'''
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.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
from enum import Enum
[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
STRICT = "strict"
[docs]def apply_single(log_footprints: Dict[str, Any], model_footprints: Dict[str, Any], parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]:
"""
Apply footprints conformance between a log footprints object
and a model footprints object
Parameters
-----------------
log_footprints
Footprints of the log (NOT a list, but a single footprints object)
model_footprints
Footprints of the model
parameters
Parameters of the algorithm, including:
- Parameters.STRICT => strict check of the footprints
Returns
------------------
violations
Set of all the violations between the log footprints
and the model footprints
"""
if parameters is None:
parameters = {}
strict = exec_utils.get_param_value(Parameters.STRICT, parameters, False)
if strict:
s1 = log_footprints[Outputs.SEQUENCE.value].difference(model_footprints[Outputs.SEQUENCE.value])
s2 = log_footprints[Outputs.PARALLEL.value].difference(model_footprints[Outputs.PARALLEL.value])
violations = s1.union(s2)
else:
s1 = log_footprints[Outputs.SEQUENCE.value].union(log_footprints[Outputs.PARALLEL.value])
s2 = model_footprints[Outputs.SEQUENCE.value].union(model_footprints[Outputs.PARALLEL.value])
violations = s1.difference(s2)
return violations
[docs]def apply(log_footprints: Union[Dict[str, Any], List[Dict[str, Any]]], model_footprints: Dict[str, Any], parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Union[List[Dict[str, Any]], Dict[str, Any]]:
"""
Apply footprints conformance between a log footprints object
and a model footprints object
Parameters
-----------------
log_footprints
Footprints of the log
model_footprints
Footprints of the model
parameters
Parameters of the algorithm, including:
- Parameters.STRICT => strict check of the footprints
Returns
------------------
violations
Set of all the violations between the log footprints
and the model footprints, OR list of case-per-case violations
"""
if type(log_footprints) is list:
ret = []
for case_footprints in log_footprints:
ret.append(apply_single(case_footprints, model_footprints, parameters=parameters))
return ret
return apply_single(log_footprints, model_footprints, parameters=parameters)
[docs]def get_diagnostics_dataframe(log: EventLog, conf_result: Union[List[Dict[str, Any]], 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]
num_violations = len(conf_result[index])
is_fit = num_violations == 0
diagn_stream.append({"case_id": case_id, "num_violations": num_violations, "is_fit": is_fit})
return pd.DataFrame(diagn_stream)