'''
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 enum import Enum
from pm4py.util import exec_utils, xes_constants, constants
from typing import Optional, Dict, Any, Union, Tuple, List, Set
from pm4py.objects.log.obj import EventLog
import pandas as pd
[docs]class Outputs(Enum):
DFG = "dfg"
SEQUENCE = "sequence"
PARALLEL = "parallel"
START_ACTIVITIES = "start_activities"
END_ACTIVITIES = "end_activities"
ACTIVITIES = "activities"
SKIPPABLE = "skippable"
ACTIVITIES_ALWAYS_HAPPENING = "activities_always_happening"
MIN_TRACE_LENGTH = "min_trace_length"
TRACE = "trace"
[docs]class Parameters(Enum):
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
ENABLE_ACT_ALWAYS_EXECUTED = "enable_act_always_executed"
[docs]class ConfOutputs(Enum):
FOOTPRINTS = "footprints"
START_ACTIVITIES = "start_activities"
END_ACTIVITIES = "end_activities"
ACTIVITIES_ALWAYS_HAPPENING = "activities_always_happening"
MIN_LENGTH_FIT = "min_length_fit"
IS_FOOTPRINTS_FIT = "is_footprints_fit"
[docs]def apply(log_footprints: List[Dict[str, Any]], model_footprints: Dict[str, Any], parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> List[Dict[str, Any]]:
"""
Apply footprints conformance between a log footprints object
and a model footprints object
Parameters
-----------------
log_footprints
Footprints of the log (trace-by-trace)
model_footprints
Footprints of the model
parameters
Parameters of the algorithm
Returns
------------------
violations
List containing, for each trace, a dictionary containing the violations
"""
if parameters is None:
parameters = {}
if not type(log_footprints) is list:
raise Exception(
"it is possible to apply this variant only on trace-by-trace footprints, not overall log footprints!")
conf_traces = {}
enable_act_always_executed = exec_utils.get_param_value(Parameters.ENABLE_ACT_ALWAYS_EXECUTED, parameters, True)
model_configurations = model_footprints[Outputs.SEQUENCE.value].union(model_footprints[Outputs.PARALLEL.value])
ret = []
for tr in log_footprints:
trace = tr[Outputs.TRACE.value]
if trace in conf_traces:
ret.append(conf_traces[trace])
else:
trace_configurations = tr[Outputs.SEQUENCE.value].union(tr[Outputs.PARALLEL.value])
trace_violations = {}
trace_violations[ConfOutputs.FOOTPRINTS.value] = set(
x for x in trace_configurations if x not in model_configurations)
trace_violations[ConfOutputs.START_ACTIVITIES.value] = set(x for x in tr[Outputs.START_ACTIVITIES.value] if
x not in model_footprints[
Outputs.START_ACTIVITIES.value]) if Outputs.START_ACTIVITIES.value in model_footprints else set()
trace_violations[ConfOutputs.END_ACTIVITIES.value] = set(
x for x in tr[Outputs.END_ACTIVITIES.value] if x not in model_footprints[
Outputs.END_ACTIVITIES.value]) if Outputs.END_ACTIVITIES.value in model_footprints else set()
trace_violations[ConfOutputs.ACTIVITIES_ALWAYS_HAPPENING.value] = set(
x for x in model_footprints[Outputs.ACTIVITIES_ALWAYS_HAPPENING.value] if x not in tr[
Outputs.ACTIVITIES.value]) if Outputs.ACTIVITIES_ALWAYS_HAPPENING.value in model_footprints and enable_act_always_executed else set()
trace_violations[ConfOutputs.MIN_LENGTH_FIT.value] = tr[Outputs.MIN_TRACE_LENGTH.value] >= model_footprints[
Outputs.MIN_TRACE_LENGTH.value] if Outputs.MIN_TRACE_LENGTH.value in tr and Outputs.MIN_TRACE_LENGTH.value in model_footprints else True
trace_violations[ConfOutputs.IS_FOOTPRINTS_FIT.value] = len(
trace_violations[ConfOutputs.FOOTPRINTS.value]) == 0 and len(
trace_violations[ConfOutputs.START_ACTIVITIES.value]) == 0 and len(
trace_violations[ConfOutputs.END_ACTIVITIES.value]) == 0 and len(
trace_violations[ConfOutputs.ACTIVITIES_ALWAYS_HAPPENING.value]) == 0 and trace_violations[
ConfOutputs.MIN_LENGTH_FIT.value]
ret.append(trace_violations)
conf_traces[trace] = trace_violations
return ret
[docs]def get_diagnostics_dataframe(log: EventLog, conf_result: List[Dict[str, Any]], parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> pd.DataFrame:
"""
Gets the diagnostics dataframe from the log
and the results of footprints conformance checking
(trace-by-trace)
Parameters
--------------
log
Event log
conf_result
Conformance checking results (trace-by-trace)
Returns
--------------
diagn_dataframe
Diagnostics dataframe
"""
if parameters is None:
parameters = {}
case_id_key = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, xes_constants.DEFAULT_TRACEID_KEY)
import pandas as pd
diagn_stream = []
for index in range(len(log)):
case_id = log[index].attributes[case_id_key]
is_fit = conf_result[index][ConfOutputs.IS_FOOTPRINTS_FIT.value]
footprints_violations = len(conf_result[index][ConfOutputs.FOOTPRINTS.value])
start_activities_violations = len(conf_result[index][ConfOutputs.START_ACTIVITIES.value])
end_activities_violations = len(conf_result[index][ConfOutputs.END_ACTIVITIES.value])
act_always_happening_violations = len(conf_result[index][ConfOutputs.ACTIVITIES_ALWAYS_HAPPENING.value])
min_length_fit = conf_result[index][ConfOutputs.MIN_LENGTH_FIT.value]
diagn_stream.append({"case_id": case_id, "is_fit": is_fit, "footprints_violations": footprints_violations, "start_activities_violations": start_activities_violations,
"end_activities_violations": end_activities_violations, "act_always_happening_violations": act_always_happening_violations, "min_length_fit": min_length_fit})
return pd.DataFrame(diagn_stream)