'''
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/>.
'''
import pkgutil
from typing import Optional, Dict, Any, Union
import pandas as pd
from pm4py.algo.conformance.temporal_profile.variants import log
from pm4py.objects.log.obj import EventLog
from pm4py.util import typing
[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 pkgutil.find_loader("pandas"):
import pandas as pd
from pm4py.algo.conformance.temporal_profile.variants import dataframe
if type(elog) is pd.DataFrame:
return dataframe.apply(elog, temporal_profile, parameters=parameters)
return log.apply(elog, temporal_profile, parameters=parameters)