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evaluation

Calculates evaluation measures for a Petri nets and an Event Log

Description

Calculates evaluation measures for a Petri nets and an Event Log

Usage

evaluation_all(
  eventlog,
  petrinet,
  initial_marking,
  final_marking,
  parameters = default_parameters(eventlog),
  convert = TRUE
)

evaluation_precision(
  eventlog,
  petrinet,
  initial_marking,
  final_marking,
  parameters = default_parameters(eventlog),
  variant = variant_precision_etconformance(),
  convert = TRUE
)

variant_precision_etconformance()

evaluation_fitness(
  eventlog,
  petrinet,
  initial_marking,
  final_marking,
  parameters = default_parameters(eventlog),
  variant = variant_fitness_token_based(),
  convert = TRUE
)

variant_fitness_token_based()

variant_fitness_alignment_based()

Arguments

eventlog

A bupaR or PM4PY event log.
petrinet

A bupaR or PM4PY Petri net.
initial_marking

A R vector with the place identifiers of the initial marking or a PM4PY marking. By default the initial marking of the bupaR Petri net will be used if available.
final_marking

A R vector with the place identifiers of the final marking or a PM4PY marking.
parameters

PM4PY alignment parameter. By default the activity_key from the bupaR event log is specified using param_activity_key.
convert

TRUE to automatically convert Python objects to their R equivalent. If you pass FALSE you can do manual conversion using the r-py-conversion function.
variant

The evaluation variant to be used.

Value

A list with all available evaluation measures.

Examples

if (pm4py_available()) {
  library(eventdataR)
  data(patients)

  # As Inductive Miner of PM4PY is not life-cycle aware, keep only `complete` events:
  patients_completes <- patients[patients$registration_type == "complete", ]

  # Discover a Petri net
  net <- discovery_inductive(patients_completes)

  # Calculate evaluation measures for event log and Petri net
  evaluation_all(patients_completes,
                 net$petrinet,
                 net$initial_marking,
                 net$final_marking)

}
pm4py

Interface to the 'PM4py' Process Mining Library

v1.2.7
GPL-3
Authors
Felix Mannhardt [aut, cre]
Initial release

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