pm4py.algo.discovery.log_skeleton.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.algo.discovery.log_skeleton.variants.classic 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.algo.discovery.log_skeleton.variants.classic.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- NOISE_THRESHOLD = 'noise_threshold'#
- CONSIDERED_CONSTRAINTS = 'considered_constraints'#
- DEFAULT_CONSIDERED_CONSTRAINTS = ['equivalence', 'always_after', 'always_before', 'never_together', 'directly_follows', 'activ_freq']#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- PARAMETER_VARIANT_DELIMITER = 'variant_delimiter'#
- class pm4py.algo.discovery.log_skeleton.variants.classic.Outputs(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- EQUIVALENCE = 'equivalence'#
- ALWAYS_AFTER = 'always_after'#
- ALWAYS_BEFORE = 'always_before'#
- NEVER_TOGETHER = 'never_together'#
- DIRECTLY_FOLLOWS = 'directly_follows'#
- ACTIV_FREQ = 'activ_freq'#
- pm4py.algo.discovery.log_skeleton.variants.classic.equivalence(logs_traces, all_activs, noise_threshold=0)[source]#
Gets the equivalence relations given the traces of the log
Parameters#
- logs_traces
Traces of the log
- all_activs
All the activities
- noise_threshold
Noise threshold
Returns#
- rel
List of relations in the log
- pm4py.algo.discovery.log_skeleton.variants.classic.always_after(logs_traces, all_activs, noise_threshold=0)[source]#
Gets the always-after relations given the traces of the log
Parameters#
- logs_traces
Traces of the log
- all_activs
All the activities
- noise_threshold
Noise threshold
Returns#
- rel
List of relations in the log
- pm4py.algo.discovery.log_skeleton.variants.classic.always_before(logs_traces, all_activs, noise_threshold=0)[source]#
Gets the always-before relations given the traces of the log
Parameters#
- logs_traces
Traces of the log
- all_activs
All the activities
- noise_threshold
Noise threshold
Returns#
- rel
List of relations in the log
- pm4py.algo.discovery.log_skeleton.variants.classic.never_together(logs_traces, all_activs, len_log, noise_threshold=0)[source]#
Gets the never-together relations given the traces of the log
Parameters#
- logs_traces
Traces of the log
- all_activs
All the activities
- len_log
Length of the log
- noise_threshold
Noise threshold
Returns#
- rel
List of relations in the log
- pm4py.algo.discovery.log_skeleton.variants.classic.directly_follows(logs_traces, all_activs, noise_threshold=0)[source]#
Gets the allowed directly-follows relations given the traces of the log
Parameters#
- logs_traces
Traces of the log
- all_activs
All the activities
- noise_threshold
Noise threshold
Returns#
- rel
List of relations in the log
- pm4py.algo.discovery.log_skeleton.variants.classic.activ_freq(logs_traces, all_activs, len_log, noise_threshold=0)[source]#
Gets the allowed activities frequencies given the traces of the log
Parameters#
- logs_traces
Traces of the log
- all_activs
All the activities
- len_log
Length of the log
- noise_threshold
Noise threshold
Returns#
- rel
List of relations in the log
- pm4py.algo.discovery.log_skeleton.variants.classic.apply(log: EventLog | DataFrame, parameters: Dict[str | Parameters, Any] | None = None) Dict[str, Any][source]#
Discover a log skeleton from an event log
Parameters#
- log
Event log
- parameters
- Parameters of the algorithm, including:
the activity key (Parameters.ACTIVITY_KEY)
the noise threshold (Parameters.NOISE_THRESHOLD)
Returns#
- model
Log skeleton model