'''
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.algo.conformance.tokenreplay.variants import token_replay
from pm4py.algo.conformance.tokenreplay import algorithm as executor
from pm4py.objects import log as log_lib
from pm4py.algo.evaluation.precision import utils as precision_utils
from pm4py.statistics.start_activities.log.get import get_start_activities
from pm4py.objects.petri_net.utils.align_utils import (
get_visible_transitions_eventually_enabled_by_marking,
)
from pm4py.util import exec_utils
from enum import Enum
from pm4py.util import constants
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.objects.conversion.log import converter as log_converter
import pandas as pd
[docs]
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
TOKEN_REPLAY_VARIANT = "token_replay_variant"
CLEANING_TOKEN_FLOOD = "cleaning_token_flood"
SHOW_PROGRESS_BAR = "show_progress_bar"
MULTIPROCESSING = "multiprocessing"
CORES = "cores"
"""
Implementation of the approach described in paper
Muñoz-Gama, Jorge, and Josep Carmona. "A fresh look at precision in process conformance." International Conference
on Business Process Management. Springer, Berlin, Heidelberg, 2010.
for measuring precision.
For each prefix in the log, the reflected tasks are calculated (outgoing attributes from the prefix)
Then, a token replay is done on the prefix in order to get activated transitions
Escaping edges is the set difference between activated transitions and reflected tasks
Then, precision is calculated by the formula used in the paper
At the moment, the precision value is different from the one provided by the ProM plug-in,
although the implementation seems to follow the paper concept
"""
[docs]
def apply(
log: EventLog,
net: PetriNet,
marking: Marking,
final_marking: Marking,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
):
"""
Get ET Conformance precision
Parameters
----------
log
Trace log
net
Petri net
marking
Initial marking
final_marking
Final marking
parameters
Parameters of the algorithm, including:
Parameters.ACTIVITY_KEY -> Activity key
"""
if parameters is None:
parameters = {}
cleaning_token_flood = exec_utils.get_param_value(
Parameters.CLEANING_TOKEN_FLOOD, parameters, False
)
token_replay_variant = exec_utils.get_param_value(
Parameters.TOKEN_REPLAY_VARIANT,
parameters,
executor.Variants.TOKEN_REPLAY,
)
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, log_lib.util.xes.DEFAULT_NAME_KEY
)
case_id_key = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
)
show_progress_bar = exec_utils.get_param_value(
Parameters.SHOW_PROGRESS_BAR, parameters, constants.SHOW_PROGRESS_BAR
)
# default value for precision, when no activated transitions (not even by
# looking at the initial marking) are found
precision = 1.0
sum_ee = 0
sum_at = 0
parameters_tr = {
token_replay.Parameters.SHOW_PROGRESS_BAR: show_progress_bar,
token_replay.Parameters.CONSIDER_REMAINING_IN_FITNESS: False,
token_replay.Parameters.TRY_TO_REACH_FINAL_MARKING_THROUGH_HIDDEN: False,
token_replay.Parameters.STOP_IMMEDIATELY_UNFIT: True,
token_replay.Parameters.WALK_THROUGH_HIDDEN_TRANS: True,
token_replay.Parameters.CLEANING_TOKEN_FLOOD: cleaning_token_flood,
token_replay.Parameters.ACTIVITY_KEY: activity_key,
}
if type(log) is not pd.DataFrame:
log = log_converter.apply(
log,
variant=log_converter.Variants.TO_EVENT_LOG,
parameters=parameters,
)
prefixes, prefix_count = precision_utils.get_log_prefixes(
log, activity_key=activity_key, case_id_key=case_id_key
)
prefixes_keys = list(prefixes.keys())
fake_log = precision_utils.form_fake_log(
prefixes_keys, activity_key=activity_key
)
aligned_traces = executor.apply(
fake_log,
net,
marking,
final_marking,
variant=token_replay_variant,
parameters=parameters_tr,
)
# fix: also the empty prefix should be counted!
start_activities = set(get_start_activities(log, parameters=parameters))
trans_en_ini_marking = set(
[
x.label
for x in get_visible_transitions_eventually_enabled_by_marking(
net, marking
)
]
)
diff = trans_en_ini_marking.difference(start_activities)
if type(log) is EventLog:
sum_at += len(log) * len(trans_en_ini_marking)
sum_ee += len(log) * len(diff)
else:
sum_at += log[case_id_key].nunique() * len(trans_en_ini_marking)
sum_ee += log[case_id_key].nunique() * len(diff)
# end fix
for i in range(len(aligned_traces)):
if aligned_traces[i]["trace_is_fit"]:
log_transitions = set(prefixes[prefixes_keys[i]])
activated_transitions_labels = set(
[
x.label
for x in aligned_traces[i][
"enabled_transitions_in_marking"
]
if x.label is not None
]
)
sum_at += (
len(activated_transitions_labels)
* prefix_count[prefixes_keys[i]]
)
escaping_edges = activated_transitions_labels.difference(
log_transitions
)
sum_ee += len(escaping_edges) * prefix_count[prefixes_keys[i]]
if sum_at > 0:
precision = 1 - float(sum_ee) / float(sum_at)
return precision
