'''
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 typing import Optional, Dict, Any, Union
import pandas as pd
from pm4py.algo.conformance.temporal_profile.variants import log, dataframe
from pm4py.objects.log.obj import EventLog
from pm4py.util import (
typing,
exec_utils,
constants,
xes_constants,
pandas_utils,
)
from pm4py.objects.conversion.log import converter as log_converter
from enum import Enum
[docs]
class Parameters(Enum):
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
[docs]
def apply(
elog: Union[EventLog, pd.DataFrame],
temporal_profile: typing.TemporalProfile,
parameters: Optional[Dict[Any, Any]] = None,
) -> typing.TemporalProfileConformanceResults:
"""
Checks the conformance of the log using the provided temporal profile.
Implements the approach described in:
Stertz, Florian, Jürgen Mangler, and Stefanie Rinderle-Ma. "Temporal Conformance Checking at Runtime based on Time-infused Process Models." arXiv preprint arXiv:2008.07262 (2020).
Parameters
---------------
elog
Event log
temporal_profile
Temporal profile
parameters
Parameters of the algorithm, including:
- Parameters.ACTIVITY_KEY => the attribute to use as activity
- Parameters.START_TIMESTAMP_KEY => the attribute to use as start timestamp
- Parameters.TIMESTAMP_KEY => the attribute to use as timestamp
- Parameters.ZETA => multiplier for the standard deviation
Returns
---------------
list_dev
A list containing, for each trace, all the deviations.
Each deviation is a tuple with four elements:
- 1) The source activity of the recorded deviation
- 2) The target activity of the recorded deviation
- 3) The time passed between the occurrence of the source activity and the target activity
- 4) The value of (time passed - mean)/std for this occurrence (zeta).
"""
if pandas_utils.check_is_pandas_dataframe(elog):
return dataframe.apply(elog, temporal_profile, parameters=parameters)
else:
elog = log_converter.apply(
elog,
variant=log_converter.Variants.TO_EVENT_LOG,
parameters=parameters,
)
return log.apply(elog, temporal_profile, parameters=parameters)
[docs]
def get_diagnostics_dataframe(
elog: Union[EventLog, pd.DataFrame],
conf_result: typing.TemporalProfileConformanceResults,
parameters: Optional[Dict[Any, Any]] = None,
) -> pd.DataFrame:
"""
Gets the diagnostics dataframe from a log and the results
of temporal profle-based conformance checking
Parameters
--------------
log
Event log
conf_result
Results of conformance checking
Returns
--------------
diagn_dataframe
Diagnostics dataframe
"""
if parameters is None:
parameters = {}
if pandas_utils.check_is_pandas_dataframe(elog):
case_id_key = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
)
cases = pandas_utils.format_unique(elog[case_id_key].unique())
else:
elog = log_converter.apply(
elog,
variant=log_converter.Variants.TO_EVENT_LOG,
parameters=parameters,
)
case_id_key = exec_utils.get_param_value(
Parameters.CASE_ID_KEY,
parameters,
xes_constants.DEFAULT_TRACEID_KEY,
)
cases = [x.attributes[case_id_key] for x in elog]
case = []
source_activities = []
target_activities = []
throughput = []
num_st_devs = []
for i in range(len(conf_result)):
for el in conf_result[i]:
case.append(cases[i])
source_activities.append(el[0])
target_activities.append(el[1])
throughput.append(el[2])
num_st_devs.append(el[3])
dataframe = pandas_utils.instantiate_dataframe(
{
"case": case,
"source_activity": source_activities,
"target_activity": target_activities,
"throughput": throughput,
"num_st_devs": num_st_devs,
}
)
return dataframe
