Source code for pm4py.algo.evaluation.simplicity.variants.extended_cyclomatic

'''
    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
'''

from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.objects.petri_net.utils import reachability_graph
from typing import Optional, Dict, Any
from pm4py.util import nx_utils




def apply(
    petri_net: PetriNet,
    im: Optional[Marking] = None,
    parameters: Optional[Dict[Any, Any]] = None,
) -> float:
    """
    Computes the extended cyclomatic metric as described in the paper:

    "Complexity Metrics for Workflow Nets"
    Lassen, Kristian Bisgaard, and Wil MP van der Aalst

    Parameters
    -------------
    petri_net
        Petri net

    Returns
    -------------
    ext_cyclomatic_metric
        Extended Cyclomatic metric
    """
    if parameters is None:
        parameters = {}

    if im is None:
        # if not provided, try to reconstruct the initial marking by taking the
        # places with empty preset
        im = Marking()
        for place in petri_net.places:
            if len(place.in_arcs) == 0:
                im[place] = 1

    reach_graph = reachability_graph.construct_reachability_graph(
        petri_net, im, use_trans_name=True
    )

    G = nx_utils.DiGraph()
    for n in reach_graph.states:
        G.add_node(n.name)

    for n in reach_graph.states:
        for n2 in n.outgoing:
            G.add_edge(n, n2)

    sg = list(nx_utils.strongly_connected_components(G))

    return len(G.edges) - len(G.nodes) + len(sg)