = pulp.LpVariable("y", cat='Integer', lowBound=1)
# add objective
prob += pulp.lpSum(X[i] for i in set_B)
if style=='uniform':
# variables for uniform. Therefore, the resulting weight can either be 0 or 1
z = pulp.LpVariable.dicts("z", range(0, len(vector)), lowBound=0, upBound=1, cat='Integer')
# add constraints
for i in range(len(vector)):
prob += pulp.lpSum(X[j]*modified_base[j][i] for j in range(len(modified_base)))+y*vector[i]== z[i]
elif style=='weighted':
for i in range(len(vector)):
prob += pulp.lpSum(X[j]*modified_base[j][i] for j in range(len(modified_base)))+y*vector[i] >= 0
prob.solve()"""
# problem is solved
c = [1] * len(set_B) + [0] * (len(vector) + 1)
zeros = [0] * (len(set_B) + len(vector) + 1)
Aub = []
bub = []
Aeq = []
beq = []
first_constraint = copy(zeros)
first_constraint[len(set_B)] = -1
Aub.append(first_constraint)
bub.append(-1)
for i in range(len(vector)):
this_row = copy(zeros)
this_row[len(set_B)] = list(vector[i])[0]
for j in range(len(modified_base)):
this_row[j] = list(modified_base[j][i])[0]
if style == "uniform":
this_row[len(set_B) + 1 + i] = -1
if style == "uniform":
Aeq.append(this_row)
beq.append(0)
elif style == "weighted":
Aub.append([-x for x in this_row])
bub.append(0)
for i in range(len(vector)):
last_constraint_1 = copy(zeros)
last_constraint_1[len(set_B) + 1 + i] = 1
Aub.append(last_constraint_1)
bub.append(1)
last_constraint_2 = copy(zeros)
last_constraint_2[len(set_B) + 1 + i] = -1
Aub.append(last_constraint_2)
bub.append(0)
Aeq = np.asmatrix(Aeq).astype(np.float64)
beq = np.asmatrix(beq).transpose().astype(np.float64)
Aub = np.asmatrix(Aub).astype(np.float64)
bub = np.asmatrix(bub).transpose().astype(np.float64)
if Aeq.shape[1] == 0:
Aeq = np.zeros((1, len(c))).astype(np.float64)
beq = np.zeros(1).transpose().astype(np.float64)
if Aub.shape[1] == 0:
Aub = np.zeros((1, len(c))).astype(np.float64)
bub = np.zeros(1).transpose().astype(np.float64)
# this is highly critical and LP solutions are not always correct
# :(
proposed_solver = solver.SCIPY
if importlib.util.find_spec("pulp"):
proposed_solver = solver.PULP
else:
if constants.SHOW_INTERNAL_WARNINGS:
warnings.warn(
"solution from scipy may be unstable. Please install PuLP (pip install pulp) for fully reliable results."
)
sol = solver.apply(
c,
Aub,
bub,
Aeq,
beq,
variant=proposed_solver,
parameters={"method": "revised simplex", "require_ilp": True},
)
points = solver.get_points_from_sol(sol, variant=proposed_solver)
val = solver.get_prim_obj_from_sol(sol, variant=proposed_solver)
if points is not None:
new_vector = np.zeros(len(vector))
if style == "weighted":
for i in range(len(new_vector)):
new_vector[i] = points[len(set_B)] * vector[i]
for j in range(len(modified_base)):
new_vector[i] = (
new_vector[i] + modified_base[j][i] * points[j]
)
elif style == "uniform":
for i in range(len(new_vector)):
new_vector[i] = points[len(set_B) + 1 + i]
new_vector = np.array([new_vector]).T
modified_base.append(new_vector)
return modified_base
[docs]
def compute_uncovered_places(invariants, net):
"""
Compute a list of uncovered places for invariants of a given Petri Net. Note that there exists a separate algorithm
for s-components
:param invariants: list of invariants. Each invariants is a numpy-Array representation
:param net: Petri Net object of PM4Py
:return: List of uncovered place over all invariants
"""
place_list = sorted(list(net.places), key=lambda x: x.name)
unncovered_list = place_list.copy()
for invariant in invariants:
for index, value in enumerate(invariant):
if value != 0:
if place_list[index] in unncovered_list:
unncovered_list.remove(place_list[index])
return unncovered_list