Source code for pm4py.evaluation.precision.variants.etconformance_token

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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.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.evaluation.precision.parameters import Parameters
from pm4py.util import exec_utils

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, net, marking, final_marking, parameters=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) # 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.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 } prefixes, prefix_count = precision_utils.get_log_prefixes(log, activity_key=activity_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) sum_at += len(log) * len(trans_en_ini_marking) sum_ee += len(log) * 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