pm4py.algo.discovery.dfg package

Submodules

pm4py.algo.discovery.dfg.algorithm module

class pm4py.algo.discovery.dfg.algorithm.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
CASE_ID_KEY = 'case_id_glue'
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
class pm4py.algo.discovery.dfg.algorithm.Variants(value)[source]

Bases: enum.Enum

An enumeration.

CASE_ATTRIBUTES = <module 'pm4py.algo.discovery.dfg.variants.case_attributes' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\case_attributes.py'>
FREQUENCY = <module 'pm4py.algo.discovery.dfg.variants.native' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\native.py'>
FREQUENCY_GREEDY = <module 'pm4py.algo.discovery.dfg.variants.native' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\native.py'>
FREQ_TRIPLES = <module 'pm4py.algo.discovery.dfg.variants.freq_triples' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\freq_triples.py'>
NATIVE = <module 'pm4py.algo.discovery.dfg.variants.native' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\native.py'>
PERFORMANCE = <module 'pm4py.algo.discovery.dfg.variants.performance' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\performance.py'>
PERFORMANCE_GREEDY = <module 'pm4py.algo.discovery.dfg.variants.performance' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\performance.py'>
pm4py.algo.discovery.dfg.algorithm.apply(log, parameters=None, variant=<Variants.NATIVE: <module 'pm4py.algo.discovery.dfg.variants.native' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\native.py'>>)[source]

Calculates DFG graph (frequency or performance) starting from a log

Parameters
  • log – Log

  • parameters

    Possible parameters passed to the algorithms:

    Parameters.AGGREGATION_MEASURE -> performance aggregation measure (min, max, mean, median) Parameters.ACTIVITY_KEY -> Attribute to use as activity Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp

  • variant

    Variant of the algorithm to use, possible values:
    • Variants.NATIVE

    • Variants.FREQUENCY

    • Variants.FREQUENCY_GREEDY

    • Variants.PERFORMANCE

    • Variants.PERFORMANCE_GREEDY

    • Variants.FREQ_TRIPLES

Returns

DFG graph

Return type

dfg

pm4py.algo.discovery.dfg.parameters module

class pm4py.algo.discovery.dfg.parameters.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
AGGREGATION_MEASURE = 'aggregationMeasure'
CASE_ID_KEY = 'case_id_glue'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
WINDOW = 'window'

pm4py.algo.discovery.dfg.replacement module

pm4py.algo.discovery.dfg.replacement.replace_values(dfg1, dfg2)[source]

Replace edge values specified in a DFG by values from a (potentially bigger) DFG

Parameters
  • dfg1 – First specified DFG (where values of edges should be replaces)

  • dfg2 – Second specified DFG (from which values should be taken)

Returns

First specified DFG with overrided values

Return type

dfg1

Module contents