Source code for pm4py.algo.discovery.inductive.util.cut_detection

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py 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 General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
from pm4py.algo.discovery.inductive.util import detection_utils


[docs]def detect_sequential_cut(subtree, dfg, strongly_connected_components): """ Detect sequential cut in DFG graph Parameters -------------- dfg DFG strongly_connected_components Strongly connected components """ if subtree.contains_empty_trace(): return [False, [], []] if len(strongly_connected_components) > 1: conn_matrix = detection_utils.get_connection_matrix(strongly_connected_components, dfg) comps = [] closed = set() for i in range(conn_matrix.shape[0]): if max(conn_matrix[i, :]) == 0: if len(comps) == 0: comps.append([]) comps[-1].append(i) closed.add(i) cyc_continue = len(comps) >= 1 while cyc_continue: cyc_continue = False curr_comp = [] for i in range(conn_matrix.shape[0]): if i not in closed: i_j = set() for j in range(conn_matrix.shape[1]): if conn_matrix[i][j] == 1.0: i_j.add(j) i_j_minus = i_j.difference(closed) if len(i_j_minus) == 0: curr_comp.append(i) closed.add(i) if curr_comp: cyc_continue = True comps.append(curr_comp) last_cond = False for i in range(conn_matrix.shape[0]): if i not in closed: if not last_cond: last_cond = True comps.append([]) comps[-1].append(i) if len(comps) > 1: comps = [detection_utils.perform_list_union(list(set(strongly_connected_components[i]) for i in comp)) for comp in comps] # this part assures that the sequential cut follows completely the definition, i.e. there are no # subsequent components with no edges between them (except when the detected cut is binary). # # Remind: the tree returned by IM is folded at the end, this ensures that the cut is still maximal dfg = [x[0] for x in dfg] i = 0 while i < len(comps) - 1: outer_edges = [d for d in dfg if d[0] in comps[i] and d[1] not in comps[i] and d[1] not in comps[i + 1]] # a situation that is not managed by considering only the outer edges is the one # that sees a chain of invisibles at the end. In this case, it can be detected that the end activity # has no edge toward any of the following components. end_activities_wo_exedge = set( a for a in subtree.end_activities if a in comps[i] and a not in [x[0] for x in dfg]) # before merging the connected components, make sure that at the end there are two (or more) # connected components if (outer_edges or end_activities_wo_exedge) and len(comps) > 2: comps[i] = comps[i].union(comps[i + 1]) del comps[i + 1] continue i = i + 1 return [True, comps] return [False, [], []]