pm4py.streaming.algo.discovery.dfg.variants package#

PM4Py – A Process Mining Library for Python

Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

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

Submodules#

pm4py.streaming.algo.discovery.dfg.variants.frequency module#

PM4Py – A Process Mining Library for Python

Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

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

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

Bases: Enum

DICT_VARIANT = 'dict_variant'#
DICT_ID = 'dict_id'#
CASE_DICT_ID = 'case_dict_id'#
DFG_DICT_ID = 'dfg_dict_id'#
ACT_DICT_ID = 'act_dict_id'#
START_ACT_DICT_ID = 'start_act_dict_id'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
class pm4py.streaming.algo.discovery.dfg.variants.frequency.StreamingDfgDiscovery(parameters=None)[source]#

Bases: StreamingAlgorithm

build_dictionaries(parameters)[source]#

Builds the dictionaries that are needed by the discovery operation

Parameters#

parameters
Parameters:
  • Parameters.DICT_VARIANT: type of dictionary to use

  • Parameters.CASE_DICT_ID: identifier of the case dictionary (hosting the last activity of a case) (0)

  • Parameters.DFG_DICT_ID: identifier of the DFG dictionary (1)

  • Parameters.ACT_ID: identifier of the dictionary hosting the count of the activities (2)

  • Parameters.START_ACT_DICT_ID: identifier of the dictionary hosting the count of the start activities (3)

event_without_activity_or_case(event)[source]#

Print an error message when an event is without the activity or the case identifier

Parameters#

event

Event

encode_str(stru)[source]#

Encodes a string for storage in generic dictionaries

encode_tuple(tup)[source]#

Encodes a tuple for storage in generic dictionaries

pm4py.streaming.algo.discovery.dfg.variants.frequency.apply(parameters=None)[source]#

Creates a StreamingDFGDiscovery object

Parameters#

parameters

Parameters of the algorithm