pm4py.algo.analysis.marking_equation.variants package#

PM4Py – A Process Mining Library for Python

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

Submodules#

pm4py.algo.analysis.marking_equation.variants.classic module#

PM4Py – A Process Mining Library for Python

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

class pm4py.algo.analysis.marking_equation.variants.classic.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

CASE_ID_KEY = 'pm4py:param:case_id_key'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

COSTS = 'costs'#

INCIDENCE_MATRIX = 'incidence_matrix'#

A = 'A_matrix'#

FULL_BOOTSTRAP_REQUIRED = 'full_bootstrap_required'#

class pm4py.algo.analysis.marking_equation.variants.classic.MarkingEquationSolver(net: PetriNet, im: Marking, fm: Marking, parameters: Dict[Any, Any] | None = None)[source]#

Bases: object

get_components() → Tuple[Any, Any, Any, Any, Any][source]#

Retrieve the components (Numpy matrixes) of the problem

Returns#

c: objective function
Aub: Inequalities matrix
bub: Inequalities vector
Aeq: Equalities matrix
beq: Equalities vector

change_ini_vec(ini: Marking)[source]#

Changes the initial marking of the synchronous product net

Parameters#

ini: Initial marking

get_x_vector(sol_points: List[int]) → List[int][source]#

Returns the x vector of the solution

Parameters#

sol_points: Solution of the integer problem

Returns#

x: X vector

get_h(sol_points: List[int]) → int[source]#

Returns the value of the heuristics

Parameters#

sol_points: Solution of the integer problem

Returns#

h: Heuristics value

get_activated_transitions(sol_points: List[int]) → List[Transition][source]#

Gets the transitions of the synchronous product net that are non-zero in the solution of the marking equation

Parameters#

sol_points: Solution of the integer problem

Returns#

act_trans: Activated transitions

solve() → Tuple[int, List[int]][source]#

Solves the marking equation, returning the heuristics and the x vector

Returns#

h: Heuristics value
x: X vector

solve_given_components(c, Aub, bub, Aeq, beq)[source]#

Solves the linear problem given the components

Parameters#

c: Objective vector
Aub: Inequalities matrix
bub: Inequalities vector
Aeq: Equalities matrix
beq: Equalities vector

Returns#

h: Heuristics value
x: X vector

get_firing_sequence(x: List[int]) → Tuple[List[Transition], bool, int][source]#

Gets a firing sequence from the X vector

Parameters#

x: X vector

Returns#

firing_sequence: Firing sequence
reach_fm: Boolean value that is true whether the firing sequence reaches the final marking
explained_events: Number of explaned events by the firing sequence

pm4py.algo.analysis.marking_equation.variants.classic.build(net: PetriNet, im: Marking, fm: Marking, parameters: Dict[Any, Any] | None = None) → MarkingEquationSolver[source]#

Builds the marking equation out of a Petri net

Parameters#

net: Petri net
im: Initial marking
fm: Final marking
parameters: Parameters of the algorithm, including: - Parameters.CASE_ID_KEY => attribute to use as case identifier - Parameters.ACTIVITY_KEY => attribute to use as activity - Parameters.COSTS => (if provided) the cost function (otherwise the default cost function is applied) - Parameters.INCIDENCE_MATRIX => (if provided) the incidence matrix of the Petri net - Parameters.A => (if provided) the A numpy matrix of the incidence matrix - Parameters.FULL_BOOTSTRAP_REQUIRED => The preset/postset of places/transitions need to be inserted

pm4py.algo.analysis.marking_equation.variants.classic.get_h_value(solver: MarkingEquationSolver, parameters: Dict[Any, Any] | None = None) → int[source]#

Gets the heuristics value from the marking equation

Parameters#

solver: Marking equation solver (class in this file)
parameters: Possible parameters of the algorithm