pm4py.algo.discovery.inductive.variants.im_d package¶

Subpackages¶

Submodules¶

pm4py.algo.discovery.inductive.variants.im_d.dfg_based 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.inductive.variants.im_d.dfg_based.Parameters[source]¶

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'¶

CASE_ID_KEY = 'pm4py:param:case_id_key'¶

CONCURRENT_KEY = 'concurrent'¶

EMPTY_TRACE_KEY = 'empty_trace'¶

NOISE_THRESHOLD = 'noiseThreshold'¶

ONCE_PER_TRACE_KEY = 'once_per_trace'¶

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'¶

STRICT_TAU_LOOP_KEY = 'strict_tau_loop'¶

TAU_LOOP_KEY = 'tau_loop'¶

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'¶

pm4py.algo.discovery.inductive.variants.im_d.dfg_based.apply(log, parameters=None)[source]¶

Apply the IMDF algorithm to a log obtaining a Petri net along with an initial and final marking

Parameters:

log – Log
parameters –

Parameters of the algorithm, including:

Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name)

Returns:

net – Petri net
initial_marking – Initial marking
final_marking – Final marking

pm4py.algo.discovery.inductive.variants.im_d.dfg_based.apply_dfg(dfg, parameters=None, activities=None, contains_empty_traces=False, start_activities=None, end_activities=None)[source]¶

Apply the IMDF algorithm to a DFG graph obtaining a Petri net along with an initial and final marking

Parameters:

dfg – Directly-Follows graph
parameters –

Parameters of the algorithm, including:

Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name)
activities – Activities of the process (default None)
contains_empty_traces – Boolean value that is True if the event log from which the DFG has been extracted contains empty traces
start_activities – If provided, the start activities of the log
end_activities – If provided, the end activities of the log

Returns:

net – Petri net
initial_marking – Initial marking
final_marking – Final marking

pm4py.algo.discovery.inductive.variants.im_d.dfg_based.apply_tree(log, parameters=None)[source]¶

Apply the IMDF algorithm to a log obtaining a process tree

Parameters:	log – Log parameters – Parameters of the algorithm, including: Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name)
Returns:	Process tree
Return type:	tree

pm4py.algo.discovery.inductive.variants.im_d.dfg_based.apply_tree_dfg(dfg, parameters=None, activities=None, contains_empty_traces=False, start_activities=None, end_activities=None)[source]¶

Apply the IMDF algorithm to a DFG graph obtaining a process tree

Parameters:	dfg – Directly-follows graph parameters – Parameters of the algorithm, including: Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name) activities – Activities of the process (default None) contains_empty_traces – Boolean value that is True if the event log from which the DFG has been extracted contains empty traces start_activities – If provided, the start activities of the log end_activities – If provided, the end activities of the log
Returns:	Process tree
Return type:	tree

Deprecated since version 2.2.10: This will be removed in 3.0.0. use newer IM implementation (IM_CLEAN)

pm4py.algo.discovery.inductive.variants.im_d.dfg_based.apply_tree_variants(variants, parameters=None)[source]¶

Apply the IMDF algorithm to a dictionary/list/set of variants a log obtaining a process tree

Parameters:	variants – Dictionary/list/set of variants in the log parameters – Parameters of the algorithm, including: Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name)
Returns:	Process tree
Return type:	tree

pm4py.algo.discovery.inductive.variants.im_d.dfg_based.apply_variants(variants, parameters=None)[source]¶

Apply the IMDF algorithm to a dictionary/list/set of variants obtaining a Petri net along with an initial and final marking

Parameters:

variants – Dictionary/list/set of variants in the log
parameters –

Parameters of the algorithm, including:

Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name)

Returns:

net – Petri net
initial_marking – Initial marking
final_marking – Final marking

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/>.