pm4py.algo.discovery.inductive package

Submodules

pm4py.algo.discovery.inductive.algorithm module

class pm4py.algo.discovery.inductive.algorithm.Variants(value)[source]

Bases: enum.Enum

An enumeration.

IM = <module 'pm4py.algo.discovery.inductive.variants.im.algorithm' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im\\algorithm.py'>
IM_CLEAN = <module 'pm4py.algo.discovery.inductive.variants.im_clean.algorithm' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im_clean\\algorithm.py'>
IMd = <module 'pm4py.algo.discovery.inductive.variants.im_d.dfg_based' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im_d\\dfg_based.py'>
IMf = <module 'pm4py.algo.discovery.inductive.variants.im_f.algorithm' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im_f\\algorithm.py'>
pm4py.algo.discovery.inductive.algorithm.apply(log, parameters=None, variant=<Variants.IM_CLEAN: <module 'pm4py.algo.discovery.inductive.variants.im_clean.algorithm' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im_clean\\algorithm.py'>>)[source]

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

Parameters
  • log – Log

  • variant – Variant of the algorithm to apply, possible values: - Variants.IMd

  • 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.algorithm.apply_dfg(dfg, parameters=None, variant=<Variants.IMd: <module 'pm4py.algo.discovery.inductive.variants.im_d.dfg_based' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im_d\\dfg_based.py'>>)[source]

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

Parameters
  • dfg – Directly-Follows graph

  • variant – Variant of the algorithm to apply, possible values: - Variants.IMd

  • 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.algorithm.apply_tree(log, parameters=None, variant=<Variants.IM_CLEAN: <module 'pm4py.algo.discovery.inductive.variants.im_clean.algorithm' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im_clean\\algorithm.py'>>)[source]

Apply the chosen IM algorithm to a log obtaining a process tree

Parameters
  • log – Log

  • variant – Variant of the algorithm to apply, possible values: - Variants.IMd

  • 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.algorithm.apply_tree_dfg(dfg, parameters=None, variant=<Variants.IMd: <module 'pm4py.algo.discovery.inductive.variants.im_d.dfg_based' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im_d\\dfg_based.py'>>)[source]

Apply the chosen IM algorithm to a DFG graph obtaining a process tree

Parameters
  • dfg – Directly-follows graph

  • variant – Variant of the algorithm to apply, possible values: - Variants.IMd

  • 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.algorithm.apply_tree_variants(variants, parameters=None, variant=<Variants.IM_CLEAN: <module 'pm4py.algo.discovery.inductive.variants.im_clean.algorithm' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im_clean\\algorithm.py'>>)[source]

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

Parameters
  • variants – Dictionary/list/set of variants in the log

  • variant – Variant of the algorithm to apply, possible values: - Variants.IMd

  • 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.algorithm.apply_variants(variants, parameters=None, variant=<Variants.IM_CLEAN: <module 'pm4py.algo.discovery.inductive.variants.im_clean.algorithm' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\inductive\\variants\\im_clean\\algorithm.py'>>)[source]

Apply the chosen IM 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

  • variant – Variant of the algorithm to apply, possible values: - Variants.IMd

  • 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.parameters module

class pm4py.algo.discovery.inductive.parameters.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'

Module contents