pm4py.algo.discovery.log_skeleton.variants package¶

Submodules¶

pm4py.algo.discovery.log_skeleton.variants.classic 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.log_skeleton.variants.classic.Outputs[source]¶

Bases: enum.Enum

An enumeration.

ACTIV_FREQ = 'activ_freq'¶

ALWAYS_AFTER = 'always_after'¶

ALWAYS_BEFORE = 'always_before'¶

DIRECTLY_FOLLOWS = 'directly_follows'¶

EQUIVALENCE = 'equivalence'¶

NEVER_TOGETHER = 'never_together'¶

class pm4py.algo.discovery.log_skeleton.variants.classic.Parameters[source]¶

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'¶

CASE_ID_KEY = 'pm4py:param:case_id_key'¶

CONSIDERED_CONSTRAINTS = 'considered_constraints'¶

DEFAULT_CONSIDERED_CONSTRAINTS = ['equivalence', 'always_after', 'always_before', 'never_together', 'directly_follows', 'activ_freq']¶

NOISE_THRESHOLD = 'noise_threshold'¶

PARAMETER_VARIANT_DELIMITER = 'variant_delimiter'¶

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:	List of relations in the log
Return type:	rel

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:	List of relations in the log
Return type:	rel

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:	List of relations in the log
Return type:	rel

pm4py.algo.discovery.log_skeleton.variants.classic.apply(log: Union[pm4py.objects.log.obj.EventLog, pm4py.objects.log.obj.EventStream, pandas.core.frame.DataFrame], parameters: Optional[Dict[Union[str, pm4py.algo.discovery.log_skeleton.variants.classic.Parameters], Any]] = 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:	Log skeleton model
Return type:	model

pm4py.algo.discovery.log_skeleton.variants.classic.apply_from_variants_list(var_list, parameters=None)[source]¶

Discovers the log skeleton from the variants list

Parameters:	var_list – Variants list parameters – Parameters
Returns:	Log skeleton model
Return type:	model

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:	List of relations in the log
Return type:	rel

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:	List of relations in the log
Return type:	rel

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:	List of relations in the log
Return type:	rel

pm4py.algo.discovery.log_skeleton.variants.classic.prepare_encode(log_skeleton)[source]¶

Prepares the log skeleton for encoding

Parameters:	log_skeleton – Log skeleton
Returns:	Log skeleton (with lists instead of sets)
Return type:	log_skeleton

Module contents¶

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/>.