pm4py.algo.organizational_mining.roles package#

PM4Py – A Process Mining Library for Python

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

Subpackages#

Submodules#

pm4py.algo.organizational_mining.roles.algorithm module#

PM4Py – A Process Mining Library for Python

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

class pm4py.algo.organizational_mining.roles.algorithm.Variants(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

LOG = <module 'pm4py.algo.organizational_mining.roles.variants.log' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\organizational_mining\\roles\\variants\\log.py'>#

PANDAS = <module 'pm4py.algo.organizational_mining.roles.variants.pandas' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\organizational_mining\\roles\\variants\\pandas.py'>#

pm4py.algo.organizational_mining.roles.algorithm.apply(log: EventLog | EventStream | DataFrame, variant=None, parameters: Dict[Any, Any] | None = None) → List[Any][source]#

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