'''
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.algo.organizational_mining.roles.variants import pandas
from pm4py.algo.organizational_mining.roles.variants import log
from pm4py.util import exec_utils
from enum import Enum
import pkgutil
from typing import Optional, Dict, Any, Union, Tuple, List
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
[docs]class Variants(Enum):
LOG = log
PANDAS = pandas
[docs]def apply(log: Union[EventLog, EventStream, pd.DataFrame], variant=None, parameters: Optional[Dict[Any, Any]] = None) -> List[Any]:
"""
Gets the roles (group of different activities done by similar resources)
out of the log.
The roles detection is introduced by
Burattin, Andrea, Alessandro Sperduti, and Marco Veluscek. "Business models enhancement through discovery of roles." 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2013.
Parameters
-------------
log
Log object (also Pandas dataframe)
variant
Variant of the algorithm to apply. Possible values:
- Variants.LOG
- Variants.PANDAS
parameters
Possible parameters of the algorithm
Returns
------------
roles
List of different roles inside the log, including:
roles_threshold_parameter => threshold to use with the algorithm
"""
if parameters is None:
parameters = {}
if variant is None:
if pkgutil.find_loader("pandas"):
import pandas as pd
if type(log) is pd.DataFrame:
variant = Variants.PANDAS
if variant is None:
variant = Variants.LOG
return exec_utils.get_variant(variant).apply(log, parameters=parameters)