pm4py.algo.discovery.minimum_self_distance.variants package#

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/>.

Submodules#

pm4py.algo.discovery.minimum_self_distance.variants.log module#

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/>.

class pm4py.algo.discovery.minimum_self_distance.variants.log.Parameters(value)[source]#

Bases: Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'#
pm4py.algo.discovery.minimum_self_distance.variants.log.apply(log: Union[DataFrame, EventLog, EventStream], parameters: Optional[Dict[Union[str, Parameters], Any]] = None) Dict[str, int][source]#

This algorithm computes the minimum self-distance for each activity observed in an event log. 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. The activity key needs to be specified in the parameters input object (if None, default value ‘concept:name’ is used).

Parameters#

log

event log (either EventLog or EventStream)

parameters

parameters object;

Returns#

dict mapping an activity to its self-distance, if it exists, otherwise it is not part of the dict.

pm4py.algo.discovery.minimum_self_distance.variants.pandas module#

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/>.

class pm4py.algo.discovery.minimum_self_distance.variants.pandas.Parameters(value)[source]#

Bases: Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
pm4py.algo.discovery.minimum_self_distance.variants.pandas.apply(df, parameters=None)[source]#

This algorithm computes the minimum self-distance for each activity observed in an event log. 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. The activity key needs to be specified in the parameters input object (if None, default value ‘concept:name’ is used).

Parameters#

df

Pandas dataframe

parameters

parameters object;

Returns#

dict mapping an activity to its self-distance, if it exists, otherwise it is not part of the dict.