'''
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 collections import Counter
from math import sqrt
from pm4py import util as pmutil
from pm4py.algo.conformance.tokenreplay import algorithm as token_replay
from pm4py.evaluation.generalization.parameters import Parameters
from pm4py.util import exec_utils
[docs]def get_generalization(petri_net, aligned_traces):
"""
Gets the generalization from the Petri net and the list of activated transitions
during the replay
The approach has been suggested by the paper
Buijs, Joos CAM, Boudewijn F. van Dongen, and Wil MP van der Aalst. "Quality dimensions in process discovery:
The importance of fitness, precision, generalization and simplicity."
International Journal of Cooperative Information Systems 23.01 (2014): 1440001.
A token replay is applied and, for each transition, we can measure the number of occurrences
in the replay. The following formula is applied for generalization
\sum_{t \in transitions} (math.sqrt(1.0/(n_occ_replay(t)))
1 - ----------------------------------------------------------
# transitions
Parameters
-----------
petri_net
Petri net
aligned_traces
Result of the token-replay
Returns
-----------
generalization
Generalization measure
"""
trans_occ_map = Counter()
for trace in aligned_traces:
for trans in trace["activated_transitions"]:
trans_occ_map[trans] += 1
inv_sq_occ_sum = 0.0
for trans in trans_occ_map:
this_term = 1.0 / sqrt(trans_occ_map[trans])
inv_sq_occ_sum = inv_sq_occ_sum + this_term
for trans in petri_net.transitions:
if trans not in trans_occ_map:
inv_sq_occ_sum = inv_sq_occ_sum + 1
generalization = 1.0
if len(petri_net.transitions) > 0:
generalization = 1.0 - inv_sq_occ_sum / float(len(petri_net.transitions))
return generalization
[docs]def apply(log, petri_net, initial_marking, final_marking, parameters=None):
"""
Calculates generalization on the provided log and Petri net.
The approach has been suggested by the paper
Buijs, Joos CAM, Boudewijn F. van Dongen, and Wil MP van der Aalst. "Quality dimensions in process discovery:
The importance of fitness, precision, generalization and simplicity."
International Journal of Cooperative Information Systems 23.01 (2014): 1440001.
A token replay is applied and, for each transition, we can measure the number of occurrences
in the replay. The following formula is applied for generalization
\sum_{t \in transitions} (math.sqrt(1.0/(n_occ_replay(t)))
1 - ----------------------------------------------------------
# transitions
Parameters
-----------
log
Trace log
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
parameters
Algorithm parameters
Returns
-----------
generalization
Generalization measure
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, pmutil.xes_constants.DEFAULT_NAME_KEY)
parameters_tr = {Parameters.ACTIVITY_KEY: activity_key}
aligned_traces = token_replay.apply(log, petri_net, initial_marking, final_marking, parameters=parameters_tr)
return get_generalization(petri_net, aligned_traces)