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(value)[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/>.