pm4py.algo.discovery.inductive.variants.im package

Submodules

pm4py.algo.discovery.inductive.variants.im.algorithm module

class pm4py.algo.discovery.inductive.variants.im.algorithm.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
CASE_ID_KEY = 'case_id_glue'
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.algorithm.apply(log, parameters=None)[source]

Apply the IM 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.algorithm.apply_tree(log, parameters=None)[source]

Apply the IM 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

process_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.algorithm.apply_tree_variants(variants, parameters=None)[source]

Apply the IM algorithm to a dictionary of variants obtaining a process tree

Parameters
  • variants – Variants

  • 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

process_tree

pm4py.algo.discovery.inductive.variants.im.algorithm.apply_variants(variants, parameters=None)[source]

Apply the IM algorithm to a dictionary of variants, obtaining a Petri net along with an initial and final marking

Parameters
  • variants – Variants

  • 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