pm4py.algo.discovery.minimum_self_distance 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#

pm4py.algo.discovery.minimum_self_distance.variants package

Submodules#

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

Bases: Enum

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

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

pm4py.algo.discovery.minimum_self_distance.algorithm.apply(log_obj: EventLog | DataFrame | EventStream, variant: str | None = None, parameters: Dict[Any, Any] | None = None) → Dict[str, int][source]#

pm4py.algo.discovery.minimum_self_distance.utils 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.discovery.minimum_self_distance.utils.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#

pm4py.algo.discovery.minimum_self_distance.utils.derive_msd_witnesses(log: EventLog, msd: Dict[Any, int] | None = None, parameters: Dict[str | Parameters, Any] | None = None) → Dict[str, Set[str]][source]#

This function derives the minimum self distance witnesses. The self distance of a in <a> is infinity, of a in <a,a> is 0, in <a,b,a> is 1, etc. The minimum self distance is the minimal observed self distance value in the event log. A ‘witness’ is an activity that witnesses the minimum self distance. For example, if the minimum self distance of activity a in some log L is 2, then, if trace <a,b,c,a> is in log L, b and c are a witness of a.

Parameters#

log: Event Log to use
msd: Optional minimum self distance dictionary
parameters: Optional parameters dictionary

Returns#

Dictionary mapping each activity to a set of witnesses.